Report and final recommendations: Police-reported Indigenous and racialized identity data through the Uniform Crime Reporting Survey

Canadian Centre for Justice and Community Safety Statistics
June 30, 2025

Table of contents

Executive summary

In recent years, a growing demand has emerged for more granular, or disaggregated, data to shed light on the diverse experiences of individuals across multiple social domains, including the justice system. Disaggregated data are crucial for understanding the experiences of marginalized populations, and for identifying and addressing systemic inequities, discrimination and racism within society. In the context of the criminal justice system, those with concerns about the different treatment and overrepresentation of Indigenous and racialized individuals have highlighted significant gaps in the availability of disaggregated data, particularly related to the identities of those who interact with law enforcement for various reasons, including criminal incidents. These gaps have restricted research on racial inequality and discrimination in policing and hindered evidence-based policy making to address the aforementioned issues within the criminal justice system.

In response to these increasing demands, in July 2020, Statistics Canada and the Canadian Association of Chiefs of Police (CACP) proposed a joint initiative to support the collection of data on the Indigenous and racialized identity of victims and accused persons through the Uniform Crime Reporting (UCR) Survey. The UCR Survey is an instrument through which police-reported data on criminal incidents are collected by Statistics Canada. These data are used for research and statistical purposes to monitor the nature and extent of police-reported crime in Canada.

As a part of the joint Statistics Canada–CACP initiative, Statistics Canada initiated a public engagement process to gather feedback from diverse stakeholders, including Indigenous and racialized organizations, police services, and academics. The goal was to assess the valued added in collecting data on the Indigenous and racialized identity of individuals involved in criminal incidents; explore appropriate methods for data collection, reporting and use; and identify related concerns. Based on feedback received through the engagement process, six draft recommendations were developed and publicly released in September 2022.

Beginning in the summer of 2022, a second phase of public engagement to support the implementation of data collection and reporting by police was initiated. The current article synthesizes and presents the findings of this second phase of engagement, highlighting key takeaways from discussions with Indigenous and racialized community organizations, police services, academics, and other parties. Status updates on the initiative and a discussion of next steps are also presented in this article.

The current report concludes with 12 final recommendations, based on the public engagement conducted under this initiative. These recommendations are for guiding the development and implementation of a national strategy for the police collection of Indigenous and racialized identity data through the UCR Survey.

Acknowledgments

Statistics Canada would like to thank all those who participated in the engagements it has held since 2020. This includes all participants who gave feedback throughout the initiative from Indigenous and racialized community organizations; academia; police services and representative organizations; government agencies at the federal, provincial, territorial and municipal levels; and the general public.

Background

In recent decades, there have been numerous calls for action to address the overrepresentation of and discrimination against Indigenous and racialized individuals in Canada's criminal justice system (Owusu-Bempah et al., 2023; Saghbini, Bressan & Paquin-Marseille, 2021; Clark, 2019; Department of Justice, 2017; Owusu-Bempah & Wortley, 2013; Cotter, 2022; Truth and Reconciliation Commission of Canada, 2015a;  Royal Commission on Aboriginal Peoples, 1996 ). These calls emphasize the need for disaggregated data from the justice system to uncover and address issues of systemic racism (Canadian Race Relations Foundation, 2020; Black Canadian National Survey, 2023; Clark, 2019; National Action Plan, 2021; Willick, 2021).

This information has also been called for by communities, organizations and academics as an essential component to measuring and understanding the extent to which individuals from different populations are represented in Canada's criminal justice system, beginning with their interactions with the police (National Inquiry into Missing and Murdered Indigenous Women and Girls, 2019 ; David & Mitchell, 2021; Millar & Owusu-Bempah, 2011; Owusu-Bempah & Jones, 2023; Owusu-Bempah & Jones, 2024; Samuels-Wortley, 2021; Statistics Canada, 2020; Truth and Reconciliation Commission of Canada, 2015).  

In response, in July 2020, Statistics Canada and the Canadian Association of Chiefs of Police (CACP) announced a joint commitment to collect data on the Indigenous and racialized identities of victims and accused persons Footnote 1 as they pertain to criminal incidents through the Uniform Crime Reporting (UCR) Survey Footnote 2. This police-reported Indigenous and racialized identity data (PIRID) initiative aims to provide insights on and further understanding of experiences faced by Indigenous and racialized individuals to address systemic issues of racism, discrimination and inequity within the Canadian criminal justice system.

Although this initiative focuses solely on police-reported criminal incidents collected through the UCR Survey, it will nonetheless help shed light on the experiences of Indigenous and racialized communities in relation to policing and the criminal justice system. Specifically, PIRID can be used for:

  • identifying differences and inequities in criminal justice system pathways and outcomes
  • providing quantitative indications to support evidence-based policies and programming
  • developing targets and benchmarks to monitor progress and assess the effectiveness of policies and programs and their impacts on specific populations
  • in combination with linked data from other sources, identifying the role of systemic issues in the inequitable experiences of Indigenous and racialized persons in the criminal justice system
  • providing communities with analytical findings to support programs and initiatives.

The Uniform Crime Reporting Survey

Statistics Canada's Uniform Crime Reporting (UCR) Survey is the official national statistical tool for collecting police-reported crime data and the main source used by government agencies, policy makers and researchers for information on crime trends in Canada. The UCR Survey data reflect crime that has come to the attention of Canadian police services. Since the UCR Survey was implemented in 1962, Statistics Canada has collected data on all criminal incidents that have been reported to federal, provincial, territorial and municipal police services in Canada involving offences under the Criminal Codeand other federal statutes.

The Incident-based UCR Survey covers 99.9% of the Canadian population and collects information on characteristics of criminal incidents and victims and accused persons involved. The UCR Survey is periodically revised to improve the quality of the information collected, to respond to changes in the definitions of different types of crime, and to better reflect emerging types of crime and information needs.

Police-reported Indigenous and racialized identity data engagement activities

Following the July 2020 announcement: Collection of data on Indigenous and ethnocultural groups in Canada's official police-reported crime statistics, Statistics Canada began working toward enabling the collection of Indigenous and racialized identity data by police through the UCR Survey. In July 2021, Statistics Canada embarked on an engagement process to seek feedback and diverse perspectives on this initiative, including from Indigenous and racialized community organizations; police services; academics; and other parties at the national, provincial, territorial and municipal levels. The purpose of this engagement was to assess the value-added in collecting this sensitive information, what information should be reported by police, how police should collect and report the information, and how the data should ultimately be used and accessed.

The engagement provided key insights on how to proceed with the initiative, leading to the development of six draft recommendations to guide the initiative forward. Statistics Canada published these draft recommendations and the results of the engagement in September 2022 in the Report and Draft Recommendations: Police-Reported Indigenous and Racialized Identity Statistics via the Uniform Crime Reporting Survey(hereafter referred to as "the September report"). This report outlined feedback and insights from participants, which led to the creation of the recommendations, as well as some reassurance and guiding principles for advancing this initiative effectively.

Following the development of the September report and draft recommendations, the need for further e ngagement became apparent, to inform specifics related to operationalizing data collection. As a result, in the summer of 2022, Statistics Canada embarked on a second phase of engagement, with the goal of seeking further feedback on the September report and draft recommendations, gauging concerns related to operationalizing data collection, and seeking further guidance to support the development of concrete data collection and analysis methods and plans.

Engagement to operationalize data collection by police: What we heard

Engagement strategy

From July 2022 to August 2023, Statistics Canada embarked on a second phase of engagement with Indigenous and racialized community organizations, police services, academics, and other parties of interest. This phase, the "operationalization phase," specifically aimed to

  • gauge operational needs and concerns
  • seek guidance on how best to collect these data, including when it would be most appropriate to collect them
  • take stock of legislative and regulatory considerations, including acts and directives, that may affect data collection
  • strategize on how data could be used after collection.

Generally, the engagement approach began with the development of discussion guides, allowing for an option to provide written feedback or participate in virtual engagements. Discussion guides included background information, a questionnaire to complete and space for respondents to provide any further information.

  • Three discussion guides were created to ask specific questions of
  • Two facilitation approaches, or a combination, were used to guide the meetings:
    • asking every question in the exact format detailed in the discussion guide
    • describing the initiative, gauging feedback on it, asking key questions from the discussion guide and then encouraging participants to expand on their feedback from the meeting by submitting a completed discussion guide.

Invitations to participate in Phase 2 of the engagement included outreach to more than 780 contacts. In this effort, all organizations and partners originally identified for Phase 1 of the engagement were included. Additionally, efforts were made to increase outreach by connecting with organizations with which Statistics Canada already had an established relationship, conducting web searches to identify organizations serving or representing Indigenous and racialized communities, and asking key partners to provide contact information for individuals and organizations they believed should be included in the engagement. Moreover, extending the length of the engagement period in Phase 2 led to improved outcomes, compared with the first phase of engagement. O utreach included multiple invitations, reminders and phone calls to encourage participation.

Overall, 132 respondents (individuals or organizations) participated in the Phase 2 engagement, an increase of 39 respondents from the previous phase. Community organizations (including Indigenous organizations and representative bodies) made up 48% of the respondents, followed by police services (42%) (Chart 1).

Chart 1
Chart 1: Sector Breakdown of Respondents, Phase 2 - description
  • Police representative bodies and committees - 4%
  • Police associations - 2%
  • Police services - 42%
  • Community organizations - 35%
  • Indigenous representative bodies and organizations - 13%
  • Academics - 4%

Engaging police services and representative bodies

The engagement with police services and other police representative bodies occurred from July 2022 to September 2023. During this time, more than 125 police services were invited to participate. These comprised all police services reporting to the Incident-based UCR Survey, including 15 First Nations police services. Engagement with police services in Quebec was facilitated through that province's ministère de la Sécurité publique.

Engaging police bodies also involved reaching out to other police representative bodies, including police associations at the national, provincial and municipal levels, and selected committees under the CACP. Selected police services were consulted to review the Phase 2 engagement guide for police associations. In all, Statistics Canada's outreach yielded feedback from

  • 55 police services from across the regions (Table 1)
  • eight police representative bodies and committees (including police associations and committees)Footnote 3
  • three Indigenous police services or representative bodies.
Table 1: Regional breakdown of respondent police services, including provincial police services
Region Number of respondents
National 1
Atlantic 8
Quebec 7
Ontario 21
Alberta 4
Manitoba 3
Saskatchewan 3
British Columbia 8
Total 55

Engaging community organizations

Engagement with community organizations began in May 2023, with invitations to 435 non-Indigenous organizations that represent or serve racialized or marginalized communities. This phase saw a significant increase in participation, with 47 organizations providing feedback, up from 18 in Phase 1, and with representation spreading across regions.

In addition to the engagement of non-Indigenous community organizations mentioned above, engagement with Indigenous organizations also began in May 2023, with outreach to more than 130 contacts from First Nations, Métis and Inuit communities, including national, regional, rural and urban organizations. In Phase 2, participation increased to 17 organizations, compared with Phase 1, when seven out of 83 contacted Indigenous organizations provided feedback.

In all, 63 Indigenous and racialized community organizations participated in the second phase of engagement.

Table 2: Regional breakdown of community organizations in Phase 2
Region Number of respondents
National 16
Atlantic 4
Quebec 2
Ontario 18
Alberta 12
Manitoba 2
Saskatchewan 4
British Columbia 2
Territories 3
Total 63

Engaging academics

Academics consulted in the second phase of engagement were identified during Phase 1. Of the 10 academics who were contacted, five participated in the second phase (other academics were also consulted through the engagement with police services, police representative bodies and committees, and feedback from these academics is included within the police services and representative bodies feedback).

Academics were also consulted in Phase 2 as part of an expert advisory panel for reviewing community engagement guides.

Findings from engagement with police services and police representative bodies

Discussions with police services and police representative bodies aimed to gauge concerns and perspectives related to the collection of Indigenous and racialized identity data through self-identification and officer perception, exploring current police agencies' policies, data usage concerns and system requirements. They considered how to develop guidelines for data collection, collection timing, training needs and community engagement recommendations. The discussions also touched on the role of Statistics Canada, other operational considerations, and the ideal data collection process to ensure success and sensitivity. The full discussion guides used for these engagements can be found in Appendix B and Appendix C.

Data collection and data standards

Respondents were asked to identify any concerns regarding the collection of Indigenous and racialized identity data, particularly relating to data standards. The following points were the most widespread concerns raised by police:

  • Data collection may have negative impacts on the relationships between police services and communities. Specifically, there were concerns that, because of the sensitivity of the data being collected, police officers may be accused of racial profiling.
  • Individuals may hesitate to provide accurate identity information for various reasons, such as fears of discriminatory treatment.
  • Too many identity categories within records management systems may cause misidentification and data quality issues.
  • There may be difficulties collecting the data in incidents that are sensitive or when emotions are heightened.
  • Reassurance that collected data will not be used in criminal investigations is necessary.
  • Proper training on police sensitivity when asking these questions is needed.

Police respondents were also asked to identify the circumstances in which police officers should refrain from collecting these data:

  • in volatile or active situations—according to the responding officer's assessment
  • in situations with a potential for escalation because of self-identification data being requested
  • in situations when an individual is experiencing mental health issues, they are intoxicated or there is a language barrier between the individual and the officer, and they are therefore incapable of providing and/or gathering the information
  • in situations when the person involved experienced a traumatic event or when asking them to self-identify could lead to additional trauma.

Some respondents raised concerns over the appropriateness of collecting this information when incidents involve youth.

Furthermore, based on engagement feedback from Phase 1, Statistics Canada has recommended that Indigenous and racialized identity information be collected using both the officer perception method and the self-identification method (Recommendation 1). Respondents were asked for their thoughts on this recommendation, and below are the main areas of concern raised by police.

Officer perception data collection

  • There were concerns related to the potential inaccuracy of officer perception data, compared with how accused persons and victims self-identify, and this may affect police relationships with communities.
  • A possible challenge is that no data may be reported if the police is unable to determine identity based on very specific population groups.
  • Concerns were raised over the specific racialized categories police will list during the collection of Indigenous and racialized identity data, with some police services explaining that some communities may not want to be generalized into certain racialized categories.

Self-identification data collection

  • It would be best to give police officers the discretion to decide when self-identification information should be requested.
  • The ideal time to collect is during booking (for accused individuals) and during the victim's statement (for victims).
  • Collection should take place after the initial interaction, such as during the booking process or when emotions are lowered.
  • Data should not be collected during arrest; some advised that the information should be collected after the initial investigation process has concluded.

Legislative and regulatory considerations

Police respondents were asked whether there are any current policies within their service, municipality, province or territory that would impact or prevent the collection of Indigenous or racialized identity data. Further, respondents were asked whether their police services currently collect any data (self-identification or officer perception) on Indigenous and racialized identity and, if so, what type of data they currently collect.

  • The majority of respondents indicated that they did not have policies in place that explicitly prevent the collection of Indigenous or racialized identity data.
  • Most respondents also highlighted that they do collect data on Indigenous and racialized identity, but that there are some limitations to these data collection practices:
    • These data are not being collected in a systematic or consistent manner.
    • There is no differentiation between officer perception and self-identification.
    • The data are not consistently collected in all types of incidents (e.g., typically during traffic stops). However, there is usually no way to distinguish how or when the data were collected.

Police service respondents also expressed several other regulatory concerns related to the data collection. These included concerns related to

  • geographical comparisons, as some provinces have legislation that governs data collection, while many do not
  • the impact of former "carding" legislation on relationships between police services and communities Footnote 4
  • the use of the data within police services as performance measures
  • how data collection could be enforced within jurisdictions, and the specific mandates that would govern this data collection
  • information privacy and the need for establishing rules around data access, sharing, release and oversight.

Data analysis and dissemination

Police services were asked whether they had any input regarding the use of the data after they have been collected (e.g., analysis and dissemination). Broad support for this initiative was accompanied by cautions and recommendations regarding data analysis and the dissemination (or public release) of analytical results. These are highlighted below.

Data analysis

There was a general caution that analysis has the potential to reinforce stereotypes about Indigenous and racialized persons, further leading to mistrust of police-collected data. Several police respondents raised awareness about the potential misuse and misinterpretation of collected data. Respondents stated that these data alone should not and cannot be used to measure discrimination and racism without appropriate context related to Indigenous and racialized communities' experiences, as well as about the work of the police in those communities.

Further, respondents expressed some concerns related to discrepancies between officer perception and self-identification data. Officers raised concerns that by misidentifying individuals through officer perception, officers could potentially be blamed for poorer data quality overall. Concerns were also shared related to the officer perception data collection method and the potential for bias. Respondents indicated that this is especially true in situations where the identity information has already been predetermined prior to the police response at the scene (for example, an individual may call dispatch and describe someone's racialized identity over the phone).

Data usage and dissemination

When asked about concerns related to the usage and dissemination of the data collected through this initiative, police respondents expressed a need for contextualizing the data and putting parameters in place to pre-empt data misuse. To help prevent such misuse, respondents emphasized that the collected data should be presented as transparently and completely as possible.

Police respondents also raised questions about the challenges in determining which data type takes precedence (officer perception or self-identification) and how the data will be published and accessed. Additionally, there were cautions about skewed statistics leading to incorrect conclusions, if only specific interactions are recorded, and challenges in presenting and analyzing the data. Without context, the representation of policing within certain communities may be inflated (for example, when there are repeat encounters with the same individual).

Overall, with the collection of such sensitive information, there were strong suggestions by police respondents for best practices and guidelines to support informed and responsible usage of the collected data before dissemination.

Education and awareness

Education and awareness were central topics in the engagement with police services. Police respondents were asked whether they had any concerns related to training officers on how to collect the data and suggestions on how the training should be conducted. The following summarizes the feedback and recommendations received from respondents, categorized into several subtopics.

Transparency and communication

An overwhelming majority of the police respondents indicated that communicating the "why" of the data collection is a significant factor for gaining both community and police personnel support for the collection of data.

  • Communities and police officers need to know the rationale behind the data collection, how the data will be used, and the purpose and value of collecting these data. This will more likely lead to better interactions with community members and provide reassurance to communities and individuals fearing that the data will be used to reinforce stereotypes.
    • If the "why" of the data collection is not clear, it may potentially create difficult interactions between officers and community groups.
    • Several respondents suggested that local police associations could reach out through their annual meetings to achieve buy-in from front-line officers.
    • It was suggested by many respondents that a comprehensive public information campaign is needed, with clear messaging and marketing efforts to convey the goals, benefits and importance of data collection to different communities.
    • Several respondents emphasized the importance of engaging with racialized and Indigenous advisory committees, using social media platforms, and providing information in multiple languages to rebuild trust.
  • Police services also suggested that community groups should be informed of individuals' right to refuse to provide information about their identity during self-identification data collection.

Officer training

Police respondents were asked for any suggestions on types of training and why the training is important. The most common feedback from police is highlighted here:

  • Training should provide information on when during an interaction data should be collected, what data should be collected and how they should be collected. Training should also ensure that officers understand the category definitions being used and how to apply them (officer perception).
  • There was an emphasis on the importance of clarifying the purpose and benefit of the data collection to police officers to remove potential hesitancy to collect the information.
  • Training should be standardized across the country, and communities should be informed about what officers are being trained on.
  • Training should include a focus on diversity and understanding cultural differences, providing officers with the necessary skills to navigate diverse interactions and situations.
    • Training should be developed and delivered in collaboration with community groups.
  • There was an emphasis on the need to reassure officers that data or the mistaken identification of an individual's Indigenous or racialized identity (officer perception) would not lead to reprimands or impact performance measure indicators.
  • Concerns were raised related to the amount of time and resources required to deliver training to officers.
    • Smaller-scale and remote police services may be unable to provide in-person training sessions to their staff.
    • There could be time and schedule constraints for overburdened staff.
  • Awareness and sensitivity must be an integral part of training, ensuring that experts are consulted to form the communication strategy and the training that is required for officers.
  • Additional human rights, anti-sexism and anti-racism training was emphasized as necessary, along with additional awareness of trauma-informed approaches.
  • There was support for information being presented by Statistics Canada to the boards, and this information will then trickle down to police services in a top-down approach.

Methods of training

Respondents were asked about what methods are recommended regarding the sensitive collection of these data. Various suggestions were made by police respondents, including the following:

  • Training should be offered in person and online, through platforms such as the Canadian Police Knowledge Network, which allows for progress tracking and flexibility in completing the training across all police services.
  • In-person training should be incorporated for topics that require human explanation and interaction.
  • A hybrid approach should be used, with a combination of online and in-person training.
  • Training should be scenario-based, emphasizing the use of discretion and judgment in the data collection, based on the situation, environment and individual history.
  • A standardized script for officers to address questions from community members should be included in officer notebooks and training.
  • Training should be in person, but to expedite the process, electronic training may be required.
  • If training is held in person, there are opportunities to ask questions and test out the data collection cycle, receive feedback, and troubleshoot in real time.

Awareness

Respondents emphasized the need for a comprehensive public education campaign to raise awareness of the initiative. When asked about proposed methods for raising awareness of this initiative, police respondents made the following recommendations:

  • Use modern communication tools, broadcast short and informative advertisements on TV and the radio, and use various forms of social media to raise awareness of the data collection initiative and reach a wider and more diverse audience.
  • Collaborate with stakeholder organizations and community agencies to develop accessible communication materials in different formats and languages to deliver the message to communities.
  • Spread information related to the data collection through provincial and municipal governments.

Community engagement

Police respondents highlighted the importance of community engagement and provided feedback related to raising awareness of the purpose of the data collection and associated concerns. The main input and considerations included the following:

  • The "why" of data collection needs to be clearly communicated. Communities and police officers seek understanding, which requires transparent messaging.
  • Collaboration with Indigenous and racialized community groups and advisory committees, along with a massive public information campaign, can help rebuild trust.
  • There is fear of potential misuse and misinterpretation of data and a need for transparent dissemination of information.
  • Community engagement should be extended to the training realm; respondents advocated for collaboration with community groups in developing and delivering training, where possible.

Findings from engagement with community organizations

The engagement with community organizations focused on feedback related to the initiative as a whole and on the September report. Key aspects of the discussions with the organizations were how to ensure successful collection and use of the data and manage and report self-identification and officer perception data. Questions were also asked about training needs for police regarding data collection and the types of data analyses to conduct once data are collected. The engagement guide used to facilitate some of the discussions with community organizations can be found in Appendix A. The following was the most prevalent feedback from community organizations.

Data collection and data standards

Most representatives of community organizations agreed that both methods, officer perception and self-identification, are essential and should be implemented. This would enable the most accurate collection of data, allowing for the best quality. The following summarizes the feedback and recommendations received from respondents, categorized into several subtopics.

Officer perception data collection

Community organizations were asked for their views on this method of collection. R espondents stated that officer perception involves assumptions based on physical characteristics and may not be correct and, as such, could cause issues with data quality and accuracy. There were also concerns about police bias impacting identification. Some respondents expressed concern that initially using the officer perception method of collecting these data could be seen as a form of "carding." Footnote 4

To avoid negative interactions or the retraumatizing of communities, respondents indicated that a thorough understanding of how practices such as street checks have disproportionately harmed racialized communities in old and recent history should be provided in police training. Training should also include cultural sensitivity and give an understanding of the Indigenous history of the justice system in Canada to help ensure these data are collected with respect. In tandem, respondents emphasized the importance of collecting self-identification data whenever possible.

Self-identification data collection

When asked about the collection of self-identification data, community organizations gave the following prevalent input:

  • Individuals may choose not to self-identify out of mistrust and fear of discrimination and any impact on the case.
  • There were concerns that self-identification could lead to discriminatory police interactions with Indigenous and racialized individuals, causing harm.
  • For police services to ask self-identification questions, they need to have cultural sensitivity and data collection training, and to understand power dynamics.

Moreover, many community organizations shared thoughts on how to proceed with operationalizing the initiative, and much of the feedback was related to raising awareness within communities. The suggestions are listed below:

  • It is important to clearly communicate the reason why these data are being collected.
  • An education campaign should be proactively implemented for the initiative, with topics such as informing individuals of their right to consent or refuse to self-identify. This could be done through infographics shared on social media, hashtags, community groups, information cards, brochures, flyers, etc.

Furthermore, respondents also shared feedback on how information should be communicated about the project during interactions when data are to be collected:

  • Communication should be in plain language, avoiding technical jargon, and in individuals' own language to ensure full comprehension (for example, by offering a multilingual sheet with a definition and examples of consent).
  • Different ways should be used to communicate and ensure communication is accessible. The explanation and consent should be in different formats, including verbal, electronic and written on paper.

Other feedback related to the collection of self-identification data included the following:

  • It is important to put safeguards in place to protect data from potential misuse.
  • The process of determining how to ask self-identification questions with sensitivity, and how these questions are asked, should be co-developed with Indigenous and racialized communities.
  • It was suggested to consult community groups by hosting townhalls and information sessions, to engage members and create discussion.
  • Some respondents also stated that police services should not be the ones to collect this information. Rather, questions on self-identification should be asked by other professionals or third-party individuals, such as social workers, mental health workers or addiction specialists.

Community respondents were also asked to identify the circumstances in which police officers should refrain from collecting these data. The following were some of the most commonly reported scenarios in which self-identification data should not be collected:

  • when individuals are in distress
  • when individuals are inebriated
  • when individuals are traumatized, have had previous negative experiences with the police or feel unsafe
  • during volatile interactions
  • during mental health crises.

"Data should be collected for criminal and non-criminal interactions"

Most respondents emphasized the importance of collecting Indigenous and racialized identity information for both criminal and non-criminal interactions. This was consistent with findings in the first phase of Statistics Canada's engagement (the September report)

Respondents highlighted that focusing only on criminal interactions would not fully address systemic issues. By analyzing data from both types of interactions, law enforcement agencies can address biases and disparities that may occur in a broader range of situations. Most respondents stated that through the process, individuals being questioned by police must be assured ("by actions and evidence, not just words") of the benefits to themselves and their families or communities.

Respondents mentioned benefits of collecting this type of data for both non-criminal and criminal interactions, including

  • to reveal disproportionate treatment in situations such as traffic stops
  • to demonstrate any evidence of over-policing and over-surveillance of racialized and Indigenous communities that is not captured by an exclusive focus on criminal incidents
  • to support more welcoming, inclusive and safer communities
  • to help identify systemic issues that may extend beyond criminal cases, enabling more effective policy changes.

Data analysis and dissemination

Overall, the collection of the data was regarded by community organizations as beneficial and needed. However, several recommendations were also made related to the analysis and dissemination of these data.

Respondents were asked what types of analysis they would like to see applied. Listed below are several of the prioritized analyses:

  • Intersectional analysis: This is a framework that helps analyze how individuals and communities are affected by various social identity factors as they interact with each other and with
    • society (in the context of group membership)
    • organizations (in the context of institutional power, such as policies and practices)
    • systems of power (including prejudice and discrimination).Footnote 5
    Examples of intersectionality are Indigenous identity; gender identity, especially for women, girls and Two-Spirit individuals; disability status; immigrant status; religion; age; socioeconomic status; and geographic location. Individuals have mixed identities.
  • Descriptive analysis: In this type of analysis, basic statistics such as frequencies, proportions and percentages are calculated to provide an overview of the distribution of those with Indigenous and racialized identities among accused persons and victims, showing, for example, overrepresentations, disproportionality and disparities. It can be used to measure trends from raw data to get a clear picture of what is happening.
  • Year-over-year comparison: Data from consecutive years are compared to understand short-term fluctuations and changes in the representation of Indigenous and racialized individuals in crime incidents.
  • Comparative analysis: This type of analysis compares the representation of those with Indigenous and racialized identities in different types of crimes and differential outcomes across racialized identities, geographic regions or time periods to identify patterns and disparities.
  • Geospatial analysis: This type of analysis uses geographic information systems to map the spatial distribution of crime incidents involving Indigenous and racialized individuals. This can reveal geographical patterns and disparities.
  • Geographic comparisons: Comparing the representation of individuals with Indigenous and racialized identities in crime incidents across different geographic regions can identify regional trends and disparities.

Additionally, the need to make data accessible to community organizations, while respecting individuals' privacy, was emphasized throughout the engagement. It was articulated that access to the data can empower communities to assess the extent of criminal behaviour within their communities, identify barriers to justice and use this information to develop interventions. As summarized by one respondent,

"Communities have an inherent right to the information collected for the exact purposes they intend to use it for, such as identifying trends, allocating resources, and developing programs; and continuing to develop and draw down responsibilities as negotiated in self-governing agreements."

Some community organizations also suggested that one way of making data accessible could be the creation and provision of a data "dashboard" that allows different communities to pull reports specific to their populations or community members, in keeping with principles such as OCAP, Footnote 6 FAIR Footnote 7 and CARE. Footnote 8

Regarding the dissemination of findings from the data, respondents from community organizations

  • cautioned about the potential negative reactions to results pointing to inequity, particularly from the police
  • had concerns about accountability and transparency from law enforcement
  • expressed worries about the public interpretation of findings, which could lead to varied reactions or misunderstanding of marginalized groups
  • had preoccupations about the released data potentially perpetuating colonialism, racism, racial discrimination or community profiling, or creating further harm to cultures, communities and individuals.

Education and awareness

Training police officers and other police service personnel (e.g., data analysts) on the why, how and when of data collection was a significant component of the feedback received from community organizations. Respondents indicated that this training should be developed in partnership with diverse groups, including members of or experts from Indigenous and racialized communities. Further, there were strong suggestions that such training be conducted by community members to support relevant and accurate perspectives, history sharing and experiences.

Common suggestions related to training included the following:

  • Co-designed workshops: Organize workshops where community members, law enforcement personnel and trainers work together to co-design the training materials. This ensures that the training is relevant, is accurate and resonates with all stakeholders.
  • Indigenous-led training: Collaborate with Indigenous organizations or experts to lead training sessions specific to Indigenous perspectives, history and experiences.
  • Continuous learning: Ensure that training is an ongoing process. Offer refresher courses and opportunities for law enforcement personnel to engage with communities beyond the initial training.
  • De-escalation and conflict resolution: Learn de-escalation strategies to manage tense situations, and practise conflict resolution techniques to ensure safety.
  • Trauma-informed practices: Recognize trauma and its potential impact on interactions, and use trauma-informed approaches to minimize re-traumatization.
  • Data collection purpose: Explain the purpose and benefits of the initiative, and detail when, what and how data should be collected. Emphasize the importance of accurate and unbiased data.
  • Intersectionality: Recognize the intersection of multiple identities and experiences, and address the complexities of individuals' identities.
  • Cultural awareness and sensitivity: Provide training on developing cultural competency, addressing biases and stereotypes, understanding cultural dynamics and sensitivities, understanding the fears of communities, and demonstrating acceptable body language and eye contact.
  • Feedback and continuous learning: Create opportunities for police to welcome feedback from community members and colleagues, and commit to ongoing learning and improvement.
  • Bias: Provide training on implicit and explicit biases and their impact on data collection.
  • Power imbalance: Provide training that includes discussions on power imbalances between police and victims or offenders and their impact on data collection.
  • Data misuse: Provide information about how data have historically been used to harm.

Respondents also voiced concerns related to training, including, most prevalently, the following:

  • If done in a superficial manner, without addressing underlying systemic racism or bias, training may further contribute to power imbalances and negative stereotypes.
  • There is a need to ensure compliance and accountability in the delivery of training.
  • Change management and best practices should be applied for police to welcome training and commit.

The following suggestions were proposed regarding effective training delivery:

  • Comprehensive: Consider a multi-day format to allow for more interactive sessions, case studies and group activities.
  • Ongoing: Encourage continuous learning.
  • Interactive: Incorporate a mix of presentations, discussions, group activities, case studies, role-plays and simulations to keep participants engaged and encourage active learning.
  • Accessibility: Include various components. Provide flexibility in the format, allowing for different learning styles and preferences.
  • Testing component: Include methods to determine the effectiveness of training to ensure that concepts are properly understood. Consider the possibility of testing all participants after each module, with a minimum score required to pass. If the passing score is not achieved, participants should have to retake the module.
  • Evolving: Continuously gather feedback from participants and community representatives to refine and improve the training content and approach. Training materials should continue to evolve over time based on new information.
  • Feedback loops: Collect feedback from the community through, for example, surveys, complaint submission mechanisms and engagement (open conversations or townhalls). Collecting feedback, reporting on the level of engagement and collaboration between law enforcement and communities, and regularly assessing outcomes can support improved training. These can also lead to building better relationships in the communities.

Community engagement

Community respondents were asked about how communities can be encouraged to be involved with the data collection initiative. Most respondents spoke of the need for a new culture of trust between police and Indigenous and racialized individuals. Discussions revealed the following commonly noted approaches for engaging with communities:

  • Explain clearly and succinctly why data are being collected and how they will be used and benefit communities. Explain what is in place to ensure the data will not be used to harm communities.
  • Ensure there is collaboration between police and local cultural leaders or community partners and organizations to raise awareness and enhance engagement with community members.
    • The importance of taking in feedback and implementing it was also highlighted.
  • Raise awareness of the data collection and educate the public (examples included through social media campaigns, information sessions in community spaces, community townhalls, public libraries, public transit advertising, electronic roadside billboards, podcasts, webinars and partnerships in schools).
    • Use multiple digital communication channels (e.g., social media, email, podcasts and commercials).
    • Produce plain language handouts, information packages and brochures that contain information in multiple languages and that are culturally specific.
  • Create personal contact in the communities to build relationships and earn trust. This could include, for example, building reciprocal relationships and making respectful visits to local cultural or religious centres to introduce the initiative to community members in an environment where they are comfortable.

Findings from engagement with academics

The engagement with academics focused on feedback related to the initiative as a whole and on the September report. Key aspects of the discussions with the academics were how to ensure successful collection and use of the data, as well as manage and report self-identification and officer perception data. Questions were also asked about training needs for police regarding data collection and the types of data analyses to conduct once data are collected.

Throughout the engagement, academics were also presented with questions on developing guidelines that incorporate Indigenous and racialized communities' perspectives while ensuring transparency and context in data publications.

In this second phase of engagement, academics reiterated their acknowledgment of the importance of this initiative and provided the following feedback in terms of operationalizing the collection.

Data collection and data standards

All academic participants saw the value of collecting these data using both the officer perception and self-identification methods for accused persons and victims to ensure all necessary data are captured. Academics also stressed the importance of differentiating between the two methods of collection.

Academics also raised some cautionary notes related to data collection:

data quality and the risk of intentional misclassification of individuals (and related accountability) by police (specifically, where both officer perceptions and self-reported identity are collected, there are concerns that officers may be able to change their perception entry after collecting the self-identification information)

  • overreliance on "catch-all" categories (for example, "unknown identity") for officer perception data collection, and its impact on data quality
  • resistance of individuals to self-identify, and its impact on data quality and misleading results
  • the impact of power dynamics that may affect how the victim or accused responds to questions pertaining to their Indigenous or racialized identity
  • the potential for officers, victims and accused to conflate nationality, racialized identity and ethnicity
  • the impact of refusing to provide self-identification information on the results.

Some academics also indicated that self-identification data should not be collected if officers perceive the interaction is dangerous. Some suggestions were made that individuals be offered the opportunity to self-report through a confidential third party.

Standards and guidelines

Academics were asked to provide feedback on how to ensure the data are collected in a standardized way (e.g., uniform record layouts) and on guidelines (e.g., operating manuals and best practice documentation) related to the technical aspects of data collection and what operational considerations need to be addressed. Numerous recommendations were provided by academics, including the following:

They suggested using insights from the United Kingdom's protocols for collecting information about race and ethnicity. The United Kingdom uses racialized categories broken down into many subgroups, which also include options for mixed race, and this is important for respondents who need to select multiple categories. Academics agreed that capturing intersectional identities is valuable.

Several academics proposed the development of clear guidelines and definitions to help respondents and officers differentiate between Indigenous and racialized identity categories.

Data analysis and dissemination

Academics were asked whether they had any feedback regarding the use of the data after they have been collected (e.g., analysis and dissemination). While there was broad support for this initiative, respondents identified some areas warranting caution and provided some recommendations:

  • There is a need to develop a robust framework to address privacy concerns related to data collection and sharing.
  • There is a need for contextual information related to the findings and how the data relate to social issues.
  • Leveraging record linkages, whenever possible, would be a way to maximize data to enhance analysis.
  • It is important to develop approaches to measuring concepts such as overrepresentation, differential outcomes and systemic bias that can be unified and consistent across jurisdictions, provinces and organizations to ensure meaningful comparisons.
  • There is a need to commit to transparent public reporting, including recommendations for actioning results from the data, to better the lives of Canadians.

Academics underscored the importance of ensuring that data are not collected or used

  • to reinforce negative stereotypes
  • to justify additional policing and surveillance of communities
  • to support social and economic policies that can harm Indigenous and racialized communities
  • if the accused person or victim refuses to self-identify
  • if the data collection is not accompanied by an educational and awareness campaign to inform Indigenous and racialized communities of the purpose and value of this initiative.

Finally, many academic respondents reported the importance of acknowledging limitations when using sensitive data such as PIRID. Key limitations include those of the Indigenous and racialized identity categories used, as well as the scope of the UCR Survey being limited to criminal incidents only and not all police interactions.

Education and awareness

Most academic respondents advised that clearly articulating the purpose of the data collection would be a significant factor for gaining both community and police personnel support. This was a key recommendation for training police services and raising awareness among communities.

Addressing power dynamics in police interactions was identified as another crucial component, given the authoritative position of police officers. The importance of creating training programs that foster a nuanced understanding of power dynamics was identified as a key contributing factor to fair and unbiased data collection.

The following were key takeaways from the discussions with academics, when they were asked for specifics related to officer training:

  • Training should be mandatory and could be part of police officers' yearly reviews. Training should also be ongoing.
  • Training should not be delivered by a police officer, or a person involved in policing, but a third party.
  • Communities should be consulted on training development and delivery.
  • Establishing training standards at the provincial, territorial and federal levels could support police services in ensuring delivery of training.
  • Training should include many examples and scenarios.

Community engagement

Community engagement emerged as a key component of the engagement with academics. The majority of academic participants indicated that police services and the public both need to be informed about the importance of collecting Indigenous and racialized identity data. This involves disseminating information about the purpose of data collection, dispelling misconceptions, and ensuring the public understands the "why" behind the initiative and how the data will or will not be used.

Furthermore, academics highlighted the significance of acknowledging historical issues (e.g., carding practices) that continue to impact police–community relations and educating the public and affected communities on the benefits of the new data collection initiative. The consensus is that historical context plays a crucial role in shaping public perception and acceptance:

"…this will be the biggest hurdle—attempting to re-educate the public on the fact that this data is for addressing systemic racism and not for carding purposes. The need to address the history of systemic racism experienced by communities at the hand of Canadian institutions is necessary to ensure the community's acceptance and trust. This will also be imperative to understanding how the communities want the information and data to be collected."

Finally, academics emphasized the active role that communities should play in shaping data collection. The importance of consulting communities during the creation of training programs was stressed to ensure inclusivity and transparency. This theme underlines the need for a collaborative approach, recognizing the impact of data collection on the communities being represented.

Indigenous data sovereignty at Statistics Canada

The Government of Canada recognizes the unique rights, interests and circumstances of First Nations persons, Métis and Inuit and implements a distinctions-based approach to Indigenous statistics in support of self-determination and reconciliation.

Statistics Canada carries out its mandate of providing high-quality statistics that matter. It strives to carry out its mission in a way that is ethical, respectful and responsive to First Nations, Métis and Inuit needs and concerns, and to collaborate with First Nations, Métis and Inuit governments, communities and organizations. The agency's reputation, and ultimately its ability to produce high-quality Indigenous statistics, depends on appropriate engagement and relationship building.

As the national statistical agency, Statistics Canada carries out its work under theStatistics Act(the act). Under this federal legislation, Statistics Canada collects, compiles and publishes statistical information that is used by governments, businesses, researchers and the general public to understand demographic, social and economic realities across Canada. Any data collected under the authority of the act are kept confidential and are used only for statistical purposes. This includes data related to First Nations, Métis and Inuit communities.

Statistics Canada understands the importance of the ownership, control, access and possession (OCAP) principles that apply specifically to First Nations data, and the issues that underlay their development regarding data collection and research in First Nations communities. As a federal agency, Statistics Canada has been given the responsibility of acting as a responsible steward of its data holdings and works under a governance structure outlined by the act. The OCAP principles are a data governance structure that was developed by the First Nations Information Governance Centre and allow individual First Nations communities to determine whether projects or processes are OCAP-compliant. Both data governance regimes allow for the respective organizations to act as responsible data stewards and to ensure that privacy and confidentiality are protected while maintaining work and projects that are relevant, are valuable and uphold the policy to "do no harm." Métis and Inuit are also developing their own data governance strategies and frameworks.

Final recommendations

Based on the feedback received throughout the engagement for the PIRID initiative, the following final recommendations were developed to support the implementation of a national data collection strategy.

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

Recommendation 2

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and the "officer perception" method.

Recommendation 3

The Canadian Association of Chiefs of Police should work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes and workflow.

Recommendation 4

Any training delivered by Statistics Canada, or the police community, should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy makers and the police.

Recommendation 5

In developing or delivering any additional training related to the collection of Indigenous and racialized identity data, police services should consider including components related to systemic racism, the purposes of collecting these data, power differentials, the importance of informed consent without reprisal when collecting self-identification data, cultural competency, sensitivity training, and ongoing training and evaluation to address evolving needs and best practices.

Recommendation 6

Police services should consider how the voices of local community members can be incorporated in the development and implementation of a data collection initiative and related training through meaningful engagement and collaboration throughout the entire process, from initial planning to implementation and evaluation. Community advisory boards or working groups can provide ongoing input and guidance.

Recommendation 7

The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes should be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context (e.g., colonialism, ongoing systemic barriers, the social determinants of health and inequities for Indigenous and racialized peoples, etc.) in all publications and related dissemination products.

Recommendation 8

To ensure consistency, the standards developed in the context of this initiative should be considered for future data collection within the justice and community safety sectors.

Recommendation 9

Police services should develop plans for implementing the standards and guidelines co-developed by Statistics Canada and the Canadian Association of Chiefs of Police in their data collection initiatives or processes, considering their local contexts and the need for flexibility and adaptability. Leveraging technology and data analytics can improve data collection and analysis.

Recommendation 10

The Canadian Association of Chiefs of Police should develop mechanisms that discourage police services from using any part of the self-identification data collection process as a performance metric and recommend systems of reassurance for police service members and the communities they serve. Performance metrics should focus on outcomes and impact, rather than on the quantity of data collected. Consideration should be given to developing a code of ethics for the collection and use of Indigenous and racialized identity data.

Recommendation 11

Statistics Canada should develop guardrails to ensure the responsible use of Indigenous and racialized identity data collected through the Uniform Crime Reporting Survey. Transparency, accountability and a data governance framework should be prioritized to oversee the collection, storage and use of these data.

Recommendation 12

Police services should consider expanding the collection of Indigenous and racialized identity information for specific types of police incidents beyond criminal incidents, such as traffic stops, use of force incidents and calls for service, and for other involved persons, such as persons of interest and subjects of various interactions, to provide a more comprehensive picture of policing interactions.

Translating PIRID Recommendations into Action

System updates at Statistics Canada

Updates to the UCR Survey system at Statistics Canada began at the end of 2022, and, as of February 13, 2024, the system has been updated to version 2.5 to enable collection of data reported by police services. Updates to the system were based on feedback received through the engagements, specifically Recommendations 1 and 2:

  • Recommendation 1: The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.
  • Recommendation 2: The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and the "officer perception" method.

Recognizing that police services have flexibility and discretion with the collection method (i.e., officer perception, self-identification or both), the UCR 2.5 allows for police to report Indigenous and racialized identity regardless of the method of collection. Footnote 9

Data will become available at Statistics Canada upon police services' adoption of the updated UCR Survey system. Footnote 10 Adoption of UCR 2.5 is not mandatory.

Development of Key Documents: Operational Guidelines and Analytical Framework

The recommendations emerging from the engagement feedback were also used to inform the development of two critical documents: (i) Operational Guidelines for aiding police services in the implementation of PIRID (i.e., the collection and use of the data); and (ii) an Analytical Framework to support analysts and researchers in the rigorous, ethical, and responsible analysis, interpretation and dissemination of the data.

These two documents were created by a new joint StatCan-CACP Special Purpose Committee, which waslaunched in September 2023 in response to Recommendation 3, namely, for the CACP to work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes and workflow.

For developing the PIRID Operational Guidelines for police services, five expert working groups were formed under the SPC to elaborate the following main themes emerging from the consultative engagement feedback:

  • community engagement;
  • legislative and regulatory considerations;
  • education and awareness, including training;
  • data standards, including system changes; and
  • data analysis and dissemination.

Moreover, the working group tasked with developing the operational guidelines around data analysis and dissemination for police services was also responsible for the creation of a PIRID Analytical Framework for researchers and data analysts more broadly, which delivers on Recommendation 7:

  • Recommendation 7: The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes should be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context (e.g., colonialism, ongoing systemic barriers, the social determinants of health and inequities for Indigenous and racialized peoples, etc.) in all publications and related dissemination products.

Thus, the Framework aims to:

  • support the responsible and ethical use of PIRID by proposing guiding principles to help avoid further stigmatization and marginalization of communities as a result of data use
  • equip data users with tools and guidance for the careful, robust and culturally competent interpretation of the data.

Both the Operational Guidelines and Analytical Framework will be made publicly available in Summer 2025.

References

Appendix: Discussion Guides

A: Discussion guide for community organizations

Discussion guide for community organizations on Police-Reported Indigenous and Racialized Identity Statistics via the Uniform Crime Reporting Survey

Background

On July 15, 2020, Statistics Canada and the Canadian Association of Chiefs of Police (CACP) released a joint statement announcing their commitment to working on the collection of data on the Indigenous and racialized identity of all victims and accused persons as it pertains to criminal incidents through the Uniform Crime Reporting Survey (UCR).

As the first step in this initiative, Statistics Canada embarked on a collaborative engagement process from July 2021 to February 2022 to acquire feedback from diverse perspectives on the collection of Indigenous and racialized identity data through the UCR Survey. This mobilization process involved collaborative efforts among various community organizations, academics, police services, the public and other parties of interest including government bodies at the national, provincial/territorial, municipal, and local levels. Perspectives and opinions heard throughout this engagement led to the release of a Report and Draft Recommendations on September 1, 2022, which can be found in the following link.

Report and Draft Recommendations: Police-Reported Indigenous and Racialized Identity Statistics via the Uniform Crime Reporting Survey

The recommendations developed from the results of the initial engagements are informing the next steps of the initiative (see Appendix A for the list of recommendations).

Objectives of the current engagement (agenda item):

Information for this initiative will be collected through the Uniform Crime Reporting (UCR) Survey. The UCR Survey, introduced in 1962, is Canada's primary source of information on police-reported data. The UCR Survey collects annual statistics on all federal crimes and offences recorded by police services to measure the incidence of crime in Canadian society and its characteristics.

To implement the recommendations developed from the initial engagements, and operationalize the initiative for police services across Canada to collect Indigenous and racialized identity data from victims and persons accused in criminal incidents, we are:

  • Seeking feedback on the recommendations (particularly recommendations 1, 3, 4 and 5; see Appendix A) from Indigenous and racialized community organizations, in order to bring further refinements to the approach and ensure that we take any additional considerations into account.
  • Gauging any further concerns related to operationalizing the initiative and next steps.

Engagement participant information

The following information will help us to compile and analyze the results of the engagement.

Please note that all individual and group responses will remain confidential. If you have any questions regarding the Engagement Document, please contact the Canadian Centre for Justice and Community Safety Statistics (CCJCSS) at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca.

Please send your response to CCJCSS at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca

If multiple representatives from your organization are responding, please combine your answers into one document.

If responding as an organization, please provide the following information:

  • Full name of Organization:
  • Address:
  • Name and position of contact for the purpose of this engagement:
  • Contact email address:
  • Contact phone number:

List of contributors to this engagement (optional):

  • Name
  • Position

May we contact you for follow up information and discussion if necessary? Y / N

If responding as an individual:

  • Name:
  • Position/title:
  • Contact email address:
  • Contact phone number:
  • Province:

May we contact you for follow up information and discussion if necessary? Y / N

Discussion Questions

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

The UCR Survey will be updated to include 2 new fields for victims and accused persons in criminal incidents (Indigenous and racialized identity through self-identification and Indigenous and racialized identity through officer perception).

Considerations

  1. Self-Identification and officer perception have their own distinct advantages:
    1. The "self-identification" approach entails that the information on Indigenous or racialized identities of the people involved in a criminal incident is voluntarily provided by accused and victims. This method is said to provide the most accurate data and can help empower communities by providing data representing the needs of communities.
    2. The "officer perception" approach entails that the Indigenous or racialized identities of the people involved in a criminal incident (i.e., accused and victims) are recorded based on a police officer's subjective assessment of these individuals. This method is deemed to be important because perceptions could influence an officer's decision-making in an interaction and lead to disparities in outcomes.
  2. Much of the feedback highlights the importance of self-identification to Indigenous and racialized community organizations.

Question 1.1

Do you have any concerns about police collection, management and reporting of: a. self-identification and b. officer perception?

a. Any concerns about collecting either one or the other?

Question 1.2

How can we encourage communities to be involved in the initiative to ensure that it is a collective (i.e., collaborative and participatory) endeavor?

Question 1.3

For the self-identification collection method, individuals are provided with the option to decline or refuse to respond. What are some ways that we can clearly ensure that the data are collected through an individual's consent?

a. How do we highlight and communicate the importance of consent?

Question 1.4

How can our organization raise awareness for this initiative and the collection of Indigenous and racialized identity data within your community?

a. How should this information be disseminated to ensure that all persons involved are aware of the intended objectives for this initiative?

Recommendation 3

The Canadian Association of Chiefs of Police work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that fit police procedures, processes, and workflow.

Procedures for collecting this information should be developed to reflect the various scenarios that officers are likely to encounter when asking these questions. Nevertheless, any approach should be standardized to ensure consistency across all police services. It should, among other things, include a standardized explanation of purpose so that police officers can clearly explain the motivation behind this data collection to both victims and accused. It is also clear from the feedback received in the initial set of engagements that there are situations in which it might be inappropriate, impractical or impossible to collect data on the Indigenous and racialized identity of accused and victims. The approach needs to include specific collection standards and guideline to account for these situations.

Considerations

  1. Police officers are in positions of authority when interacting with the public. As such, clearly and respectfully communicating consent as it relates to self-reported data is of paramount importance.
  2. Ensure that the data optimally reflects the realities of Indigenous and racialized individuals by allowing the selection of multiple Indigenous and/or racialized identities.

Question 2.1

How should the police communicate the purpose behind the collection of self-identification data when interacting with victims or accused?

Question 2.2

How should this explanation take place? (e.g., police explaining to the individual, providing an information card etc.)

a. How should the police collect the data during an interaction?

Question 2.3

Are there situations where it would not be appropriate to collect this information (using either method)?

a. Are their situations where it would not be appropriate for police officers to ask people directly for their Indigenous or racialized identities?

Question 2.4

Do you have any safety or security concerns for victims, accused persons or their communities in relation to this data collection?

Question 2.5

What are your thoughts on collecting this data for criminal and non-criminal interactions?

Question 2.6

How should your community members be informed about this initiative?

a. What role should police services play in informing communities about the initiative?

Recommendation 4

Any training delivered by Statistics Canada, or the police community should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy-makers, and the police.

Several respondents highlighted that the training should provide information on when data should be collected, what data should be collected, how they should collect it and why it is being collected. There was an emphasis on the importance of clearly explaining the purpose of this data collection to help facilitate collection by police officers. When developing the training, respondents also highlighted the importance of ensuring that the content and delivery are informed by the perspectives of Indigenous and racialized communities, thus ensuring that the training is culturally appropriate and that the initiative is a collective endeavor. Regarding training components relating to the experiences of First Nations, Inuit, and Métis people, respondents suggested drawing on the expertise of Indigenous Elders and prioritizing Indigenous-led training models.

Considerations

a. The training guidelines should incorporate Indigenous and racialized community perspectives and organizational needs.

Question 3.1

Do you have any suggestions on how an inclusive approach to training should be conducted?

Question 3.2

Current recommendations for training include the following: information on why the data is being collected, when it should be collected, what data should be collected, how they should collect it, and how the data will be used. Is there anything else that you think should be included?

Question 3.3

What topics should be included in the training of police?

Question 3.4

Do you have any concerns about the training on the collection of this data?

Question 3.5

Who do you think should be involved in providing training?

  1. How long should the training be and what should it include to maximize its effectiveness ?
  2. Howcan we determine the effectiveness of training?

Recommendation 5

The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context to all its publications and related dissemination products.

Respondents were asked to provide advice on the analysis and use of data on the Indigenous or racialized identity of accused persons and victims of criminal incidents. They highlighted the importance of leveraging perspectives of Indigenous and racialized communities to ensure that the data that are analyzed and used provide a comprehensive picture of their experiences.

Considerations

  1. Potential for data quality/reliability issues.
  2. Ensure that the data collected are accessible and presented as transparently and as completely as possible.
  3. Necessity to include context during analysis, and in the interpretation and reporting of findings, to help reduce any potential misrepresentation.

Question 4.1

How should we engage diverse Indigenous and racialized communities/organizations in the development of analytical guidelines related to these data, to ensure that their input informs how the data is eventually interpreted and how the results are presented?

Question 4.2

What types of data analysis should be conducted with the data collected?

  1. What types of data comparisons should be made to measure change over time?
  2. Do you have any concerns related to data quality or reliability of results?
    1. If so, how can these concerns be addressed?

Question 4.3

Providing context to accompany the publication and dissemination of Indigenous and racialized identity data may include, but is not limited to, historical context and their continuing impacts on the current experiences of individuals and communities.

a. What contextual information should be included when publishing data on Indigenous and racialized peoples?

Final thoughts

Do you have any final thoughts on this initiative?

Key questions

  • How would you like to be notified regarding the next steps of the initiative?
  • What are potential positive/negative impacts do you think the initiative will have on Indigenous and racialized communities?
  • How can we improve the initiative to ensure that it has a meaningful impact on your community?
  • Any other thoughts, comments, or concerns about this initiative (not already shared above)?

B: Discussion guide for police organizations

Engagement Document
Police-Reported Indigenous and Racialized Identity Datathrough the Uniform Crime Reporting Survey
(Phase II)

Overview

Scope of the Data Collection Initiative:

Statistics Canada, in partnership with the Canadian Association of Chiefs of Police (CACP), is working toward determining the best method for collecting data on the Indigenous and racialized identity of all accused and victims of criminal incidents through its Uniform Crime Reporting (UCR) Survey. The UCR Survey collects information on all criminal incidents reported by Canadian police services to monitor the nature and extent of police-reported crime in Canada. Every police service in Canada reports to the UCR Survey. Only information on criminal incidents related to federal statutes are collected, which accounts for a minority of police-citizen interactions.

Detailed description of the Uniform Crime Reporting (UCR) Survey:
Uniform Crime Reporting Survey (UCR)

Detailed description of the joint partnership with CACP:
Resolution - Canadian Association of Chiefs of Police

In the first phase of the initiative, Statistics Canada embarked on an engagement process from July 2021 to February 2022 to seek feedback on the collection of Indigenous and racialized identity data through the UCR Survey. This engagement sought advice on the value of collecting this sensitive information, but also input on how the police should collect and report the data, what information should be reported by the police, how the data should be used and accessed, as well as other related concerns. This led to the development of an interim report and recommendations published on the Statistics Canada website: Police-Reported Racialized and Indigenous Identity Statistics via the Uniform Crime Reporting Survey: Report and Draft Recommendations

There was broad support amongst respondents for this initiative across all sectors canvassed, including community organizations and police services. As such, the feedback from respondents has led to the development of the following recommendations on the best way to move forward with this initiative so that the collection of data on the Indigenous and racialized identity of accused persons and victims of crime fulfill the data needs of communities, the police, policymakers, and the Canadian population broadly.

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

Recommendation 2

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and "officer perception" method.

Recommendation 3

The Canadian Association of Chiefs of Police work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes, and workflow.

Recommendation 4

Any training delivered by Statistics Canada or the police community should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy-makers, and the police.

Recommendation 5

The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context to all its publications and related dissemination products.

Recommendation 6

To ensure consistency, the standards developed in the context of this initiative should be considered for future data collection within justice and community safety sectors.

Objectives of the Phase II engagement:

Statistics Canada is currently conducting a second phase of engagements with various partners from diverse perspectives, including community organizations, academics, police services and other parties of interest at the national, provincial/territorial, municipal, and local government level. This engagement seeks feedback on operationalizing data collection and analysis. Specifically, the objectives of this document are to:

  • Walk through Recommendations 1 and 3 to obtain any further refinements or considerations from police services.
  • Understand police services' requirements and concerns.
  • Obtain feedback and endorsement on the work plan and next steps.

Engagement participant information

The following information will help us to compile and analyze the results of the engagement.

Please note that all individual and group responses will remain confidential. If you have any questions regarding the Engagement Document, please contact the Canadian Centre for Justice and Community Safety Statistics (CCJCSS) at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca.

Please send your response to CCJCSS at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca

If multiple representatives from your organization are responding, please combine your answers into one document.

If responding as an organization, please provide the following information:

  • Full name of Organization:
  • Address:
  • Name and position of contact for the purpose of this engagement:
  • Contact email address:
  • Contact phone number:

List of contributors to this engagement (optional):

  • Name
  • Position

May we contact you for follow up information and discussion if necessary? Y / N

If responding as an individual:

  • Name:
  • Position/title:
  • Contact email address:
  • Contact phone number:
  • Province:

May we contact you for follow up information and discussion if necessary? Y / N

Discussion Questions

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

  1. CCJCSS will adapt the UCR survey to add 2 separate fields (self-identification and officer perception)
  2. While the system undergoes technical changes, CACP & CCJCSS will work on developing guidelines on how to best collect the information, including the timing of collection

Considerations

  1. Self-Identification and police perception have their own advantages:
    1. The "self-identification" approach entails that the information on the Indigenous or racialized identities of the people involved in a criminal incident is volunteered by accused and victims. This method is said to provide the most accurate data and can help empower communities by providing data representing the needs of communities.
    2. The "officer perception" approach entails that the Indigenous or racialized identities of the people involved in a criminal incident (i.e., accused and victims) are recorded based on a police officer's subjective assessment of these individuals. This method is deemed to be important because perceptions could influence an officer's decision-making in an interaction and lead to disparities in outcomes.
  2. The UCR solution should be evergreen and also allow for future refinements to data collection.
  3. Much of the feedback highlights the importance of self-identification to communities.

Question 1.1

Are there any current policies in your service or province stating that police officers should not collect Indigenous and racialized identity data?

a. If yes, what are your guidelines on collecting these data?

Question 1.2

Does your police service currently collect any data (via the self-identification or officer perception methods) on Indigenous and racialized identity?

a. If yes, what type of data do you currently collect?

Question 1.3

Do you have any concerns about the collection of both types of data (i.e., self-identification and officer perception)?

b. Do you have any concerns regarding the use of the data after it has been collected (e.g., analysis and publication)?

Question 1.4

What are your thoughts on making the officer perception field a mandatory field?

Question 1.5

How should the data collection be navigated while prioritizing consent for self-identification data?

Recommendation 3

The Canadian Association of Chiefs of Police work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that fit police procedures, processes, and workflow.

Procedures for collecting this information should be developed to reflect the various scenarios in which officers are likely to encounter when asking these questions. Nevertheless, any approach should be standardized to ensure consistency across all police services. It should, among other things, include a standardized explanation of purpose so that police officers can clearly explain the motives behind this data collection to victims and accused. It is also clear from the feedback received that there are situations in which it might be inappropriate, impractical or impossible to collect data on the Indigenous and racialized identity of accused and victims. The approach needs to include specific collection standards and guideline to account for these situations.

Considerations

  1. For those already collecting data /or soon to be collecting, how do we consider standards/guidelines in place?
  2. Privacy legislation and the Statistics Act.
  3. Varying relationships between police and different communities.
  4. Records Management Software (RMS): timing of the updates and adoption of new version by police services.

Question 2.1

What operational considerations need to be addressed?

Question 2.2

How should standards and guidelines for police officers collecting this information be developed?

Question 2.3

What are some ways that the CCJCSS can help police officers in explaining to victims and accused persons the motivation for this data collection?

a. At what point should the motivation be explained?

Question 2.4

When would be the ideal time during an interaction related to a criminal incident to collect self-identification data?

a. How should data be collected in these interactions (e.g., self-filled via iPad, collect at the time of arrest, initial introduction, after trust has been established?)

Question 2.5

In which circumstances should police officers refrain from collecting these data?

Question 2.6

Do you have any concerns about training officers on how to collect these data ?

a. Do you have any suggestions on how the training should be conducted?

General Comments

Question

Do you have any other comments, questions, concerns or recommendations regarding operationalizing the collection of information on the Indigenous and racialized identity of victims and accused persons as it relates to criminal incidents through the UCR Survey?

Next steps

Statistics Canada is committed to working with the policing community and key organizations to enable police to report statistics on the Indigenous and racialized identity of all victims and accused persons. Your feedback marks an important step in this engagement process for collecting more disaggregated data.

As a next step, Statistics Canada is currently working with the Canadian Association of Chiefs of Police (CACP) to launch a CACP special purpose committee to guide the next steps of the initiative, particularly as they relate to operationalizing the September recommendations, and the production of guidelines for police services to implement data collection. Thus, feedback received through these engagements will be presented to the committee to collaboratively identify next steps in establishing data collection standards and guidelines.

From a national standpoint, this initiative aims to develop evergreen national standards and guidelines for the data collection and analysis that build on expertise, established frameworks, lessons learned and best practices. We look forward to continuing to work together in this critical phase of operationalizing the data collection and analysis of police-reported identity information and finding a balance that will result in sound and meaningful data for all jurisdictions.

Thank you for your valuable input and participation.

C: Discussion guide for police associations

Engagement Document

Police-Reported Indigenous and Racialized Identity Datathrough the Uniform Crime Reporting Survey

(Phase II)

Overview

On July 15th, 2020, Statistics Canada and the Canadian Association of Chiefs of Police (CACP) released a joint statement committing to working with Canada's policing community and organizations to collect police-reported information on the Indigenous and racialized identity of all victims and accused persons as it pertains to criminal incidents through its Uniform Crime Reporting (UCR) Survey. The UCR Survey collects information on all criminal incidents reported by Canadian police services to monitor the nature and extent of police-reported crime in Canada. Every police service in Canada reports to the UCR Survey. Only information on criminal incidents related to federal statutes are collected, which accounts for a minority of police-citizen interactions. Accordingly, incidents outside this purview are not reported to the UCR Survey. Incidents not covered by the UCR Survey include provincial statute offences, by-law infractions, use of force interactions, traffic stops and any other non-criminal incident or police interaction.

Whereas this initiative is focused on Statistics Canada's collection of race-based data through the UCR Survey, police services may choose to expand the scope of this data collection to include other type of interactions for their own internal use. For instance, police services in the province of Ontario are mandated to collect race-based and Indigenous data on persons involved in use of force incidents. Furthermore, Statistics Canada's present focus on UCR-based data collection does not preclude future expansion of the scope to other areas of interest. The current initiative is a first step toward gathering data that will help shed light on the experiences of Indigenous and racialized groups as they relate to policing and the criminal justice system more broadly.

Detailed description of the Uniform Crime Reporting (UCR) Survey:
Uniform Crime Reporting Survey (UCR)

Detailed description of the joint partnership with CACP:
Resolution - Canadian Association of Chiefs of Police

Phase I: Statistical Engagement:

Statistics Canada embarked on an engagement process from July 2021 to February 2022 to seek feedback on the collection of Indigenous and racialized identity data through the UCR Survey. This engagement sought advice not only on the value of collecting this sensitive information, but also input on how the police should collect and report the data, what information should be reported by the police, how the data should be used and accessed, as well as other related concerns. This led to the development of an interim report and recommendations published on the Statistics Canada website: Police-Reported Racialized and Indigenous Identity Statistics via the Uniform Crime Reporting Survey: Report and Draft Recommendations

There was broad support amongst respondents for this initiative across all sectors canvassed, including community organizations and police services. As such, the feedback from respondents has led to the development of the following recommendations on the best way to move forward with this initiative, so that the collection of data on the Indigenous and racialized identity of accused persons and victims of crime fulfill the data needs of communities, the police, policymakers, and the Canadian population broadly.

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

Recommendation 2

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and "officer perception" method.

Recommendation 3

The Canadian Association of Chiefs of Police work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes, and workflow.

Recommendation 4

Any training delivered by Statistics Canada or the police community should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy-makers, and the police.

Recommendation 5

The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context to all its publications and related dissemination products.

Recommendation 6

To ensure consistency, the standards developed in the context of this initiative should be considered for future data collection within justice and community safety sectors.

Phase II: Operationalization

Statistics Canada is currently conducting a second phase of engagements with various partners from diverse perspectives, including community organizations, academics, police services and other parties of interest at the national, provincial/territorial, municipal, and local government level. The purpose of these engagements is to seek feedback on operationalizing data collection and analysis.

Engaging police associations at this juncture is a crucial step in operationalizing this initiative. With the interest of your members in mind, we are seeking your input on next steps. Specifically, the objectives of this document are to:

  • Seek your feedback on the recommendations (particularly recommendations 1, 3 and 4), to bring further refinements to the approach and ensure that we take any additional considerations into account; and
  • Identify and document concerns from police associations related to operationalizing the initiative or next steps so that we can properly address any challenges or concerns moving forward.

Engagement participant information

The following information will help us to compile and analyze the results of the engagement.

Please note that all individual and group responses will remain confidential. If you have any questions regarding the Engagement Document, please contact the Canadian Centre for Justice and Community Safety Statistics (CCJCSS) at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca.

Please send your response to CCJCSS at statcan.ucrengagementmobilisationduc.statcan@statcan.gc.ca

If multiple representatives from your organization are responding, please combine your answers into one document.

If responding as an organization, please provide the following information:

  • Full name of Organization:
  • Address:
  • Name and position of contact for the purpose of this engagement:
  • Contact email address:
  • Contact phone number:

List of contributors to this engagement (optional):

  • Name
  • Position

May we contact you for follow up information and discussion if necessary? Y / N

If responding as an individual:

  • Name:
  • Position/title:
  • Contact email address:
  • Contact phone number:
  • Province:

May we contact you for follow up information and discussion if necessary? Y / N

Discussion Questions

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

The UCR Survey will be updated to include 2 new fields for victims and accused persons in criminal incidents (Indigenous and racialized identity through self-identification and Indigenous and racialized identity through officer perception).

Considerations

  1. Self-Identification and police perception have their own advantages:
    1. The "self-identification" approach entails that the information on Indigenous or racialized identities of the people involved in a criminal incident is voluntarily provided by accused and victims. This method is said to provide the most accurate data and can help empower communities by providing data representing the needs of communities.
    2. The "officer perception" approach entails that the Indigenous or racialized identities of the people involved in a criminal incident (i.e., accused and victims) are recorded based on a police officer's subjective assessment of these individuals. This method is deemed to be important because perceptions could influence an officer's decision-making in an interaction and lead to disparities in outcomes.
  2. Much of the feedback highlights the importance of self-identification to Indigenous and racialized community organizations.

Question 1.1

Are there any concerns about the collection of both types of data (i.e., self-identification and officer perception)?

  1. Any concerns about collecting either one but not the other?
  2. Does your association have any concerns regarding the use of the data after it has been collected (e.g., analysis and publication)?
    1. Do you have any suggestions or guidance on how these concerns can be addressed?

Question 1.2

Do you have concerns with making the officer perception field a mandatory field?  If so, how do we ensure this data is collected?

Question 1.3

How can CCJCSS assist your association in raising awareness for this initiative among officers on the collection of Indigenous and racialized identity data?

a. How should this information be disseminated to your membership that effectively ensures all officers are aware of the intended objectives for this initiative?

Question 1.4

What are some ways your members could benefit from the data collection (please specify if your response applies to officer-perception, self-identification, or both)?

Question 1.5

What types of reassurances do you believe your members would need to encourage cooperation related to the data collection (e.g., reassurances related to fears of being reprimanded)?

  1. Would these be the same for officer-perception and self-identification?
  2. How can the CCJCSS support in the provision of these reassurances?

Recommendation 3

The Canadian Association of Chiefs of Police work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that fit police procedures, processes, and workflow.

Procedures for collecting this information should be developed to reflect the various scenarios that officers are likely to encounter. Regardless of collection method, any approach should be standardized to ensure consistency across all police services. It should, among other things, include a standardized explanation of purpose so that police officers and the general public can clearly understand the motivation behind this data collection. It is also clear from the feedback received in the initial set of engagements that there are situations in which it might be inappropriate, impractical or impossible to collect both self-identified and officer perception data on the Indigenous and racialized identity of accused and victims. The approach needs to include specific collection standards and guidelines to account for these situations.

Considerations

  1. The importance of acknowledging police officers' positions of authority when interacting with the public that may affect the willingness of some victims and accused persons to truly consent to the collection of self-identification information.
  2. Recognizing the varying relationships and experiences different communities have with police.
  3. Ensuring that the data analyses and reporting reflect the realities of policing, reporting and public experiences of police.

Question 2.1

Who should collect officer-perception information (e.g. first officer interacting with individual, arresting officer, etc.)?

b. When should the officer-perception data be collected?

Question 2.2

When collecting Indigenous and racialized identity information using the self-identification method, how should police services communicate the purpose behind the collection of the data when interacting with victims or accused?

a. What are some ways that CCJCSS can help police officers in explaining the motives for this data collection?

Question 2.3

For the self-identification collection method, individuals are provided with the option to decline or refuse to respond.

  1. What are some ways that CCJCSS can support officers to clearly ensure that the data are collected through an individual's consent?
  2. How do we highlight and communicate the importance of consent?

Question 2.4

When would be the ideal time during a criminal interaction to collect self-identification data (at the time of arrest, during initial introduction/identification, after trust has been established)?

  1. For Accused?
  2. For Victims?
  3. Do you have any suggestions for how the data should be collected by police services in these criminal interactions (e.g., police personnel reading out the question)?

Question 2.5

Are there situations where it would not be appropriate to collect this information?

Question 2.6

Do you have any safety or security concerns for your membership and colleagues in relation to this data collection?

Question 2.7

Do you have any suggestions for how police officers can balance the need to collect these data while respecting communities and the individual right to refuse providing self-identification data?

Recommendation 4

Any training delivered by Statistics Canada, or the police community should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy-makers, and the police.

Several respondents highlighted that the training should provide information on when data should be collected, what data should be collected, how they should collect it and why it is being collected. There was an emphasis on the importance of clearly explaining the purpose of this data collection to help facilitate collection by police officers. When developing the training, respondents also highlighted the importance of ensuring that the content and delivery are informed by the perspectives of Indigenous and racialized communities, thus ensuring that the training is culturally appropriate and that the initiative is a collective endeavour. Regarding training components relating to the experiences of First Nations, Inuit, and Métis people, respondents suggested drawing on the expertise of Indigenous Elders and prioritizing Indigenous-led training models.

Considerations

a) The training guidelines should incorporate Indigenous and racialized community perspectives and organizational needs.

Question 3.1

Do you have any suggestions on how to incorporate feedback and advice from diverse communities in police training on the collection of data for:

  1. Officer perception?
  2. self-identification?

Question 3.2

Current recommendations for training include the following: information on when data should be collected, what data should be collected, how they should collect it and why it is being collected. Is there anything else that you think should be included?

Question 3.3

What topics should be included in the training for police?

Question 3.4

Do you have any concerns about the training on the collection of this data?

Question 3.5

Are there situations where it would not be appropriate to collect this information?

Question 3.6

Who do you think should be involved in developing and providing training?

Question 3.7

Are there any other training, resources or supports that your members could benefit from?

General Comments

Do you have any final thoughts on this initiative?

Examples:

  • What resources/supports do you need to support your members as it relates to this initiative?
  • How would you like to be notified regarding the next steps of the initiative?
  • What impact do you think the initiative will have on the members of your association?
  • How can we improve the initiative to ensure that it has a meaningful impact on policing and the public perception of police services and officers?
  • What parts of this initiative do you think should be applied to other types of police interactions (non-criminal)?

Next steps

The September Report and draft recommendations was an interim report and is being used to inform the way forward, with room for further consideration, elaboration and development in light of emerging feedback and information. Feedback received through the engagements will further inform best practice recommendations, alignment opportunities and guidelines that consider these various perspectives.

In addition to these engagements, Statistics Canada is currently working on launching a CACP Special Purpose Committee to guide next steps. Issues, concerns and considerations raised throughout the engagements will be presented to the committee in order to ensure that they are considered in the development of national standards and guidelines for the collection and analysis of Indigenous and racialized identity information.

Applying Semi-Supervised Machine Learning Classification to Anomaly Detection Exercises: The Case of Sensor Data

By Stany Nzobonimpa, Mohamed Abou Hamed, Ilia Korotkine, Tiffany Gao, Housing, Infrastructure and Communities Canada

Acknowledgements

The authors would like to acknowledge the following individuals for their contributions and support of the project: Kate Burnett-Isaacs, Director of Data Science and Matt Steeves, Senior Director of Engineering, Technical and Project Operations - Major Bridges and Projects.

Introduction

The identification and flagging of events such as data points that significantly deviate from standard or expected behaviour is known as anomaly detection in the field of data science. Anomalies are of special interest to data scientists because their presence signals underlying issues that prompt suspicions and usually warrants investigations. Various statistical techniques have historically been applied to answer anomaly-related questions and advances in Artificial Intelligence (AI) have made the exercise one of the most known applications of ML frameworks and approaches.

In this article, we present and discuss key findings of a project applying anomaly detection to sensor data. The project was carried out at HICC by a multi-disciplinary team of data scientists and engineers. While the project is multi-phased, this article reports on methods and results of the first phase in which sensor data from a federal bridge was used for early detection of anomalous readings.

The article is structured as follows: we start by a short review of relevant and recent literature on anomaly detection and its applications where we show that methods for data anomaly detection have evolved and continue to do so. After reviewing the literature, we present the problem, the methods, and results of this first phase of the project. In particular, we show that the approach enabled us to proactively, in a timelier and less laborious manner, flag anomalies in the generated sensor data that could have otherwise been missed. Indeed, our results show that for the period of April 2020 to September 2024, a number of sensor readings were successfully flagged as anomalies using the methods that we present in this paper. We conclude by stating the next steps of the project.

Literature Review

The application of machine learning to anomaly detection

Anomaly detection has long been a focus in data mining, which laid the groundwork for many modern ML approaches. Foundational works in data mining have explored anomaly detection techniques extensively, including clustering-based, distance-based, and density-based methods. Thus, anomaly detection is one of the core applications of ML technology that has been applied by AI practitioners for decades (Nassif et al., 2021). In its simplest form, anomaly detection is known as outlier detection and consists of flagging data points that significantly deviate from or do not conform with the majority of data.  The multiple applications of anomaly detection from fields as diverse as medical research, cybersecurity, financial fraud, and law enforcement have attracted both researchers and practitioners’ interests and resulted in a flourishing literature on the topic (Chandola et al. 2009).

Traditionally, anomaly detection could be completed manually by examining and filtering data and using context knowledge to provide insights on the detected behaviours. As datasets get larger, statistical approaches have been useful in detecting anomalies. For example, Soule et al., (2005) proposed a combination of filtering using Kalman filters and other statistical techniques to detect volume anomalies in large networks. The authors argue that anomaly detection can always be viewed as a problem of statistical hypothesis testing and they use behaviours, residual means, and variance changes over time to compare four different statistical approaches to anomaly detection (pp.333-338).

Using receiver operating characteristic (ROC) curves for binary normal vs anomaly illustrations, Soule et al., (2005, p. 334) argue that “ROC curves are grounded in statistical hypothesis testing” and that “[…] any anomaly detection method will at some point use a statistical test to verify whether or not a hypothesis (e.g., there was an anomaly) is true or false.” Another technique that has been applied to anomaly detection is what was called “robust statistics” by Rousseeuw & Hubert (2017). These authors revisited the techniques using fitting the majority of data and flagging anomalies once fitting normal data is completed. For example, the authors suggest estimating univariate locations and scales through means, medians, and standard deviations.

While the techniques such as those proposed by Soule et al. (2005) and Rousseeuw & Hubert (2017) for anomaly detection are popular and have been applied to various contexts and problems, the advent of advanced ML models and computing capabilities makes the exercise easier and scalable to large amounts of data. Additionally, some anomalies may be harder to detect and require more advanced techniques (Pang et al., 2021). These authors note that some unique complexities can prove more challenging and may require advanced approaches. They argue that anomaly detection is more problematic and complex because of its focus on minority data that are generally rare, diverse, unpredictable, and uncertain. For example, the authors note the following challenge related to the complexity of anomalies (Pang et al., 2021):

Most of existing methods are for point anomalies, which cannot be used for conditional anomaly and group anomaly, since they exhibit completely different behaviors from point anomalies. One main challenge here is to incorporate the concept of conditional/group anomalies into anomaly measures/models. Also, current methods mainly focus on detecting anomalies from single data sources, while many applications require the detection of anomalies with multiple heterogeneous data sources, e.g., multidimensional data, graph, image, text, and audio data. One main challenge is that some anomalies can be detected only when considering two or more data sources (p. 4).

Hence, the authors distinguish between what they call “traditional” anomaly detection and “deep” anomaly detection (p. 5) and argue that the latter is concerned with more complex situations and enables “end-to-end optimization of the whole anomaly detection pipeline, and they also enable the learning of representations specifically tailored for anomaly detection” (p. 5). Conditional anomaly detection, also known as contextual anomaly detection, refers to the method in where unusual values are identified in a subset of variables while taking into account the values of other variables. Group anomaly detection focuses on identifying anomalies within groups of data. Instead of looking at individual data points, this approach examines patterns and behaviors of groups to detect any deviations from the norm.

As noted by authors such as Chandola et al. (2009); Pang et al. (2021) and Nassif et al., (2021), among others, ML has made anomaly detection exercises more robust and seamless. Indeed, multiple techniques leveraging ML technologies have been suggested and applied to anomaly detection and have proved to be practical and handy in automating the process (Liu et al, 2015). For example, Nassif et al. (2021) split ML based anomaly detection into three broad categories: supervised anomaly detection where the process involves labelling a dataset and training a model to recognize anomalous points based on the labels, semi-supervised anomaly detection where the training set is only partly labelled and unsupervised anomaly detection where no training sets are needed, and a model automatically detects and flags abnormal patterns.

In this project, we leveraged the category of semi-supervised ML where sensor data are partly labelled. We show that this approach, combined with deep understanding of sensor readings, can achieve robust results, and presents the non-negligeable advantage of model and statistical parameter finetuning while keeping the automation power of machine learning.

Research Context: The Problem and Data Pipeline

The Problem

Physical structural health monitoring (SHM) sensors are installed at strategic locations along a given infrastructure such as a bridge or a dam to record its behaviour. Most sensors work by recording electronic signals, which are then converted into forces, movements, vibrations, inclinations, and other information that is critical in understanding the performance and the structural health of the infrastructure. These sensors send large amounts of data on an ongoing basis. In order for this SHM data to become useable by bridge engineers and operators for adequate asset management, the data must first be cleaned of anomalies such as outliers and noise.

Owner and operators of critical infrastructure perform regular due diligence activities to ensure the structural health of their assets. As a first step in identifying anomalies, data which exceeds predefined maximum or minimum thresholds, as set by the bridge designers, is identified as an outlier. However, Structural Health Monitoring System (SHMS) data is often more complex and requires further analysis to remove the outliers and noise from the data that falls within these predefined thresholds.

Prior to developing the ML approach, the Major Bridges and Projects (MBP) team had been conducting analyses which required manual anomaly detection of large amounts of sensor data which proved complex, time consuming, less effective, and prone to human error. The use of ML techniques has enabled the teams to test a faster and more accurate anomaly detection mechanism.

This project was conducted as a partnership between the Data Science (DS) team and the Major Bridges and Projects (MBP) team at HICC.

The general objective of the project is to improve the monitoring of the structural health of bridge infrastructure by outputting data with less noise. The specific objectives include detecting sensor data anomalies (counts) of various types such as those caused by irregular fluctuations or the lack of fluctuations and to start identifying sensor data anomaly trends. Additionally, the project aims to inform the decision-making process regarding the implementation of a new anomaly detection technology.

The Data Pipeline

The data pipeline of the project follows a typical data science process: the analytical team receives sensor readings, cleaning and pre-processing is done using python, training sets are created, labelled, and tested, machine learning models are applied using the scikit-learn library of Python and resulting predictions are visualized using MS Power BI. Figure 1 below summarizes the data pipeline for the project.

Data Pipeline
Description - Figure 1 : Data Pipeline

Figure 1 shows an image that illustrates the data pipeline for the project. It starts with input data, which includes Sensor readings, sensor IDs, date, time, and temperature. This data is ingested into a SharePoint Document Library. The next step in the pipeline is data cleaning and preprocessing, followed by machine learning. In the final step, the post-processed data is stored back in the SharePoint Library. The training set involves data labeling and train and test iterations. The entire project uses sensor data.

Methods

The project uses a semi-supervised ML classification approach similar to techniques discussed in our literature review section. This technique uses a combination of labelled and unlabelled data where we partially label a training set and apply statistical parameters in the anomaly detection process as described in the following sections. The choice of the method was motivated by multiple factors related to ensuring control over training data, the types of anomalies, cost-effectiveness, replicability, and performance, among others. Authors such as Soule et al. (2005) and Rousseeuw & Hubert (2017) proposed robust methods for using statistical techniques in anomaly detection and demonstrated the combination of such techniques with existing machine learning approaches yields robust results while the use of ML alone can generally miss hard-to-detect anomaly types (Spanos et al., 2019; Jasra et al., 2022). This indeed proved to be true in this project: when we applied an unsupervised ML model during the exploration phase, we realized that only outliers, i.e. data that were significantly above or below the centres, were being flagged as anomalies. Yet, we needed a method that can capture the anomalies that are not easily detected.

To successfully implement the approach, we partially labelled a sample set and trained a ML model to flag those anomalies. Prior to running the supervised model, we start by prepopulating anomaly predictions in an unsupervised and rule-based approach with pre-defined parameters for standard deviations and time series window sizes described in the following sections. With a partially labelled training set, ML classification is applied, and we chose the Gradient Boosting algorithm summarized by equations (1) and (2) below:

(1) y ^ = F K ( x )

(2) F K ( x ) = F 0 ( x ) + ∑ i = 1 K γ i ⁢ λ i ( x )

Where:

  • y ^ is the predicted binary data label for a normal or an abnormal sensor reading;
  • X is a set of predictors for time series data with sensor reading, date and time;
  • F( x ) is the function learned by the classifier from the training (labeled) data;
  • F 0 is a constant initial value for the target prediction;
  • K is the number of boosting stages, also called “weak learners”;
  • γ i are coefficients also known as the learning rate;
  • λ i are the weak learners or the decision trees

The expression in (1) represent the model’s predicted output y for input features x where F is the aggregated function combining all individual weak learners. The expression in (2) illustrates how the model is built iteratively over K boosting rounds: starting with an initial constant, each subsequent iteration adds a scaled weak learner to progressively minimize the residual errors.

It is important to note that the Gradient Boosting algorithm usually outperforms other ensemble learner models (Ebrahimi et al., 2019) and, in some instances, authors have found a gradient boosted decision tree was superior by a large margin to some neural models (Qin et al., 2021). Prior to employing the semi-supervised approach through the Gradient Boosting algorithm, we tested an Isolation Forest model as a fully unsupervised anomaly detection technique. Results of the Isolation Forests method did not meet our expectations in terms of accuracy, which is not surprising in such cases with conditional anomalies as shown by Pang et al. (2021) cited above.

This approach allowed full control over training data and gave room to possibilities of finetuning multiple parameters including model parameters such as testing and training sizes, random states as well as statistical parameters for standard deviations and time series window sizes. Indeed, we defined anomaly types by existing formulas defined by bridge engineers and by labelling training sets manually using an iterative process. For example, we were able to isolate the following type of anomalies called flatlines that were not flagged by the alarm system because they remained within the predefined thresholds previously mentioned and that would be time-consuming and not practical to identify manually:

  • Absolute flatline anomalies: when a sensor returns exactly the same reading four times consecutively with standard deviation equal to 0
  • Consecutive but relative flatline anomalies: when a sensor returns 12 consecutive readings in a day with very little fluctuation set to absolute standard deviation value inferior or equal to 0.15 

The use of this semi-supervised approach also ensured that our method could be applied to similar problems without the need to rewrite the algorithm, hence ensuring cost effectiveness and replicability. Finally, the choice of this method was also motivated by the well documented compute efficiencies and performances for supervised machine learning models (Akinsola et al., 2019; Aboueata et al., 2019; Ma et al., 1999). It is worth noting that while the results were satisfactory, this approach had some limitations and challenges. In particular, the manual labelling of training data required significant efforts. Additionally, the combination of techniques, while allowing flexibility, had a risk for model overfitting which we are mitigating by carefully inspecting the predicted results with analysts’ knowledge.

Results and Discussion

In its first phase, the project led to the identification of 714,185 data readings flagged as anomalies. This represented roughly 4.6% of all sensor readings and fell in one of the following pre-determined categories:

  • Anomalies caused by irregular fluctuations
  • Anomalies related to the lack of normal fluctuations including absolute and relative flatlines
  • Anomalies caused by readings being outside of the expected range for sensors

The proportion of data points identified as anomalies was over 4% larger than what a non-supervised Isolation Forest model had returned in an earlier exploration, confirming the conclusions by Pang et al. (2021) that more complex anomaly types require more advanced combination of methods for accurate detection. We also obtained an average internal accuracy score of 0.988 (or over 98%), which, while not an absolute indication of success, was a signal for the model’s performance.

Predictive Accuracy

During the proof-of-concept phase of this project, we computed an F1 score for model accuracy and obtained a score of 0.956 (96%) accuracy. The F1 score is the harmonic mean of a model's precision and recall scores and is often used to evaluate classification models (for example, see Silva et al., 2024; Zhang et al. 2015). In the following section, we report on the internal model accuracy computed using the cross-validation approach from the scikit-learn library. To avoid overfitting, we held on to a small sample of data as a test set and computed the accuracy score using the `train_test_split` method from scikit-learn. The method splits datasets into training and testing subsets for ML tasks. It has various parameters such as the ‘test_size’ and ‘train_size’ which determine, randomly or in controlled manner, the fraction of number of samples for the test and train set. This approach allows better control, especially in the cases of imbalanced training data.

This process is well documented by scikit-learn as shown in figure 2 below:

Splitting training and test set
Figure 2: Splitting training and test set

Figure 2 shows an image that was taken from the Scikit-Learn Library to visually represents the process of dividing a dataset into training sets and test sets. The training set is used to train a machine learning model, while the test set is used to evaluate the model's performance. The image displays a diagram showing how the data is split, with arrows indicating the separation between the various elements of the train-test split process. Those elements are linked by arrows as follows: parameters, cross-validation, Best parameters, Dataset, Training data, Retrained model, Test data and Final Evaluation.

Table 1 below shows accuracy scores obtained for a sample of 9 sensors out of 44 sensors in the scope of the first phase of the project. This score is calculated by dividing the number of correct predictions by the number of total predictions and thus allows comparison of the predicted values with the actual values. The score was computed using the y_val_split, y_val_pred methods from the sklearn.metrics sub-library. It is worth noting that the exercise of partially labelling training data was done on a full cycle of the sensor readings, covering a sample year. This helped overcome the problem of imbalanced training set and resulted in comparable shares of anomalies in training data and anomalies identified.

Table 1: Sample accuracy scores for the first 9 sensors using the test-split helper function
Sensor ID Accuracy score
Sensor ID 1 0.998463902
Sensor ID 2 0.998932764
Sensor ID 3 0.990174672
Sensor ID 4 0.98579235
Sensor ID 5 0.99592668
Sensor ID 6 0.994401679
Sensor ID 7 0.998294486
Sensor ID 8 0.998385361
Sensor ID 9 0.99452954

While this score served as a good benchmark, it is worth noting that because of the specificity of anomalies being detected, more accuracy scoring metrics are being researched to ensure the best representation of the model’s performance.

Using the results: From Machine Learning to Business Value

The predicted anomalies need to be consumable to the bridge engineers so they can action on the insights produced by the ML model. The solution was to visualize the output in a simple and easy dashboard that summarizes the findings while providing enough information that can inform decision-making. We used Microsoft’s Power BI to produce a simplistic visualization that contain the following information:

  1. Anomaly percentage by day using a stacked column chart, which can be drilled up to anomaly percentage by month
  2. The sensor readings using a line chart and differentiating between normal readings and anomalies
  3. A table with the detailed results provided by the algorithm for the selected sensor and year/month.
  4. The performance indicator for the selected sensor for the selected time range, which is the percentage of the number of normal readings over the total number of readings
  5. The share of normal sensor readings vs anomalies (donut chart) and a filter for comparison

Anomaly percentage by day using a stacked column chart, which can be drilled up to anomaly percentage by month

This type of dashboard is used to determined how well a specific sensor is performing and if it needs calibration, repair, or replacement. It provides limited visibility on the behaviour of the structure that the sensor is monitoring.

A snapshot of the dashboard showing the above is provided in Figure 3 below.

Results Visualization (1)
Figure 3: Results Visualization (1)

Figure 3 is an image that shows a visual representation of the results from the project. The image is divided into several sections each labelled with letters A through F. Section A displays a line chart with multiple lines representing different sensors. The lines show the sensor readings over time, with anomalies removed from the visual. Section B shows the correlation between the sensor readings and the inverse of temperature, and how temperature fluctuations impact the sensor data. Section C shows the offsets between the displacement lines removed so they could overlap to provide a clearer comparison of the sensor data. Section D presents the monthly averages of the sensor readings and gives an overview of the data trends over time. Section E includes performance indicators for all the selected sensors and shows the accuracy and reliability of the sensor readings. Section F provides additional details and insights about the sensor data and the detected anomalies.

We also ensured the results can be visualized together for all sensors which allowed comparison and readability. The three figures below show the steps performed in order to attain this comparison and readability.

Unlike the previous dashboard, this type of dashboard, once anomalies such as noise and outliers are removed, allows for better visibility of the trends and the behaviour of the structures that the sensors are monitoring, as opposed to the behaviour of the sensors themselves.

Figure 4 shows the initial raw data of multiple sensors on the same graph, averaged monthly to show the results over extended periods of time (i.e. trend evaluation), along with temperature, which is distinguished by being dashed, as temperature is the main influencing reading predictor.

Results Visualization (2) – Initial Data
Figure 4: Results Visualization (2) – Initial Data

Figure 4 is an image with a multiple-lines graph showing the trends and distribution of the initial data. The lines have different colors, each representing a different sensor over time. A dotted line shows temperature readings. The time is displayed by year and by month starting January 2021 and ending in December 2023. The lines representing sensors are scattered throughout the time axis.

In Figure 5, the offsets between the sensor data lines are removed to ensure overlapping, and temperature is inverted so that it can also follow the upward and downward trend of the sensor data lines.

Results Visualization (2) – Initial Data with Improved Visuals
Figure 5: Results Visualization (2) – Initial Data with Improved Visuals

Figure 5 is an image that has a multiple-lines graph showing the trends and distribution of the initial data with improvement. The lines have different colors, each representing a different sensor over time. A dotted line shows temperature readings. The time is displayed by year and by month starting January 2021 and ending in December 2023. The lines representing sensors are less scattered throughout the time axis compared to the previous image with initial results.

Finally, Figure 6. displays the final results with the removal of anomalies detected by our model. Once more, the overlap of temperature with the sensor data allows to qualitatively evaluate the behaviour of the structures at a glance.

Results Visualization (2) – Final Results
Figure 6: Results Visualization (2) – Final Results

Figure 6 is an image that has a multiple-lines graph showing the trends and distribution of the initial data with improvement. The lines have different colors, each representing a different sensor over time. A dotted line shows temperature readings. The time is displayed by year and by month starting January 2021 and ending in December 2023. The lines representing sensors are not scattered but show similar trends throughout the time axis compared to the previous two images.

The results obtained in this project have demonstrated the potential for using ML approaches to improve the anomaly detection exercise and to ensure a more efficient and accurate monitoring of sensor data. Indeed, the project has contributed to understanding historical data on bridge sensors and provided insights on the overall performance of the sensors. Moreover, the results assisted the Bridge Team in evaluating the performance of sensors, ultimately flagging a number of them for further analysis. Once the sensors are investigated and their functionality confirmed, the now noise-less data allows for a better appreciation of the behaviour of the structure, its performance trends, and its structural health over time.

Our approach has proven to have the potential to assist during the bridge operation, maintenance, and rehabilitation phase by helping to contribute towards early detection and inspection planning for infrastructure. Additionally, by exploring and manipulating historical data, the project has contributed to the overall objective of bridge health monitoring by checking data quality and reliability. The data quality checks that come with anomaly detection will help improve the accuracy of historical bridge SHMS data and facilitate the planning and management of future work. The control over anomaly types reduces the occurrence of false alarms while opening avenue for detailed inspections and checks. The resulting visualization will also be handy for the technical team and will provide insights on the performance of the sensors, as well as potential structural issues.

Conclusion and Next Steps

This project sought to leverage advanced techniques combining semi-supervised ML and statistical approaches for anomaly detection. As demonstrated in the literature section, anomaly detection is a complex problem that requires expertise and is usually well achieved by combining a host of methods. Indeed, we have shown that the exercise has the potential to flag sensor data anomalies in a controlled and replicable manner. In the next phases of the project, a multi-class model is being researched to ensure that not only anomalies are detected but they are also labelled and clustered into specific anomaly types. This granular approach will improve diagnostic clarity and help engineers to better understand sensor anomaly causes and ultimately support bridge maintenance.

It is worth noting, in this concluding paragraph, that with the advent of Generative AI (GenAI), future research should explore its potential for anomaly detection. GenAI has the ability to learn patterns and distributions of normal data and therefore could, if provided with examples, identify deviations that may signify anomalous data readings. Additionally, by training models like Generative Adversarial Networks (GANs) on large datasets, researchers can generate realistic representations of typical behavior and enhance anomaly detection exercises. The use of GenAI was outside of the scope of this project.

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References

A Data Story on Immigration and Climate Change – St. John's

A data story. A discussion with Statistics Canada.

Presentations

Presentation #1: A data dive into important trends and impacts of immigration on Newfoundland and Labrador

9:30 am-11:30 am NST

Just like Canada, Newfoundland and Labrador experienced some recent shifts in its demographics, notably in the number of incoming permanent and temporary immigrants, and in interprovincial migrations. On top of these shifts, Newfoundland and Labrador is also experiencing population aging, changes in ethnocultural and linguistic diversity, and increasing regional differences, notably between urban and rural areas.

The Chief Statistician of Canada will present a statistical portrait that will describe these recent shifts and trends, will put them in perspective compared to national and other provincial trends, and will also discuss some possible future trends on the basis of population projections. There will be a panel of experts who will present and engage the audience in the discussion on social and economic implications.

Presentation #2: Can data save lives in response to climate change and natural disasters?

1:00 pm-3:00 pm NST

Newfoundland is no stranger to extreme weather events and natural disasters. How can data provide actionable insights to understand the potential impact of natural disasters on the province’s people and its economy? The Chief Statistician of Canada will present a statistical portrait that will draw on recent events to demonstrate how data can inform the impact on the economy and will show how data can be used to understand the profiles of communities in order to prepare, respond and recover from natural disasters or emergency events.

In partnership with Memorial University’s Harris Centre and RAnLab, an expert panel discussion and question period will follow immediately after the presentations. You are encouraged to provide your input on these very important topics.

We sincerely hope you will join us in what will undoubtedly be thought-provoking and fruitful discussions.

General information

When: Wednesday, January 24, 2024 – 9:30 am to 3:00 pm NST

Where: Emera Innovation Exchange, Signal Hill Campus, Memorial University
100 Signal Hill Road, St. John's NL A1A 1B3

Cost: Free

If you are registered and your plans have changed and you can no longer attend, please cancel your registration.

Presenter

Anil Arora, Chief Statistician of Canada, Statistics Canada

Emcees

  • Samuel Dupéré, Data Service Centre Manager (Eastern), Statistics Canada
  • Jamie Ward, Manager, Regional Analytics Lab (RAnLab), Harris Centre, Emera Innovation Exchange, Signal Hill Campus, Memorial University

Moderator

Tony Labillois, Director General, Justice, Diversity and Population Statistics Branch, Social, Health and Labour Statistics Field, Statistics Canada

Panellists

Presentation #1

  • Welcoming remarks, Neil Bose, President, Memorial University of Newfoundland
  • Tony Fang, Professor and Stephen Jarislowsky Chair in Economic and Cultural Transformation, Memorial University of Newfoundland
  • Jim Murphy, Director of Employment Services, Association for New Canadians (ANC)
  • Constanza Safatle, Entrepreneur and advocate, Newbornlander

Presentation #2

  • Welcoming remarks, Lisa Browne, Vice-President (Administration, Finance and Advancement), Memorial University of Newfoundland
  • Emma Power, Consultant, Fundamental Inc.
  • Joel Finnis, Climatologist, Department of Geography, Memorial University of Newfoundland
  • Willa Neilsen, Labrador Climate Services Specialist, CLIMAtlantic

Please note that you will receive a confirmation email after registering.

Simultaneous interpretation (English, French) will be available during the event.

Registration

Registration is now closed.

Wholesale Trade Survey (monthly): CVs for total sales by geography - February 2025

Wholesale Trade Survey (monthly): CVs for total sales by geography - February 2025
Geography Month
202402 202403 202404 202405 202406 202407 202408 202409 202410 202411 202412 202501 202502
percentage
Canada 0.8 1.0 0.4 0.4 0.4 0.4 0.8 0.8 0.8 0.9 0.8 0.9 1.0
Newfoundland and Labrador 0.9 1.1 1.3 1.0 0.5 0.4 0.5 0.6 0.9 1.0 0.7 1.0 0.6
Prince Edward Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nova Scotia 2.7 2.7 3.0 5.2 4.2 2.8 5.3 3.1 3.9 6.6 8.0 5.0 2.4
New Brunswick 1.6 2.1 1.8 0.5 0.7 1.0 1.8 1.3 2.2 1.6 1.6 2.3 0.9
Quebec 3.2 4.5 2.0 1.9 1.5 1.8 2.4 3.2 2.9 3.2 3.1 3.2 3.5
Ontario 1.7 1.8 0.8 0.8 0.8 0.7 1.7 1.6 1.4 1.5 1.6 1.9 2.0
Manitoba 0.8 1.0 0.7 0.8 0.5 0.6 1.2 1.5 1.7 1.3 1.5 0.6 0.7
Saskatchewan 1.2 1.0 0.7 0.2 0.3 0.7 1.2 0.5 1.0 0.6 0.9 1.3 0.5
Alberta 0.7 0.7 0.2 0.3 0.4 0.5 1.0 0.8 1.2 1.6 0.8 1.0 0.8
British Columbia 1.8 1.9 0.9 1.0 1.3 1.1 1.9 2.1 2.0 1.8 1.5 1.8 1.9
Yukon Territory 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Northwest Territories 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nunavut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Survey on the Use of Digital Technologies by Health Care Providers, 2025 (SUDTHCP)

Getting started

Why are we conducting this survey?

The Survey on the Use of Digital Technologies by Health Care Providers (SUDTHCP) collects data from health care providers on their use of digital health systems, including whether they send and receive patient clinical information electronically and barriers to using digital health tools. The SUDTHCP also collects information on health care providers’ knowledge of digital health systems to measure their digital health literacy.

Results from this survey will be used to identify areas of improvement in modernizing the health care system and improving health care services for Canadians as set out in the Working Together to Improve Health Care for Canadians plan.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Although voluntary, your participation is important so that the information collected is as accurate and complete as possible.

Other important information

Authorization and confidentiality

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19. Your information will be kept strictly confidential.

Record linkages

To enhance the data from this survey and to reduce the response burden, Statistics Canada may combine the information you provide with other survey or administrative data sources.

Contact us if you have any questions or concerns about record linkage:

Email: infostats@statcan.gc.ca

Telephone: 1-877-949-9492

Mail:
Chief Statistician of Canada
Statistics Canada
Attention of Director, Centre for Population Health Data
150 Tunney’s Pasture Driveway
Ottawa, Ontario K1A 0T6

Demographics

Please confirm your first and last name.
Note: Press the help button (?) for additional information.

  • First name
  • Last name

What is [your] date of birth?

  • Year
    • Min: 1897; Max: 2018
  • Month
    • January
    • February
    • March
    • April
    • May
    • June
    • July
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  • Day
    • 1
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    • 31

What is [your] age?

  • Age in years

What is [your] gender?

Gender refers to current gender which may be different from sex assigned at birth and may be different from what is indicated on legal documents.

Is it:

  • Man
  • Woman
  • Or please specify in your own words
    • Specify your gender

Please verify that all of the information is correct.

If all the information is correct, press the Next button.
To make changes, press the Previous button.

Your information
Sex assigned at birth: [Male/Female/Information not provided]
Gender: [Male/Female/ Other specified gender/Information not provided]

Employment

Were you a health care worker in Canada at any time between [January 1 and December 31, 2024]

Select "Yes" if you worked at least one day between [January 1 and December 31, 2024]:
* for pay (wages, salary, etc.)
* in self-employment.

Select "No" if you:
* were away from work for the entire period for retirement, sick leave, parental leave, training, sabbatical, etc.
* did not have a job and were not self-employed

  • Yes
  • No

What was your occupation during the period between [January 1 and December 31, 2024]?

If you had multiple occupations, select the occupation in health care for which you worked for the most time between [January 1 and December 31, 2024].

  • Nurse practitioner
  • Registered nurse, registered psychiatric nurse, or nursing coordinator or supervisor
    e.g., nurse clinician, public health nurse, clinical nurse specialist, etc.
  • Physician: family physician or general practitioner
    Include residents in training to become a family physician or a general practitioner; or family physicians with advanced training
  • Physician: specialist
    Include residents in training to become a specialist physician
  • Pharmacist
  • None of the above

What is your specialty?

  • Specify your medical specialty

As [a nurse practitioner/a nurse/a general practitioner or family physician/a specialist physician/a pharmacist], did you provide direct patient care at any time between [January 1 and December 31, 2024]?

Direct patient care is to have physical or virtual contact with patients for the purpose of prevention, assessment, diagnosis, monitoring of their physical and mental health and treatment, including dispensing medication.

  • Yes
  • No

Job environment

The following questions refer to your experiences as [a nurse practitioner/a nurse/a general practitioner or family physician/a specialist physician/a pharmacist] at your primary practice setting between [January 1 and December 31, 2024]. Your primary practice setting is the specific health care facility where you worked the most hours between [January 1 and December 31, 2024].

Between [January 1 and December 31, 2024], what was your primary practice setting?

* if you worked at multiple settings as [a nurse practitioner/a nurse/a general practitioner or family physician/a specialist physician/a pharmacist], please think of the setting at which you worked the most hours and provided patient care

* if you practiced at multiple settings equally, please think of the practice setting you provided patient care on the last working day you had

* if you worked virtually for most of your practice, select the corresponding virtual practice.

  • Primary care or family medicine office or clinic
    Include nurse practitioner-led office or clinic
  • Specialist office or clinic
    Exclude offices or clinics located in a hospital
  • Hospital setting
    Include inpatient care or ambulatory clinics in a hospital or specialized medical or health facility that has in-patient care, e.g., rehabilitation centre, mental health or addictions treatment centre, cancer centre or birth centre
  • Community or public health centre, clinic or unit
    Include centres that provide primary health or health promotion services to individuals, families or communities, such as community health centres or local community service centres (CLSC)
  • Nursing home, long-term care facility, seniors' residence or retirement home, or special care home
  • Homecare establishment
    Include establishments for home-based nursing care or home-based palliative care
  • Laboratory or diagnostic clinic
  • Pharmacy
  • Other

What was the province or territory of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?

If you worked in a fully virtual setting, select the province or territory in which you provided care.

  • Alberta
  • British Columbia
  • Manitoba
  • New Brunswick
  • Newfoundland and Labrador
  • Northwest Territories
  • Nova Scotia
  • Nunavut
  • Ontario
  • Prince Edward Island
  • Quebec
  • Saskatchewan
  • Yukon

What was the postal code of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?

Refer to your primary practice setting's postal code.

  • Postal code
    Example: A9A 9A9

Patient clinical information

The following questions refer to your experiences working as [a nurse practitioner/a nurse/a general practitioner or family physician/a specialist physician/a pharmacist] at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting].

Patient clinical information could include prescriptions, consultation and clinic notes, hospital discharge summaries, medication summaries, allergies and intolerances, immunizations, procedures or surgical history, medical devices, lab and diagnostic results and requisitions, referrals, vital signs and other types of clinical information.

Between [January 1 and December 31, 2024], did you have access to any patient clinical information at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?

  • Yes
  • No

Sharing patient information

The following questions refer to your experiences working with digital health systems at the [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting] between [January 1 and December 31, 2024]. Digital health systems are internal or external applications, platforms or software that allow for documenting, sending or sharing patient clinical information. Some examples of digital health systems include Electronic Medical Records (EMR), provincial or territorial Electronic Health Records (EHR), Hospital Information Systems (HIS), Clinical Information Systems (CIS) and Pharmacy Management Software (PMS), among others. Specific examples may include MEDITECH, Epic, Cerner, CristalNet, OACIS, ClinicalViewer, Dossier Santé Québec (DSQ), Netcare, etc. For the purposes of this survey, faxes, electronic faxes (e-fax), standard email, video conferencing platforms or telephones are not considered digital health systems.

Between [January 1 and December 31, 2024], did you have access to one or more digital health systems at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?
Note: Press the help button (?) for more information on digital health systems.

  • Yes
  • No

The following question is about the use of a digital health system between [January 1 and December 31, 2024].

Which of the following methods did you use to send or share patient clinical information with health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?

Note: Press the help button (?) for more information on digital health systems and patient clinical information.

Select all that apply.

  • Entering or updating patient clinical information in a system that allows access by health care providers
  • Flagging or sending a notification that patient clinical information is available to health care providers
  • Sending or sharing patient clinical information through a digital health system via secure email or secure messaging
  • Sending or sharing patient clinical information through a digital health system via fax or e-fax
    OR
  • I was not able to send or share patient clinical information using the methods above outside of my primary practice setting
    OR
  • I did not need to send or share patient clinical information outside of my primary practice setting

For the following questions, only consider sending or sharing patient clinical information electronically through a digital health system. Please exclude fax or e-fax even if sent through a digital health system.

Between [January 1 and December 31, 2024], how often did you use a digital health system when sending or sharing patient clinical information to health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?
Note: Press the help button (?) for more information on digital health systems and patient clinical information.
Exclude fax or e-fax even if sent through a digital health system.

  • Always
  • Often
  • Sometimes
  • Rarely

To which of the following health care settings did you use a digital health system as the primary method of sending or sharing patient clinical information?
Exclude fax or e-fax even if sent through a digital health system.
Note: Press the help button (?) for more information on digital health systems and patient clinical information.

Select all that apply.

  • Primary care or family medicine office or clinic
    Include nurse practitioner-led office or clinic
  • Specialist office or clinic
    Exclude offices or clinics located in the hospital
  • Public health centre, clinic, or unit
    Include centres that provide primary health or health promotion services to individuals, families, or communities, such as community health centres or local community service centres (CLSC)
  • Nursing home, long-term care facility, seniors' residence or retirement home, or special care home
  • Homecare establishment
    Include establishments for home-based nursing care or home-based palliative care
  • Laboratory or diagnostic clinic
  • Pharmacy
  • Hospital setting
    Include ambulatory clinics in a hospital or specialized medical or health facility that has in-patient care, e.g., rehabilitation centre, mental health or addictions treatment centre, cancer centre or birth centre
  • Other
    OR
  • Did not use a digital health system as the primary method of sending or sharing patient clinical information

Between [January 1 and December 31, 2024], which of the following barriers did you experience that limited or prevented you from sending or sharing patient clinical information to health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting] using digital health systems?

Select all that apply.

  • Digital health systems did not have the ability to send or share patient clinical information electronically to health care providers that work outside of my practice setting
    e.g., digital health system can only share using fax or e-fax
  • Insufficient training on use of digital health systems
  • Reluctant to share patient clinical information because of privacy concerns
  • Available digital health systems did not meet my clinical practice needs
  • Insufficient integration between different digital health systems
    e.g., cannot send information directly from one digital health system to another
  • Required equipment was not available
    e.g., workstations, mobile devices
  • Lacked the required permissions to access digital heath systems
  • Time-consuming or multiple sign-ins required to access digital health systems
  • Non-user-friendly digital health systems
    e.g., complicated interface, very complex system
  • Systems or networks weren't available or reliable
    e.g., data disappearing or unstable internet connections
  • Insufficient IT support to use the digital health system
  • Other
    • Please specify the other barrier to sharing information
    OR
  • Did not experience any barriers to sending or sharing patient information using digital health systems

Using patient information

The following questions are about patient clinical information that you have received or is available to you through a digital health system from other health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]. This may include internal or external systems to which you have access.

Which of the following methods did you receive or access the patient clinical information through a digital health system?
Note: Press the help button (?) for more information on digital health systems and patient clinical information.

Select all that apply.

  • Accessing patient clinical information that has been entered or updated in a system by health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]
  • Receiving a flag or a notification that patient clinical information is available by health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting] for the care of your patient
  • Receiving patient clinical information via secure email or secure messaging through a digital health system from health care providers that work outside of your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]
  • Receiving patient clinical information via fax or e-fax that is scanned or copied into your digital health system
    OR
  • Did not receive or access patient clinical information through any of these methods

For the following question, only consider patient clinical information that you have received or is available to you directly through a digital health system. Please exclude if patient clinical information was received via fax or e-fax that was scanned or copied into your digital health system.

Thinking about patient clinical information available to you from other health care providers through a digital health system, were there any times that you did not access the information due to the digital health system limitations?

Consider limitations in the digital health systems that may have limited the frequency in which you accessed this information. For example, time-consuming or multiple sign-ins required to access systems, insufficient integration between different systems, non-user-friendly systems, etc.
Note: Press the help button (?) for more information on digital health systems and patient clinical information.

  • Yes
  • No

How often did you not access the information due to limitations?

  • Always
  • Often
  • Sometimes
  • Rarely

Between [January 1 and December 31, 2024], which of the following barriers limited or prevented you from accessing patient clinical information you received through a digital health system?

Select all that apply.

  • Insufficient training on use of digital health systems
  • Insufficient integration between different systems
    e.g., I could not open the patient clinical information delivered to my digital health system
  • Required equipment was not available
    e.g., workstations, mobile devices
  • Lacked the required permissions to access digital health systems
  • Time-consuming or multiple sign-ins required to access systems
  • Non-user-friendly systems
    e.g., complicated interface, very complex system
  • Systems or networks weren't available or reliable
    e.g., data disappearing or unstable internet connections
  • Insufficient IT support to use the system
  • Other
    • Please specify the other barrier for accessing patient clinical information you received or had access to
    OR
  • Did not experience any barriers accessing patient clinical information received through a digital health system

Digital health literacy

The following questions are about your eHealth or digital health skills and experiences, such as the use of digital health technology and other technologies related to health that involve patient care. Answer the questions based on your experiences with digital health technology during working days between [January 1 and December 31, 2024] at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting].

Questions are part of the e-Health Literacy Questionnaire (eHLQ Staff version). The eHLQ is copyrighted, access and use of the questions are only available with permission from the authors. Copyright © 2018 Richard Osborne (rosborne@swin.edu.au) and Lars Kayser (lk@sund.ku.dk)

Thinking about digital health technology please indicate how strongly you agree or disagree with each of the following statements.

Please respond to the following statements based on your experiences with IT systems between [January 1 and December 31, 2024] at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting].

Technology represents all devices that can be used to access data or handle information digitally. Technology is also the digital services offered by the health care system. Examples are internet, website, health apps, sensors, monitoring machines, computers, laptops, mobile phones, smart phones, tablets and smart watches.

For the following questions digital health systems means IT systems that you encounter during the working day. Examples are online health records, health apps, Medicare website and apps, private health insurance websites, health care provider's website and prescription apps.

Services are any type of care provided to patient with the purpose of prevention, assessment, diagnosis, treatment and monitoring of their physical and mental health.

  1. I am sure that the patients' health data are being used only by those who are supposed to use it
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  2. Technology makes the patients feel actively involved with their health
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  3. Information about the patients' health are always available to those who need it
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  4. At my workplace, services are available that I can access through technology
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  5. The patients' electronic health care data are being stored safely
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  6. I have a clear understanding of how health care providers use data
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  7. The patients' health data are available to me wherever I am
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  8. I find that technology helps me to assist the patients in taking care of their health
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  9. I am sure that only authorized people can access the patients' health data
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  10. All the digital health systems I use work together
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  11. I find that patients receive better services from health professionals when technology is used
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  12. Technology improves my communication with the patients
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  13. Most of the health care providers can be accessed through technology
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  14. I am confident that health care providers use the patients' data appropriately
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  15. I have access to digital health systems that work
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree
  16. I find technology useful for monitoring the patients' health
    • Strongly disagree
    • Disagree
    • Agree
    • Strongly agree

Province

In which province or territory were you living during the majority of your time working as a health care provider between [January 1 and December 31, 2024]?

Consider the place you lived in for the longest period of time between [January 1 and December 31, 2024]?

  • Alberta
  • British Columbia
  • Manitoba
  • New Brunswick
  • Newfoundland and Labrador
  • Northwest Territories
  • Nova Scotia
  • Nunavut
  • Ontario
  • Prince Edward Island
  • Quebec
  • Saskatchewan
  • Yukon

Labour market activities

Were you an employee or self-employed between [January 1 and December 31, 2024] at your [office or clinic/hospital/center or clinic/residential care home/homecare establishment/laboratory or diagnostic clinic/pharmacy/center or clinic/primary practice setting]?

Select "Employee" if you worked:
* for pay (wages, salary, etc.).
Select "Self-Employed" if you worked:
* for your own business or professional practice
* as an independent contractor, health care provider, etc.

  • Employee
  • Self-employed

How many years have you been working as [a nurse practitioner/a nurse/a general practitioner or family physician/a specialist physician/a pharmacist]?

Include years of residency if applicable.

Number of years

  • Less than 1 year
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20 or more

Administrative information

To avoid duplication of surveys, Statistics Canada has signed agreements to share the data from this survey with provincial and territorial ministries of health. Provincial ministries of health may make the data available to local health authorities. Data shared with your ministry of health may also include identifiers such as name, address and telephone number. Local health authorities, would receive only survey responses and the postal code. These organizations have agreed to keep the data confidential and use it only for statistical purposes.

To avoid duplication of surveys, Statistics Canada has signed agreements to share the data from this survey with provincial and territorial ministries of health and the Institut de la statistique du Québec. The Institut de la statistique du Québec and provincial ministries of health may make this data available to local health authorities. Data shared with your ministry of health or the Institut de la statistique du Québec may also include identifiers such as name, address and telephone number. Local health authorities, would receive only survey responses and the postal code.

These organizations have agreed to keep the data confidential and use it only for statistical purposes.

Do you agree to share the data that you have provided?

  • Yes
  • No

Reporting Guide - 2024 Annual Civil Aviation Survey - Level II

Integrated Business Statistics Program (IBSP)

Reporting Guide

This guide is designed to assist you as you complete the 2024 Annual Civil Aviation Survey - Level II. If you need more information, please call the Statistics Canada Help Line at the number below.

Help Line: 1-877-949-9492

Your answers are confidential.

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act.

Statistics Canada will use information from this survey for statistical purposes.

Table of contents

Reporting period information

For the purpose of this survey, please report information for your 12 month fiscal period for which the final day occurs on or between April 1, 2024 — March 31, 2025.

Here are twelve common fiscal periods that fall within the targeted dates:

  • May 1, 2023 to April 30, 2024
  • June 1, 2023 to May 31, 2024
  • July 1, 2023 to June 30, 2024
  • August 1, 2023 to July 31, 2024
  • September 1, 2023 to August 31, 2024
  • October 1, 2023 to September 30, 2024
  • November 1, 2023 to October 31, 2024
  • December 1, 2023 to November 30, 2024
  • January 1, 2024 to December 31, 2024
  • February 1, 2024 to January 31, 2025
  • March 1, 2024 to February 28, 2025
  • April 1, 2024 to March 31, 2025

Here are other examples of fiscal periods that fall within the required dates:

  • September 18, 2023 to September 15, 2024 (e.g., floating year-end)
  • June 1, 2024 to December 31, 2024 (e.g., a newly opened business)

Business or organization and contact information

This section verifies or requests basic identifying information of the business or organization such as legal name, operating name (if applicable), contact information of the designated contact person, current operational status, and main activity(ies).

1. Legal name and Operating name

Legal Name
The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

2. Designated contact person

Verify or provide the requested contact information of the designated business or organization contact person. The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire. If different than the designated contact person, the contact information of the person completing the questionnaire can be indicated later in the questionnaire.

3. Current operational status

Verify or provide the current operational status of the business or organization identified by the legal and operating name in question 1. If indicating the operational status of the business or organization is 'Not currently operational' then indicate an applicable reason and provide the requested information.

4. Main activity

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classes; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

If the current NAICS associated with this business or organizations is not correct, please provide a brief description of the main activity and provide any additional information as requested.

Balance Sheet, Annual - Statement 20 (II, III)

Financial assets

Current assets

Include:

  • cash, bank balances (including deposits in transit, special deposits for the payments of debts, and so on) and short-term investments due within one year from the date of the balance sheet;
  • current accounts and notes receivable as well as other current assets such as inventories, charges to subscribers on transportation contracts, interests and dividends receivable, and so on.

All other financial assets – (Include investments and special funds.)

Include investments in associated companies, other investments such as investments in stocks, bonds, and so on, and special funds such as equipment purchase funds, funds set aside for such special purposes as contractual deposits, pension funds, self-insurance funds, and so on.

Property and equipment

Operating - property and equipment – (Include capital leases.)

Include:

  • ground property and equipment (including flight equipment) owned and/or under capital leases;
  • the cost of aircraft (airframes), aircraft engines, propellers, components (aircraft communication and navigational equipment) and spare parts that have been purchased outright;
  • the cost of non-airborne communication and meteorological equipment, ramp equipment, maintenance and engineering equipment, surface transport vehicles and equipment, furniture, fixtures and office equipment, buildings and land as well as miscellaneous ground equipment such as medical equipment, airport and lighting equipment, passenger service equipment, hotel, restaurant and food service equipment, storage and distribution equipment. Property and equipment under capital leases includes the cost of property and equipment under a capital or finance lease, in other words, a lease for a period considered to be the whole or nearly the whole life of the property or equipment.

Accumulated depreciation and amortization - property and equipment

Include:

  • accumulated depreciation and amortization of ground property and equipment (including flight equipment) owned and/or under capital leases;
  • accrued charges representing losses, not replaced by current repairs, occurring in physical property and suffered through current lessening of service value due to wear and tear from use and the action of time and the elements; and losses occurring through obsolescence, supersession, new technological developments, changes in popular demand and the requirements of public authority.

Non-operating property and equipment – (Include capital leases.)

Include the cost of all non-operating property and equipment, in other words, all property and equipment not included in the "operating" category above.

Accumulated depreciation and amortization - non-operating property and equipment

Include accumulated depreciation and amortization of the non-operating property and equipment.

All other assets

Include long-term prepayments, developmental and pre-operating costs such as the cost of extraordinary training, unamortized discounts and expenses on the issue of long-term debt securities, property acquisition adjustments, other intangibles such as payments made for patents, copyrights, and so on, and other deferred charges.

Total assets

The sum of the assets above less the accumulated depreciation and amortization.

Liabilities and capital

Current liabilities

Include:

  • current accounts and traffic balances payable, including balances subject to current settlement and payable to associated companies and/or shareholders, and notes payable on demand or within one year from the date of the balance sheet;
  • the current portion of long-term debt and the current obligations under capital leases;
  • air traffic liabilities (unearned transportation revenue), which includes the value of passenger tickets sold but not used or refunded as of the date of the balance sheet, and pre-paid amounts for the transportation of baggage, freight and mail for which the transportation has not occurred as of the date of the balance sheet;
  • salaries and wages accrued and unpaid, taxes accrued and unpaid, dividends payable, deposits by subscribers on transportation contracts (air travel plan liabilities, in other words, deposits received under air travel plan contracts) and other current and accrued liabilities.

Advances from associated companies and/or shareholders

Include the net amount from associated companies and/or shareholders for notes, loans or advances which are not currently settled.

Long-term debt and other non-current liabilities – (Include capital leases.)

Include:

  • the face value or principal amount of debt securities (for example, bonds, trust certificates, debentures, notes) issued and assumed by the air carrier and in the hands of others, which is not payable within twelve months of the balance sheet date;
  • long-term obligations under capital leases, which refers to the present value of unexpired contracts for the acquisition of aircraft under such lease arrangements.

Deferred income taxes

Include taxes that will be owed on income, but that have not yet been assessed.

All other liabilities

Include:

  • deferred credits which correspond to unamortized premiums on all classes of long-term debt, and other deferred credits such as securities issued or assumed by the air carrier, and other unadjusted accounts that cannot be cleared as of the date of the balance sheet;
  • provisions for major overhauls such as for flight equipment (in other words, liabilities of uncertain value or timing associated with the complete disassembly and inspection or repair of an aircraft, engine or other component of an aircraft) and other provisions such as liabilities of uncertain value or timing.

Shareholders' equity

Capital stock

Include the equity capital invested in a business through the purchase of various classes of common and preferred shares.

Retained earnings

Include the portion of after-tax profits left over, after dividends have been paid to shareholders, for reinvestment into the company. If this account is negative, then the amount indicated for this item should be shown with a negative (-) sign.

All other items

Include other paid-in capital and reserves. Other paid-in capital or contributed surplus includes the premiums or discounts that have resulted from selling stock, and stock received from donations. Reserves include any reserve fund such as reserve for self-insurance, reserve for pension, reserves against potential future losses, and so on. Also, include proprietorship or partnership accounts (balance year-end).

Total liabilities and capital

The sum of liabilities and capital plus the sum of shareholders' equity which should equal total assets.

Statement of Revenues and Expenses, Annual - Statement 21 (I, II)

Scheduled services

Transportation of passengers or goods, or both, by an aircraft provided by an air carrier that operates the air service and that, directly or indirectly, sells some or all of its seats or part or all of its cargo space to the public on a price per seat, price per unit of mass or price per volume of cargo basis.

Charter services

Transportation of passengers or goods, or both, by aircraft pursuant to a contract under which a person, other than the air carrier that operates the air service, or its agent, reserves a block of seats or part of the cargo space of an aircraft for the person's use or for resale to the public.

Include air ambulance service and the movement of people and goods to logging or heli-logging sites.

Exclude firefighting and heli-logging activities and the movement of people and goods to a firefighting site. (The former Transport Canada TP 8880 document "Starting a Commercial Air Service" outlining a list of activities which are specialty has been replaced with a new document TP 4711 "Air Operator Certification Manual" as of December 2020. A PDF version of volumes of this manual can be requested at: Air Operator Certification Manual – TP 4711)

Passenger revenue

Refers to the revenue earned from the transportation of passengers on scheduled and charter services. Include revenue from all surcharges (baggage, fuel, seat selection, changing or cancelling flights, and so on) that are retained by the air carrier. Exclude amounts such as taxes, navigation fees, security fees, and so on that are collected but passed on to other entities.

Goods revenue

Refers to the revenue earned from the transportation of goods on scheduled and charter services. Exclude taxes such as the Goods and Services Tax (GST), Harmonized Sales Tax (HST) or Provincial Sales Tax (PST).

All other flight-related revenue

Refers to the revenue earned from air transport activities not included in passenger revenue or goods revenue. Include revenue from other flying services such as flying training, recreational flying and other specialty flying.

All other revenue

Include subsidies and revenue earned from all other sources (including contra revenue, revenue of a corporate nature (leasing revenue, third party ground-handling, and so on), ancillary passenger revenue not easily allocated by operating flight (customs brokerage, and so on), revenue from in-flight sales (beverages, food, entertainment and wireless Internet access, and so on)).

Total operating revenue

The sum of passenger revenue, goods revenue, other flight-related revenue and revenue from all other sources.

Operating expenses - Ground property and equipment maintenance

Employee wages, salaries and benefits

Direct labour costs (wages and salaries) expended on the maintenance of ground property and equipment.

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

All other maintenance - ground property and equipment expenses

Expenses, both direct and indirect, incurred in the repair and upkeep of ground property and equipment.

Include materials and supplies, purchased repair services and all other related expenses.

Total maintenance - ground property and equipment expenses

The sum of the previous two expense items.

Operating expenses - Aircraft operations

Flight crew wages, salaries and benefits

Include the wages, salaries and benefits for flight crews (pilot, co-pilot, navigator, and so on).

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on and layover expenses such as hotels and meals.

Aircraft fuel and oil

Expenses for turbo fuel, gasoline and all other fuel and oil consumed such as turbine oil and piston oil.

Include throughput charges, non-refundable duties and taxes.

Landing fees

Include airport landing fees paid both in Canada and outside of Canada.

Navigation fees

Charges remitted to NAV CANADA or other international suppliers for the provision of air navigation services. Air navigation services include aeronautical communication services, aeronautical information services, aeronautical radio navigation services, air traffic control services, aviation weather services, emergency assistance services and flight information services.

Aircraft insurance

Expenses for insurance against accidental damage to flight equipment while in flight or on the ground and for insurance against liability occurring from the operation of aircraft or, in the case of non-insurance, the resulting expenses for which the carrier is liable.

Aircraft rental

Expenses incurred for the rental of aircraft (and crew) from other carriers, such as in chartering, interchange and operating or lease agreements.

All other aircraft operation expenses

Expenses incurred directly for the in-flight operation and related standby time of aircraft which are not elsewhere classified.

Total aircraft operations expenses

The sum of the previous seven expense items.

Operating expenses - Flight equipment maintenance

Employee wages, salaries and benefits

Direct labour costs (wages and salaries) expended on the maintenance of flight equipment.

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Materials and supplies

Expenses on materials and supplies for the maintenance of flight equipment.

Purchased repair services

Expenses for repair services for the maintenance of flight equipment purchased from outside suppliers.

All other maintenance - flight equipment expenses

Expenses, both direct and indirect, incurred in the repair and upkeep of flight equipment.

Total maintenance - flight equipment expenses

The sum of the previous four expense items.

Operating expenses - In-flight services

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to cabin crews (flight attendants, and so on);
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on, and layover expenses such as hotels and meals.

Passenger food and supplies

Include expenses for in-flight meals, complimentary drinks, and so on, and the cost of supplies and personal services furnished to passengers.

Passenger liability insurance

Include the premiums for passenger liability and accident insurance paid by the carrier.

All other in-flight service expenses

Include passenger-related expenses incurred due to interrupted flights, including hotels, meals, taxi fares and other expense items, the cost of other services provided to passengers, such as pay, allowances and the cost of passenger service personnel, and all other services provided for the comfort of passengers in transit.

Total in-flight service expenses

The sum of the previous four expense items.

Operating expenses - Aircraft and traffic servicing

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to ground personnel;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Purchased services

Expenses for aircraft and traffic servicing purchased from outside suppliers.

All other aircraft and traffic servicing expenses

Include expenses incurred on the ground for scheduling or preparing aircraft for arrival and takeoff, expenses incurred in enplaning and deplaning passenger and cargo traffic, and expenses involved in servicing and handling individual aircraft and traffic on the ground, in preparing aircraft crews for flight assignment, in controlling the in-flight movements of aircraft and the in-flight expenses of handling all traffic including baggage.

Total aircraft and traffic servicing expenses

The sum of the previous three expense items.

Operating expenses - Promotion and sales

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to all staff engaged in reservations, ticketing, sales and promotional activities;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

All other promotion and sales expenses

Include:

  • passenger and cargo commission expenses;
  • the net commission payable to others for the sale of transportation on the reporting carrier's service less the commission receivable from the reporting carrier's sale of transportation on other carriers' services, advertising and publicity expenses and any related expenses, accommodation costs, agency fees for outside services, expenses associated with reservations, city ticket offices and other sales expenses.

Total promotion and sales expenses

The sum of the previous two expense items.

Operating expenses - Depreciation

Depreciation - flight equipment

Include:

  • provisions for the depreciation of flight equipment only;
  • all charges incurred in normal wear and tear on flight equipment which have not been replaced by current year repair, as well as losses in service ability.

All other depreciation

Include:

  • provisions for the depreciation of all non-flight ground property and equipment;
  • all charges incurred in normal wear and tear which have not been replaced by current year repair, as well as losses in service ability;
  • charges for the amortization of capitalized development and other intangible assets.

Total depreciation

The sum of the previous two expense items.

Operating expenses - All other expenses

Include general administration.

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to all employees performing the general and administrative functions of the air carrier;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Exclude all amounts reported in the previous six wages, salaries and benefits categories.

All other expenses

Include:

  • all operating expenses and general administration expenses not reported elsewhere;
  • expenses for general financial accounting activities, supplementary labour income, property taxes, building rentals, communications purchased, purchasing activities, representation at law, and all other operational administration expenses not directly applicable to a particular function that are not included in the previous operating expenses categories;
  • expenses such as incidental air transport-related expenses associated with revenue reported as "all other revenue";
  • all miscellaneous operating expenses not covered elsewhere;
  • staff reduction expenses.

Total other expenses

The sum of the previous two expense items.

Total operating expenses

The sum of the eight expenses sub-totals, in other words, Total maintenance - ground property and equipment expenses, Total aircraft operations expenses, and so on.

Operating income

Net operating income (a loss should be a negative number)

Total operating revenue less total operating expenses – calculated from the previous questions.

Non-operating income/expenses

Interest and discount income

Include interest income from all sources and cash discounts on the purchase of materials and supplies.

Interest expenses

Include interest on unpaid taxes and all classes of debt including premiums, discounts and expenses on short-term obligations, amortization of premiums, discounts and expenses on short-term and long-term obligations.

All other net non-operating income (enter a negative number for a loss)

Include:

  • capital gains (or losses) from retiring operating property and equipment, aircraft equipment, expendable parts, miscellaneous materials and supplies and other assets, when they are sold or otherwise retired from service as part of a general program and not as incidental sales performed as a service to others;
  • gains or losses made on investments in securities;
  • net miscellaneous non-operating income or loss, which refers to revenue and expenses attributable to financing or other activities that are not an integral part of the air transportation activities undertaken by the carrier, or its incidental services. These could include dividend income, the balance of all income or losses from affiliated companies reimbursed to the carrier, foreign exchange adjustments and special items, such as restructuring expenses, which do not occur on a regular basis.

Exclude staff reduction expenses which should be included under all other expenses.

Net non-operating income (a loss should be a negative number)

The sum of the previous three income or expense items.

Provision for income taxes

Include the provision for taxes payable on net income for the accounting period and adjustments of income taxes relating to previous years, including provisions for deferred income taxes resulting from differences between accounting income and taxable income that arise when the time of including items of revenue and expense in the computation of accounting income and taxable income do not coincide. If the net amount is negative, then the amount indicated for this item should be shown with a negative (-) sign.

Net income (a loss should be a negative number)

Net operating income plus net non-operating income less the provision for income taxes.

Fuel and oil consumed

Turbo fuel consumed

Include fuel used in both turboprop and jet aircraft.

Provide the quantity and expenses for turbo fuel consumed. Turbo fuel includes the turbine fuel uplifted for all aircraft in the carrier's fleet. Fuel uplift can be determined based on delivery notes or invoices, aircraft onboard measurement systems or, if the fuel was supplied by a customer, estimated based on hours flown. Report the quantity of turbo fuel consumed in litres.

Include turbo fuel consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for turbo fuel consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes. If the fuel was supplied by a customer, an approximate value may be provided based on prevailing market rates.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

All other fuel and oil consumed

Provide the quantity and expenses for all non-turbo fuel and oil consumed. Report the quantity of all other fuel and oil consumed in litres.

The quantity should include gasoline, turbine oil, piston oil and all other types of fuel and oil consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for all other fuel and oil consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

Total fuel and oil consumed

The sum of the quantities and expenses reported in the previous two items.

Employment

Average number of employees

Refer to the average number of people employed for each of the six categories of personnel.

Include all employees, temporary or permanent, on the payroll of the air carrier during the reporting period. Include part-time employees, prorated to the amount of time worked when compared to full-time employees (for example, two part-time employees working half-time are equivalent to one full-time employee).

Wages and salaries expenses

Include a breakdown of the wages and salaries paid for each of the six categories of personnel.

Exclude all benefits, in other words, employer contributions to pensions, medical benefits, insurance, and so on or layover expenses, such as hotels and meals, for flight and cabin crews.

Employment category

Include:

  • Pilots and co-pilots. Self-explanatory;
  • Other flight personnel. Flight crew (including flight engineers, navigators, and so on) and cabin crew (including flight attendants, and so on);
  • General management and administration employees (including the personnel performing the general and administrative functions such as administrative personnel at headquarters, comptrollers and assistants, directors and assistants (operations, passenger service, public relations, sales), and so on);
  • Maintenance personnel (including the personnel performing the ground property and equipment maintenance such as the carpenters, cleaners, and so on and including the personnel performing the flight equipment maintenance such as the aircraft maintenance engineers and the inspectors of flight equipment);
  • Aircraft and traffic servicing personnel (including supervisory personnel, assigned to ground activities, engaged directly in protecting and controlling aircraft in flight (flight dispatch personnel, flight planning staff), in scheduling and preparing flight crews for flight assignment, in parking and servicing aircraft incidental to line operations and including baggage handlers, aircraft fuelers, and so on);
  • All other employees (including air ambulance attendants, accountants, economists, statisticians, lawyers, purchasing personnel, publicity representatives, and so on).

Total employees

The sum of the number and the wages and salaries expenses for the six categories of personnel.

Revenue or expenses by area of operation

Passenger revenue

Include a breakdown of the revenue earned from the transportation of passengers for each province, territory and outside of Canada based on where the transportation service was provided. Total passenger revenue should equal the sum of passenger revenue from scheduled services and charter services previously reported.

Goods revenue

Include a breakdown of the revenue earned from the transportation of goods for each province, territory and outside of Canada based on where the transportation service was provided. Total goods revenue should equal the sum of goods revenue from scheduled services and charter services previously reported.

Employee wages and salaries

Include a breakdown of employee wages and salaries for each province, territory and outside of Canada based on where the employees are located. Total employee wages and salaries should equal the total wages and salaries expenses reported in the "Employment" section above.

Reporting Guide - 2024 Annual Civil Aviation Survey - Level I

Integrated Business Statistics Program (IBSP)

Reporting Guide

This guide is designed to assist you as you complete the 2024 Annual Civil Aviation Survey - Level I. If you need more information, please call the Statistics Canada Help Line at the number below.

Help Line: 1-877-949-9492

Your answers are confidential.

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act.

Statistics Canada will use information from this survey for statistical purposes.

Table of contents

Reporting period information

For the purpose of this survey, please report information for your 12 month fiscal period for which the final day occurs on or between April 1, 2024 — March 31, 2025.

Here are twelve common fiscal periods that fall within the targeted dates:

  • May 1, 2023 to April 30, 2024
  • June 1, 2023 to May 31, 2024
  • July 1, 2023 to June 30, 2024
  • August 1, 2023 to July 31, 2024
  • September 1, 2023 to August 31, 2024
  • October 1, 2023 to September 30, 2024
  • November 1, 2023 to October 31, 2024
  • December 1, 2023 to November 30, 2024
  • January 1, 2024 to December 31, 2024
  • February 1, 2024 to January 31, 2025
  • March 1, 2024 to February 28, 2025
  • April 1, 2024 to March 31, 2025

Here are other examples of fiscal periods that fall within the required dates:

  • September 18, 2023 to September 15, 2024 (e.g., floating year-end)
  • June 1, 2024 to December 31, 2024 (e.g., a newly opened business)

Business or organization and contact information

This section verifies or requests basic identifying information of the business or organization such as legal name, operating name (if applicable), contact information of the designated contact person, current operational status, and main activity(ies).

1. Legal name and Operating name

Legal Name
The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name
The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

2. Designated contact person

Verify or provide the requested contact information of the designated business or organization contact person. The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire. If different than the designated contact person, the contact information of the person completing the questionnaire can be indicated later in the questionnaire.

3. Current operational status

Verify or provide the current operational status of the business or organization identified by the legal and operating name in question 1. If indicating the operational status of the business or organization is 'Not currently operational' then indicate an applicable reason and provide the requested information.

4. Main activity

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS , are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classes; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

If the current NAICS associated with this business or organizations is not correct, please provide a brief description of the main activity and provide any additional information as requested.

Balance Sheet, Annual - Statement 20 (I)

Financial assets

Current assets

Include:

  • cash, bank balances (including deposits in transit, special deposits for the payments of debts, and so on) and short-term investments due within one year from the date of the balance sheet;
  • current accounts and notes receivable as well as other current assets such as inventories, charges to subscribers on transportation contracts, interests and dividends receivable, and so on.

All other financial assets – (Include investments and special funds.)

Include investments in associated companies, other investments such as investments in stocks, bonds, and so on, and special funds such as equipment purchase funds, funds set aside for such special purposes as contractual deposits, pension funds, self-insurance funds, and so on.

Property and equipment

Operating - flight equipment – (Include capital leases.)

Include:

  • flight equipment owned and/or under capital leases;
  • the cost of aircraft (airframes), aircraft engines, propellers, components (aircraft communication and navigational equipment) and spare parts that have been purchased outright. Flight equipment under capital leases includes the cost of flight equipment acquired under a capital or finance lease, in other words, a lease for a period considered to be the whole or nearly the whole life of the aircraft.

Accumulated depreciation and amortization - flight equipment

Include:

  • accumulated depreciation and amortization of flight equipment owned and/or under capital leases;
  • accrued charges representing losses, not replaced by current repairs, occurring in physical property and suffered through current lessening of service value due to wear and tear from use and the action of time and the elements; and losses occurring through obsolescence, supersession, new technological developments, changes in popular demand and the requirements of public authority.

Operating - ground property and equipment – (Include capital leases.)

Include:

  • ground property and equipment owned and/or under capital leases;
  • the cost of non-airborne communication and meteorological equipment, ramp equipment, maintenance and engineering equipment, surface transport vehicles and equipment, furniture, fixtures and office equipment, buildings and land as well as miscellaneous ground equipment such as medical equipment, airport and lighting equipment, passenger service equipment, hotel, restaurant and food service equipment, storage and distribution equipment. Ground property and equipment under capital leases includes the cost of ground property and equipment under a capital or finance lease, in other words, a lease for a period considered to be the whole or nearly the whole life of the property or equipment.

Accumulated depreciation and amortization - ground property and equipment

Include:

  • accumulated depreciation and amortization of ground property and equipment owned and/or under capital leases;
  • accrued charges representing losses, not replaced by current repairs, occurring in physical property and suffered through current lessening of service value due to wear and tear from use and the action of time and the elements; and losses occurring through obsolescence, supersession, new technological developments, changes in popular demand and the requirements of public authority.

Non-operating property and equipment – (Include capital leases.)

Include the cost of all non-operating property and equipment, in other words, all property and equipment not included in the "operating" categories above.

Accumulated depreciation and amortization - non-operating property and equipment

Include accumulated depreciation and amortization of the non-operating property and equipment.

All other assets

Include long-term prepayments, developmental and pre-operating costs such as the cost of extraordinary training, unamortized discounts and expenses on the issue of long-term debt securities, property acquisition adjustments, other intangibles such as payments made for patents, copyrights, and so on, and other deferred charges.

Total assets

The sum of the assets above less the accumulated depreciation and amortization.

Liabilities and capital

Current liabilities

Include:

  • current accounts and traffic balances payable, including balances subject to current settlement and payable to associated companies and/or shareholders, and notes payable on demand or within one year from the date of the balance sheet; current accounts and traffic balances payable, including balances subject to current settlement and payable to associated companies and/or shareholders, and notes payable on demand or within one year from the date of the balance sheet;
  • the current portion of long-term debt and the current obligations under capital leases;
  • air traffic liabilities (unearned transportation revenue), which includes the value of passenger tickets sold but not used or refunded as of the date of the balance sheet, and pre-paid amounts for the transportation of baggage, freight and mail for which the transportation has not occurred as of the date of the balance sheet;
  • salaries and wages accrued and unpaid, taxes accrued and unpaid, dividends payable, deposits by subscribers on transportation contracts (air travel plan liabilities, in other words, deposits received under air travel plan contracts) and other current and accrued liabilities.

Advances from associated companies and/or shareholders

Include the net amount from associated companies and/or shareholders for notes, loans or advances which are not currently settled.

Long-term debt and other non-current liabilities – (Include capital leases.)

Include:

  • the face value or principal amount of debt securities (for example, bonds, trust certificates, debentures, notes) issued and assumed by the air carrier and in the hands of others, which is not payable within twelve months of the balance sheet date;
  • long-term obligations under capital leases, which refers to the present value of unexpired contracts for the acquisition of aircraft under such lease arrangements.

Deferred income taxes

Include taxes that will be owed on income, but that have not yet been assessed.

All other liabilities

Include:

  • deferred credits which correspond to unamortized premiums on all classes of long-term debt, and other deferred credits such as securities issued or assumed by the air carrier, and other unadjusted accounts that cannot be cleared as of the date of the balance sheet;
  • provisions for major overhauls such as for flight equipment (in other words, liabilities of uncertain value or timing associated with the complete disassembly and inspection or repair of an aircraft, engine or other component of an aircraft) and other provisions such as liabilities of uncertain value or timing.

Shareholders' equity

Capital stock

Include the equity capital invested in a business through the purchase of various classes of common and preferred shares.

Retained earnings

Include the portion of after-tax profits left over, after dividends have been paid to shareholders, for reinvestment into the company. If this account is negative, then the amount indicated for this item should be shown with a negative (-) sign.

All other items

Include other paid-in capital and reserves. Other paid-in capital or contributed surplus includes the premiums or discounts that have resulted from selling stock, and stock received from donations. Reserves include any reserve fund such as reserve for self-insurance, reserve for pension, reserves against potential future losses, and so on. Also, include proprietorship or partnership accounts (balance year-end).

Total liabilities and capital

The sum of liabilities and capital plus the sum of shareholders' equity which should equal total assets.

Statement of Revenues and Expenses, Annual - Statement 21 (I, II)

Scheduled services

Transportation of passengers or goods, or both, by an aircraft provided by an air carrier that operates the air service and that, directly or indirectly, sells some or all of its seats or part or all of its cargo space to the public on a price per seat, price per unit of mass or price per volume of cargo basis.

Charter services

Transportation of passengers or goods, or both, by aircraft pursuant to a contract under which a person, other than the air carrier that operates the air service, or its agent, reserves a block of seats or part of the cargo space of an aircraft for the person's use or for resale to the public.

Include air ambulance service and the movement of people and goods to logging or heli-logging sites.

Exclude firefighting and heli-logging activities and the movement of people and goods to a firefighting site. (The former Transport Canada TP 8880 document "Starting a Commercial Air Service" outlining a list of activities which are specialty has been replaced with a new document TP 4711 "Air Operator Certification Manual" as of December 2020. A PDF version of volumes of this manual can be requested at: Air Operator Certification Manual – TP 4711)

Passenger revenue

Refers to the revenue earned from the transportation of passengers on scheduled and charter services. Include revenue from all surcharges (baggage, fuel, seat selection, changing or cancelling flights, and so on) that are retained by the air carrier. Exclude amounts such as taxes, navigation fees, security fees, and so on that are collected but passed on to other entities.

Goods revenue

Refers to the revenue earned from the transportation of goods on scheduled and charter services. Exclude taxes such as the Goods and Services Tax (GST), Harmonized Sales Tax (HST) or Provincial Sales Tax (PST).

All other flight-related revenue

Refers to the revenue earned from air transport activities not included in passenger revenue or goods revenue. Include revenue from other flying services such as flying training, recreational flying and other specialty flying.

All other revenue

Include subsidies and revenue earned from all other sources (including contra revenue, revenue of a corporate nature (leasing revenue, third party ground-handling, and so on), ancillary passenger revenue not easily allocated by operating flight (customs brokerage, and so on), revenue from in-flight sales (beverages, food, entertainment and wireless Internet access, and so on)).

Total operating revenue

The sum of passenger revenue, goods revenue, other flight-related revenue and revenue from all other sources.

Operating expenses - Ground property and equipment maintenance

Employee wages, salaries and benefits

Direct labour costs (wages and salaries) expended on the maintenance of ground property and equipment.

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

All other maintenance - ground property and equipment expenses

Expenses, both direct and indirect, incurred in the repair and upkeep of ground property and equipment.

Include materials and supplies, purchased repair services and all other related expenses.

Total maintenance - ground property and equipment expenses

The sum of the previous two expense items.

Operating expenses - Aircraft operations

Flight crew wages, salaries and benefits

Include the wages, salaries and benefits for flight crews (pilot, co-pilot, navigator, and so on).

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on and layover expenses such as hotels and meals.

Aircraft fuel and oil

Expenses for turbo fuel, gasoline and all other fuel and oil consumed such as turbine oil and piston oil.

Include throughput charges, non-refundable duties and taxes.

Landing fees

Include airport landing fees paid both in Canada and outside of Canada.

Navigation fees

Charges remitted to NAV CANADA or other international suppliers for the provision of air navigation services. Air navigation services include aeronautical communication services, aeronautical information services, aeronautical radio navigation services, air traffic control services, aviation weather services, emergency assistance services and flight information services.

Aircraft insurance

Expenses for insurance against accidental damage to flight equipment while in flight or on the ground and for insurance against liability occurring from the operation of aircraft or, in the case of non-insurance, the resulting expenses for which the carrier is liable.

Aircraft rental

Expenses incurred for the rental of aircraft (and crew) from other carriers, such as in chartering, interchange and operating or lease agreements.

All other aircraft operation expenses

Expenses incurred directly for the in-flight operation and related standby time of aircraft which are not elsewhere classified.

Total aircraft operations expenses

The sum of the previous seven expense items.

Operating expenses - Flight equipment maintenance

Employee wages, salaries and benefits

Direct labour costs (wages and salaries) expended on the maintenance of flight equipment.

Include benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Materials and supplies

Expenses on materials and supplies for the maintenance of flight equipment.

Purchased repair services

Expenses for repair services for the maintenance of flight equipment purchased from outside suppliers.

All other maintenance - flight equipment expenses

Expenses, both direct and indirect, incurred in the repair and upkeep of flight equipment.

Total maintenance - flight equipment expenses

The sum of the previous four expense items.

Operating expenses - In-flight services

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to cabin crews (flight attendants, and so on);
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on, and layover expenses such as hotels and meals.

Passenger food and supplies

Include expenses for in-flight meals, complimentary drinks, and so on, and the cost of supplies and personal services furnished to passengers.

Passenger liability insurance

Include the premiums for passenger liability and accident insurance paid by the carrier.

All other in-flight service expenses

Include passenger-related expenses incurred due to interrupted flights, including hotels, meals, taxi fares and other expense items, the cost of other services provided to passengers, such as pay, allowances and the cost of passenger service personnel, and all other services provided for the comfort of passengers in transit.

Total in-flight service expenses

The sum of the previous four expense items.

Operating expenses - Aircraft and traffic servicing

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to ground personnel;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Purchased services

Expenses for aircraft and traffic servicing purchased from outside suppliers.

All other aircraft and traffic servicing expenses

Include expenses incurred on the ground for scheduling or preparing aircraft for arrival and takeoff, expenses incurred in enplaning and deplaning passenger and cargo traffic, and expenses involved in servicing and handling individual aircraft and traffic on the ground, in preparing aircraft crews for flight assignment, in controlling the in-flight movements of aircraft and the in-flight expenses of handling all traffic including baggage.

Total aircraft and traffic servicing expenses

The sum of the previous three expense items.

Operating expenses - Promotion and sales

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to all staff engaged in reservations, ticketing, sales and promotional activities;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

All other promotion and sales expenses

Include:

  • passenger and cargo commission expenses;
  • the net commission payable to others for the sale of transportation on the reporting carrier's service less the commission receivable from the reporting carrier's sale of transportation on other carriers' services, advertising and publicity expenses and any related expenses, accommodation costs, agency fees for outside services, expenses associated with reservations, city ticket offices and other sales expenses.

Total promotion and sales expenses

The sum of the previous two expense items.

Operating expenses - Depreciation

Depreciation - flight equipment

Include:

  • provisions for the depreciation of flight equipment only;
  • all charges incurred in normal wear and tear on flight equipment which have not been replaced by current year repair, as well as losses in service ability.

All other depreciation

Include:

  • provisions for the depreciation of all non-flight ground property and equipment;
  • all charges incurred in normal wear and tear which have not been replaced by current year repair, as well as losses in service ability;
  • charges for the amortization of capitalized development and other intangible assets.

Total depreciation

The sum of the previous two expense items.

Operating expenses - All other expenses

Include general administration.

Employee wages, salaries and benefits

Include:

  • the wages, salaries and benefits paid to all employees performing the general and administrative functions of the air carrier;
  • benefits such as employer contributions to pensions, medical benefits, insurance, and so on.

Exclude all amounts reported in the previous six wages, salaries and benefits categories.

All other expenses

Include:

  • all operating expenses and general administration expenses not reported elsewhere;
  • expenses for general financial accounting activities, supplementary labour income, property taxes, building rentals, communications purchased, purchasing activities, representation at law, and all other operational administration expenses not directly applicable to a particular function that are not included in the previous operating expenses categories;
  • expenses such as incidental air transport-related expenses associated with revenue reported as "all other revenue";
  • all miscellaneous operating expenses not covered elsewhere;
  • staff reduction expenses.

Total other expenses

The sum of the previous two expense items.

Total operating expenses

The sum of the eight expenses sub-totals, in other words, Total maintenance - ground property and equipment expenses, Total aircraft operations expenses, and so on.

Operating income

Net operating income (a loss should be a negative number)

Total operating revenue less total operating expenses – calculated from the previous questions.

Non-operating income/expenses

Interest and discount income

Include interest income from all sources and cash discounts on the purchase of materials and supplies.

Interest expenses

Include interest on unpaid taxes and all classes of debt including premiums, discounts and expenses on short-term obligations, amortization of premiums, discounts and expenses on short-term and long-term obligations.

All other net non-operating income (enter a negative number for a loss)

Include:

  • capital gains (or losses) from retiring operating property and equipment, aircraft equipment, expendable parts, miscellaneous materials and supplies and other assets, when they are sold or otherwise retired from service as part of a general program and not as incidental sales performed as a service to others;
  • gains or losses made on investments in securities;
  • net miscellaneous non-operating income or loss, which refers to revenue and expenses attributable to financing or other activities that are not an integral part of the air transportation activities undertaken by the carrier, or its incidental services. These could include dividend income, the balance of all income or losses from affiliated companies reimbursed to the carrier, foreign exchange adjustments and special items, such as restructuring expenses, which do not occur on a regular basis.

Exclude staff reduction expenses which should be included under all other expenses.

Net non-operating income (a loss should be a negative number)

The sum of the previous three income or expense items.

Provision for income taxes

Include the provision for taxes payable on net income for the accounting period and adjustments of income taxes relating to previous years, including provisions for deferred income taxes resulting from differences between accounting income and taxable income that arise when the time of including items of revenue and expense in the computation of accounting income and taxable income do not coincide. If the net amount is negative, then the amount indicated for this item should be shown with a negative (-) sign.

Net income (a loss should be a negative number)

Net operating income plus net non-operating income less the provision for income taxes.

Fuel and oil consumed

Turbo fuel consumed

Include fuel used in both turboprop and jet aircraft.

Provide the quantity and expenses for turbo fuel consumed. Turbo fuel includes the turbine fuel uplifted for all aircraft in the carrier's fleet. Fuel uplift can be determined based on delivery notes or invoices, aircraft onboard measurement systems or, if the fuel was supplied by a customer, estimated based on hours flown. Report the quantity of turbo fuel consumed in litres.

Include turbo fuel consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for turbo fuel consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes. If the fuel was supplied by a customer, an approximate value may be provided based on prevailing market rates.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

All other fuel and oil consumed

Provide the quantity and expenses for all non-turbo fuel and oil consumed. Report the quantity of all other fuel and oil consumed in litres.

The quantity should include gasoline, turbine oil, piston oil and all other types of fuel and oil consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for all other fuel and oil consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

Total fuel and oil consumed

The sum of the quantities and expenses reported in the previous two items.

Employment

Average number of employees

Refer to the average number of people employed for each of the six categories of personnel.

Include all employees, temporary or permanent, on the payroll of the air carrier during the reporting period. Include part-time employees, prorated to the amount of time worked when compared to full-time employees (for example, two part-time employees working half-time are equivalent to one full-time employee).

Wages and salaries expenses

Include a breakdown of the wages and salaries paid for each of the six categories of personnel.

Exclude all benefits, in other words, employer contributions to pensions, medical benefits, insurance, and so on or layover expenses, such as hotels and meals, for flight and cabin crews.

Employment category

Include:

  • Pilots and co-pilots. Self-explanatory;
  • Other flight personnel. Flight crew (including flight engineers, navigators, and so on) and cabin crew (including flight attendants, and so on);
  • General management and administration employees (including the personnel performing the general and administrative functions such as administrative personnel at headquarters, comptrollers and assistants, directors and assistants (operations, passenger service, public relations, sales), and so on);
  • Maintenance personnel (including the personnel performing the ground property and equipment maintenance such as the carpenters, cleaners, and so on and including the personnel performing the flight equipment maintenance such as the aircraft maintenance engineers and the inspectors of flight equipment);
  • Aircraft and traffic servicing personnel (including supervisory personnel, assigned to ground activities, engaged directly in protecting and controlling aircraft in flight (flight dispatch personnel, flight planning staff), in scheduling and preparing flight crews for flight assignment, in parking and servicing aircraft incidental to line operations and including baggage handlers, aircraft fuelers, and so on);
  • All other employees (including air ambulance attendants, accountants, economists, statisticians, lawyers, purchasing personnel, publicity representatives, and so on).

Total employees

The sum of the number and the wages and salaries expenses for the six categories of personnel.

Revenue or expenses by area of operation

Passenger revenue

Include a breakdown of the revenue earned from the transportation of passengers for each province, territory and outside of Canada based on where the transportation service was provided. Total passenger revenue should equal the sum of passenger revenue from scheduled services and charter services previously reported.

Goods revenue

Include a breakdown of the revenue earned from the transportation of goods for each province, territory and outside of Canada based on where the transportation service was provided. Total goods revenue should equal the sum of goods revenue from scheduled services and charter services previously reported.

Employee wages and salaries

Include a breakdown of employee wages and salaries for each province, territory and outside of Canada based on where the employees are located. Total employee wages and salaries should equal the total wages and salaries expenses reported in the "Employment" section above.

Reporting Guide - 2024 Annual Civil Aviation Survey - Level III

Integrated Business Statistics Program (IBSP)

Reporting Guide

This guide is designed to assist you as you complete the 2024 Annual Civil Aviation Survey - Level III. If you need more information, please call the Statistics Canada Help Line at the number below.

Help Line: 1-877-949-9492

Your answers are confidential.

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act.

Statistics Canada will use information from this survey for statistical purposes.

Table of contents

Reporting period information

For the purpose of this survey, please report information for your 12 month fiscal period for which the final day occurs on or between April 1, 2024 — March 31, 2025.

Here are twelve common fiscal periods that fall within the targeted dates:

  • May 1, 2023 to April 30, 2024
  • June 1, 2023 to May 31, 2024
  • July 1, 2023 to June 30, 2024
  • August 1, 2023 to July 31, 2024
  • September 1, 2023 to August 31, 2024
  • October 1, 2023 to September 30, 2024
  • November 1, 2023 to October 31, 2024
  • December 1, 2023 to November 30, 2024
  • January 1, 2024 to December 31, 2024
  • February 1, 2024 to January 31, 2025
  • March 1, 2024 to February 28, 2025
  • April 1, 2024 to March 31, 2025

Here are other examples of fiscal periods that fall within the required dates:

  • September 18, 2023 to September 15, 2024 (e.g., floating year-end)
  • June 1, 2024 to December 31, 2024 (e.g., a newly opened business)

Business or organization and contact information

This section verifies or requests basic identifying information of the business or organization such as legal name, operating name (if applicable), contact information of the designated contact person, current operational status, and main activity(ies).

1. Legal name and Operating name

Legal Name

The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

2. Designated contact person

Verify or provide the requested contact information of the designated business or organization contact person. The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire. If different than the designated contact person, the contact information of the person completing the questionnaire can be indicated later in the questionnaire.

3. Current operational status

Verify or provide the current operational status of the business or organization identified by the legal and operating name in question 1. If indicating the operational status of the business or organization is 'Not currently operational' then indicate an applicable reason and provide the requested information.

4. Main activity

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS , are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classes; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Please note that any modifications to the main activity through your response to this question might not necessarily be reflected prior to the transmitting of subsequent questionnaires and as a result they may not contain this updated information.

If the current NAICS associated with this business or organizations is not correct, please provide a brief description of the main activity and provide any additional information as requested.

Balance Sheet, Annual - Statement 20 (II, III)

Financial assets

Current assets

Include:

  • cash, bank balances (including deposits in transit, special deposits for the payments of debts, and so on) and short-term investments due within one year from the date of the balance sheet;
  • current accounts and notes receivable as well as other current assets such as inventories, charges to subscribers on transportation contracts, interests and dividends receivable, and so on.

All other financial assets – (Include investments and special funds.)

Include investments in associated companies, other investments such as investments in stocks, bonds, and so on, and special funds such as equipment purchase funds, funds set aside for such special purposes as contractual deposits, pension funds, self-insurance funds, and so on.

Property and equipment

Operating - property and equipment – (Include capital leases.)

Include:

  • ground property and equipment (including flight equipment) owned and/or under capital leases;
  • the cost of aircraft (airframes), aircraft engines, propellers, components (aircraft communication and navigational equipment) and spare parts that have been purchased outright;
  • the cost of non-airborne communication and meteorological equipment, ramp equipment, maintenance and engineering equipment, surface transport vehicles and equipment, furniture, fixtures and office equipment, buildings and land as well as miscellaneous ground equipment such as medical equipment, airport and lighting equipment, passenger service equipment, hotel, restaurant and food service equipment, storage and distribution equipment. Property and equipment under capital leases includes the cost of property and equipment under a capital or finance lease, in other words, a lease for a period considered to be the whole or nearly the whole life of the property or equipment.

Accumulated depreciation and amortization - property and equipment

Include:

  • accumulated depreciation and amortization of ground property and equipment (including flight equipment) owned and/or under capital leases;
  • accrued charges representing losses, not replaced by current repairs, occurring in physical property and suffered through current lessening of service value due to wear and tear from use and the action of time and the elements; and losses occurring through obsolescence, supersession, new technological developments, changes in popular demand and the requirements of public authority.

Non-operating property and equipment – (Include capital leases.)

Include the cost of all non-operating property and equipment, in other words, all property and equipment not included in the "operating" category above.

Accumulated depreciation and amortization - non-operating property and equipment

Include accumulated depreciation and amortization of the non-operating property and equipment.

All other assets

Include long-term prepayments, developmental and pre-operating costs such as the cost of extraordinary training, unamortized discounts and expenses on the issue of long-term debt securities, property acquisition adjustments, other intangibles such as payments made for patents, copyrights, and so on, and other deferred charges.

Total assets

The sum of the assets above less the accumulated depreciation and amortization.

Liabilities and capital

Current liabilities

Include:

  • current accounts and traffic balances payable, including balances subject to current settlement and payable to associated companies and/or shareholders, and notes payable on demand or within one year from the date of the balance sheet;
  • the current portion of long-term debt and the current obligations under capital leases;
  • air traffic liabilities (unearned transportation revenue), which includes the value of passenger tickets sold but not used or refunded as of the date of the balance sheet, and pre-paid amounts for the transportation of baggage, freight and mail for which the transportation has not occurred as of the date of the balance sheet;
  • salaries and wages accrued and unpaid, taxes accrued and unpaid, dividends payable, deposits by subscribers on transportation contracts (air travel plan liabilities, in other words, deposits received under air travel plan contracts) and other current and accrued liabilities.

Advances from associated companies and/or shareholders

Include the net amount from associated companies and/or shareholders for notes, loans or advances which are not currently settled.

Long-term debt and other non-current liabilities – (Include capital leases.)

Include:

  • the face value or principal amount of debt securities (for example, bonds, trust certificates, debentures, notes) issued and assumed by the air carrier and in the hands of others, which is not payable within twelve months of the balance sheet date;
  • long-term obligations under capital leases, which refers to the present value of unexpired contracts for the acquisition of aircraft under such lease arrangements.

Deferred income taxes

Include taxes that will be owed on income, but that have not yet been assessed.

All other liabilities

Include:

  • deferred credits which correspond to unamortized premiums on all classes of long-term debt, and other deferred credits such as securities issued or assumed by the air carrier, and other unadjusted accounts that cannot be cleared as of the date of the balance sheet;
  • provisions for major overhauls such as for flight equipment (in other words, liabilities of uncertain value or timing associated with the complete disassembly and inspection or repair of an aircraft, engine or other component of an aircraft) and other provisions such as liabilities of uncertain value or timing.

Shareholders' equity

Capital stock

Include the equity capital invested in a business through the purchase of various classes of common and preferred shares.

Retained earnings

Include the portion of after-tax profits left over, after dividends have been paid to shareholders, for reinvestment into the company. If this account is negative, then the amount indicated for this item should be shown with a negative (-) sign.

All other items

Include other paid-in capital and reserves. Other paid-in capital or contributed surplus includes the premiums or discounts that have resulted from selling stock, and stock received from donations. Reserves include any reserve fund such as reserve for self-insurance, reserve for pension, reserves against potential future losses, and so on. Also, include proprietorship or partnership accounts (balance year-end).

Total liabilities and capital

The sum of liabilities and capital plus the sum of shareholders' equity which should equal total assets.

Statement of Revenues and Expenses, Annual - Statement 21 (III)

Operating revenue

Scheduled services

Transportation of passengers or goods, or both, by an aircraft provided by an air carrier that operates the air service and that, directly or indirectly, sells some or all of its seats or part or all of its cargo space to the public on a price per seat, price per unit of mass or price per volume of cargo basis.

Charter services

Transportation of passengers or goods, or both, by aircraft pursuant to a contract under which a person, other than the air carrier that operates the air service, or its agent, reserves a block of seats or part of the cargo space of an aircraft for the person's use or for resale to the public.

Include air ambulance service and the movement of people and goods to logging or heli-logging sites.

Exclude firefighting and heli-logging activities and the movement of people and goods to a firefighting site. (The former Transport Canada TP 8880 document "Starting a Commercial Air Service" outlining a list of activities which are specialty has been replaced with a new document TP 4711 "Air Operator Certification Manual" as of December 2020. A PDF version of volumes of this manual can be requested at: Air Operator Certification Manual – TP 4711)

Passenger revenue

Refers to the revenue earned from the transportation of passengers on scheduled and charter services. Include revenue from all surcharges (baggage, fuel, seat selection, changing or cancelling flights, and so on) that are retained by the air carrier. Exclude amounts such as taxes, navigation fees, security fees, and so on that are collected but passed on to other entities.

Goods revenue

Refers to the revenue earned from the transportation of goods on scheduled and charter services. Exclude taxes such as the Goods and Services Tax (GST), Harmonized Sales Tax (HST) or Provincial Sales Tax (PST).

All other flight - related revenue

Refers to the revenue earned from air transport activities not included in passenger revenue or goods revenue. Include revenue from other flying services such as flying training, recreational flying and other specialty flying.

All other revenue

Include subsidies and revenue earned from all other sources (including contra revenue, revenue of a corporate nature (leasing revenue, third party ground-handling, and so on), ancillary passenger revenue not easily allocated by operating flight (customs brokerage, and so on), revenue from in-flight sales (beverages, food, entertainment and wireless Internet access, and so on)).

Total operating revenue

The sum of passenger revenue, goods revenue, other flight-related revenue and revenue from all other sources.

Operating expenses

Maintenance - ground property and equipment

Expenses incurred in the repair and upkeep of ground property and equipment. Include employee wages, salaries and benefits (including employer contributions to pensions, medical benefits, insurance, and so on), expenses for materials and supplies, purchased repair services and all other related expenses.

Aircraft operations

Expenses incurred directly for the in-flight operation of aircraft or in the holding of aircraft and aircraft personnel in readiness for assignment to an in-flight status.

Include:

  • the wages, salaries and benefits (including employer contributions to pensions, medical benefits, insurance, and so on and layover expenses such as hotels and meals) for flight crews (pilot, co-pilot, navigator, and so on);
  • expenses for turbo fuel, gasoline and all other fuel and oil consumed such as turbine oil and piston oil (including throughput charges, non-refundable duties and taxes);
  • airport landing fees paid both in Canada and outside of Canada;
  • navigation fees remitted to NAV CANADA or other international suppliers for the provision of air navigation services (air navigation services include aeronautical communication services, aeronautical information services, aeronautical radio navigation services, air traffic control services, aviation weather services, emergency assistance services and flight information services);
  • expenses for insurance against accidental damage to flight equipment while in flight or on the ground and for insurance against liability occurring from the operation of aircraft or, in the case of non-insurance, the resulting expenses for which the carrier is liable;
  • expenses incurred for the rental of aircraft (and crew) from other carriers, such as in chartering, interchange and operating or lease agreements;
  • all other aircraft operation expenses incurred directly for the in-flight operation and related standby time of aircraft.

Maintenance - flight equipment

Expenses incurred in the repair and upkeep of flight equipment. Include employee wages, salaries and benefits (including employer contributions to pensions, medical benefits, insurance, and so on), expenses for materials and supplies, purchased repair services and all other related expenses.

General administration

Expenses incurred for in-flight service, aircraft and traffic servicing, promotion and sales and general administration.

Include:

  • the wages, salaries and benefits (including employer contributions to pensions, medical benefits, insurance, and so on and layover expenses such as hotels and meals) paid to cabin crews (flight attendants, and so on), ground personnel, staff engaged in reservations, ticketing, sales and promotional activities and all other employees (including the personnel performing the general and administrative functions of the air carrier);
  • expenses for in-flight service such as passenger food and supplies (in-flight meals, complimentary drinks, and so on and the cost of supplies and personal services furnished to passengers), passenger liability insurance (premiums for passenger liability and accident insurance) and all other in-flight service expenses (including passenger-related expenses incurred due to interrupted flights, including hotels, meals, taxi fares and other expense items, the cost of other services provided to passengers, such as pay, allowances and the cost of passenger service personnel, and all other services provided for the comfort of passengers in transit);
  • expenses for aircraft and traffic servicing purchased from outside suppliers, expenses incurred on the ground for scheduling or preparing aircraft for arrival and takeoff, expenses incurred in enplaning and deplaning passenger and cargo traffic, and expenses involved in servicing and handling individual aircraft and traffic on the ground, in preparing aircraft crews for flight assignment, in controlling the in-flight movements of aircraft and the in-flight expenses of handling all traffic including baggage;
  • all promotion and sales expenses such as passenger and cargo commission expenses (the net commission payable to others for the sale of transportation on the reporting carrier's service less the commission receivable from the reporting carrier's sale of transportation on other carriers' services), advertising and publicity expenses and any related expenses, accommodation costs, agency fees for outside services, expenses associated with reservations, city ticket offices and other sales expenses;
  • general administration expenses such as those for financial accounting activities, supplementary labour income, property taxes, building rentals, communications purchased, purchasing activities, representation at law, and all other operational administration expenses not directly applicable to a particular function that are not included in the previous operating expenses categories;
  • expenses such as incidental air transport-related expenses associated with revenue reported as "all other revenue";
  • staff reduction expenses.

Depreciation

Include:

  • provisions for the depreciation of ground property and equipment (including flight equipment);
  • all charges incurred in normal wear and tear on property and equipment which have not been replaced by current year repair, as well as losses in service ability;
  • charges for the amortization of capitalized development and other intangible assets.

All other expenses

Include any and all miscellaneous operating expenses not reported elsewhere.

Total operating expenses

The sum of the previous six expense items.

Operating income

Net operating income (a loss should be a negative number)

Total operating revenue less total operating expenses – calculated from the previous questions.

Non-operating income/expenses

Interest and discount income

Include interest income from all sources and cash discounts on the purchase of materials and supplies.

Interest expenses

Include interest on unpaid taxes and all classes of debt including premiums, discounts and expenses on short-term obligations, amortization of premiums, discounts and expenses on short-term and long-term obligations.

All other net non-operating income (enter a negative number for a loss)

Include:

  • capital gains (or losses) from retiring operating property and equipment, aircraft equipment, expendable parts, miscellaneous materials and supplies and other assets, when they are sold or otherwise retired from service as part of a general program and not as incidental sales performed as a service to others;
  • gains or losses made on investments in securities;
  • net miscellaneous non-operating income or loss, which refers to revenue and expenses attributable to financing or other activities that are not an integral part of the air transportation activities undertaken by the carrier, or its incidental services. These could include dividend income, the balance of all income or losses from affiliated companies reimbursed to the carrier, foreign exchange adjustments and special items, such as restructuring expenses, which do not occur on a regular basis.

Exclude staff reduction expenses which should be included under all other expenses.

Net non-operating income (a loss should be a negative number)

The sum of the previous three income or expense items.

Provision for income taxes

Include the provision for taxes payable on net income for the accounting period and adjustments of income taxes relating to previous years, including provisions for deferred income taxes resulting from differences between accounting income and taxable income that arise when the time of including items of revenue and expense in the computation of accounting income and taxable income do not coincide. . If the net amount is negative, then the amount indicated for this item should be shown with a negative (-) sign.

Net income (a loss should be a negative number)

Net operating income plus net non-operating income less the provision for income taxes.

Fuel and oil consumed

Turbo fuel consumed

Include fuel used in both turboprop and jet aircraft.

Provide the quantity and expenses for turbo fuel consumed. Turbo fuel includes the turbine fuel uplifted for all aircraft in the carrier's fleet. Fuel uplift can be determined based on delivery notes or invoices, aircraft onboard measurement systems or, if the fuel was supplied by a customer, estimated based on hours flown. Report the quantity of turbo fuel consumed in litres.

Include turbo fuel consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for turbo fuel consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes. If the fuel was supplied by a customer, an approximate value may be provided based on prevailing market rates.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

Gasoline consumed

Provide the quantity and expenses for gasoline consumed. If the gasoline was supplied by a customer, it may be estimated based on hours flown. Report the quantity of gasoline consumed in litres.

Include gasoline consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for gasoline consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes. If the gasoline was supplied by a customer, an approximate value may be provided based on prevailing market rates.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

All other fuel and oil consumed

Provide the quantity and expenses for all other fuel and oil consumed. Report the quantity of all other fuel and oil consumed in litres.

The quantity should include turbine oil, piston oil and all other types of fuel and oil consumed for all scheduled and/or charter operations, regardless of where purchased. The expenses for all other fuel and oil consumed should be reported in Canadian dollars, regardless of where purchased. Include throughput charges, non-refundable duties and taxes.

Conversion factor

To convert gallons (imperial) into litres (l), multiply by 4.546092.

Total fuel and oil consumed

The sum of the quantities and expenses reported in the previous three items.

Employment

Average number of employees

Refers to the average number of people employed for each of the six categories of personnel.

Include all employees, temporary or permanent, on the payroll of the air carrier during the reporting period. Include part-time employees, prorated to the amount of time worked when compared to full-time employees (for example, two part-time employees working half-time are equivalent to one full-time employee).

Wages and salaries expenses

Include a breakdown of the wages and salaries paid for each of the six categories of personnel.

Exclude all benefits, in other words, employer contributions to pensions, medical benefits, insurance, and so on or layover expenses, such as hotels and meals, for flight and cabin crews.

Employment category

Include:

  • Pilots and co-pilots. Self-explanatory;
  • Other flight personnel. Flight crew (including flight engineers, navigators, and so on) and cabin crew (including flight attendants, and so on);
  • General management and administration employees (including the personnel performing the general and administrative functions such as administrative personnel at headquarters, comptrollers and assistants, directors and assistants (operations, passenger service, public relations, sales), and so on);
  • Maintenance personnel (including the personnel performing the ground property and equipment maintenance such as the carpenters, cleaners, and so on and including the personnel performing the flight equipment maintenance such as the aircraft maintenance engineers and the inspectors of flight equipment);
  • Aircraft and traffic servicing personnel (including supervisory personnel, assigned to ground activities, engaged directly in protecting and controlling aircraft in flight (flight dispatch personnel, flight planning staff), in scheduling and preparing flight crews for flight assignment, in parking and servicing aircraft incidental to line operations and including baggage handlers, aircraft fuelers, and so on);
  • All other employees (including air ambulance attendants, accountants, economists, statisticians, lawyers, purchasing personnel, publicity representatives, and so on).

Total employees

The sum of the number and the wages and salaries expenses for the six categories of personnel.

Revenue and expenses by area of operation

Passenger revenue

Include a breakdown of the revenue earned from the transportation of passengers for each province, territory and outside of Canada based on where the transportation service was provided. Total passenger revenue should equal the sum of passenger revenue from scheduled services and charter services previously reported.

Goods revenue

Include a breakdown of the revenue earned from the transportation of goods for each province, territory and outside of Canada based on where the transportation service was provided. Total goods revenue should equal the sum of goods revenue from scheduled services and charter services previously reported.

Employee wages and salaries

Include a breakdown of employee wages and salaries for each province, territory and outside of Canada based on where the employees are located. Total employee wages and salaries should equal the total wages and salaries expenses reported in the "Employment" section above.

Scheduled Services, Revenue Operating Statistics, Annual - Statement 10 (III)

Scheduled services - operating statistics

Include fixed wing and helicopter services.

Sector of operation

Refers to the regions where carriers provide transportation services. There are three breakdowns – domestic, transborder (Canada-US) and other international.

Domestic

Includes operations between points in Canada.

Transborder (Canada-US)

Includes operations between points in Canada and points in the United States (including Alaska, Hawaii and Puerto Rico).

Other international

Includes all other operations (including between points outside of Canada).

Data reported must include both fixed wing and helicopter services, where:

Fixed wing

Means a power-driven, heavier-than-air aircraft, deriving its lift in flight chiefly from aerodynamic reactions on surfaces which remain fixed. An aircraft having wings fixed to the airplane fuselage and outspread in flight – that is non-rotating wings.

Helicopter

Means a rotary wing, heavier-than-air aircraft, supported in flight chiefly by the reactions of the air on one or more power-driven rotors on substantially vertical axes. A helicopter does not have conventional fixed wings, nor is it provided with a conventional propeller for forward thrust.

Scheduled services

Transportation of passengers or goods, or both, by an aircraft provided by an air carrier that operates the air service and that, directly or indirectly, sells some or all of its seats or part or all of its cargo space to the public on a price per seat, price per unit of mass or price per volume of cargo basis.

Enplaned passengers

Refers to revenue passengers1 who board aircraft and surrender one or more flight coupons or other documents good for transportation over the itinerary specified in these coupons or documents.

1Revenue passengers correspond to passengers for which an air carrier receives remuneration and who are travelling with tickets purchased (a) under a publicly available promotional offer; (b) through a loyalty program or through the redemption of loyalty points or miles; (c) with a corporate discount or at a preferential fare; or obtained (d) as compensation for denied boarding. It excludes (a) passengers travelling for free, at a fare available only to persons who are employees or agents of an air carrier or are travelling on the business of an air carrier; and (b) persons, such as infants, who do not occupy seats.

Passenger-kilometres

Represents the carriage of one revenue passenger on each flight stage multiplied by the number of kilometres flown on that stage. Passenger-kilometres are obtained by totalling the number of kilometres flown by all passengers.

Let's take an example with two flight stages, where:

Flight stage A to B
Number of passengers = 5
Distance between points (km) = 161
Passenger-kilometres = 805

Flight stage B to C
Number of passengers = 4
Distance between points (km) = 322
Passenger-kilometres = 1,288

The total number of passenger-kilometres for the flights covering A to B and B to C is 2,093.

Conversion factor

To convert nautical miles (6,080 feet) into kilometres (km), multiply by 1.852.

To convert statute miles (5,280 feet) into kilometres (km), multiply by 1.609344.

Hours flown

Represents the block hours, in other words, the number of hours which elapsed between the time the aircraft started to move to commence a flight and the time the aircraft came to its final stop after the conclusion of a flight. Report the total number of block hours flown to the nearest hour.

Enplaned goods

Refers to all types of non-passenger traffic. It includes priority freight, freight, mail and excess baggage for which revenue is obtained. Enplaned goods should be reported to the nearest kilogram.

Conversion factor

To convert pounds (lbs.) into kilograms (kg), multiply by 0.453592.

Goods tonne-kilometres

Represents the carriage of one tonne of goods on each flight stage multiplied by the number of kilometres flown on that stage. Goods tonne-kilometres are obtained by totalling the number of kilometres flown with all tonnes of goods.

Let's take an example with two flight stages, where:

Flight stage A to B
Tonnes of goods = 5
Distance between points (km) = 161
Goods tonne-kilometres = 805

Flight stage B to C
Tonnes of goods = 4
Distance between points (km) = 322
Goods tonne-kilometres = 1,288

The total number of goods tonne-kilometres for the flights covering A to B and B to C is 2,093.

Conversion factor

To convert nautical miles (6,080 feet) into kilometres (km), multiply by 1.852.

To convert statute miles (5,280 feet) into kilometres (km), multiply by 1.609344.

Scheduled services - revenue

Include fixed wing and helicopter services.

Passenger revenue

Refers to the revenue earned from the transportation of passengers on scheduled services. Include revenue from all surcharges (baggage, fuel, seat selection, and so on) that are retained by the air carrier. Exclude amounts such as taxes, navigation fees, security fees, and so on that are collected but passed on to other entities.

Goods revenue

Refers to the revenue earned from the transportation of goods on scheduled services. Exclude taxes such as the Goods and Services Tax (GST), Harmonized Sales Tax (HST) or Provincial Sales Tax (PST).

Charter Services, Revenue Operating Statistics, Annual - Statement 12 (III)

Charter services - operating statistics

Include fixed wing and helicopter services.

Sector of operation

Refers to the regions where carriers provide transportation services. There are three breakdowns – domestic, transborder (Canada-US) and other international.

Domestic

Includes operations between points in Canada.

Transborder (Canada-US)

Includes operations between points in Canada and points in the United States (including Alaska, Hawaii and Puerto Rico).

Other international

Includes all other operations (including between points outside of Canada).

Data reported must include both fixed wing and helicopter services, where:

Fixed wing

Means a power-driven, heavier-than-air aircraft, deriving its lift in flight chiefly from aerodynamic reactions on surfaces which remain fixed. An aircraft having wings fixed to the airplane fuselage and outspread in flight – that is non-rotating wings.

Helicopter

Means a rotary wing, heavier-than-air aircraft, supported in flight chiefly by the reactions of the air on one or more power-driven rotors on substantially vertical axes. A helicopter does not have conventional fixed wings, nor is it provided with a conventional propeller for forward thrust.

Charter services

Transportation of passengers or goods, or both, by aircraft pursuant to a contract under which a person, other than the air carrier that operates the air service, or its agent, reserves a block of seats or part of the cargo space of an aircraft for the person's use or for resale to the public.

Include air ambulance service and the movement of people and goods to logging or heli-logging sites.

Exclude firefighting and heli-logging activities and the movement of people and goods to a firefighting site. (The former Transport Canada TP 8880 document "Starting a Commercial Air Service" outlining a list of activities which are specialty has been replaced with a new document TP 4711 "Air Operator Certification Manual" as of December 2020. A PDF version of volumes of this manual can be requested at: Air Operator Certification Manual – TP 4711)

Enplaned passengers

Refers to revenue passengers1 who board aircraft and surrender one or more flight coupons or other documents good for transportation over the itinerary specified in these coupons or documents.

1Revenue passengers correspond to passengers for which an air carrier receives remuneration and who are travelling with tickets purchased (a) under a publicly available promotional offer; (b) through a loyalty program or through the redemption of loyalty points or miles; (c) with a corporate discount or at a preferential fare; or obtained (d) as compensation for denied boarding. It excludes (a) passengers travelling for free, at a fare available only to persons who are employees or agents of an air carrier or are travelling on the business of an air carrier; and (b) persons, such as infants, who do not occupy seats.

Passenger-kilometres

Represents the carriage of one revenue passenger on each flight stage multiplied by the number of kilometres flown on that stage. Passenger-kilometres are obtained by totalling the number of kilometres flown by all passengers.

Let's take an example with two flight stages, where:

Flight stage A to B
Number of passengers = 5
Distance between points (km) = 161
Passenger-kilometres = 805

Flight stage B to C
Number of passengers = 4
Distance between points (km) = 322
Passenger-kilometres = 1,288

The total number of passenger-kilometres for the flights covering A to B and B to C is 2,093.

Conversion factor

To convert nautical miles (6,080 feet) into kilometres (km), multiply by 1.852.

To convert statute miles (5,280 feet) into kilometres (km), multiply by 1.609344.

Hours flown

Represents the block hours, in other words, the number of hours which elapsed between the time the aircraft started to move to commence a flight and the time the aircraft came to its final stop after the conclusion of a flight. Report the total number of block hours flown to the nearest hour.

Enplaned goods

Refers to all types of non-passenger traffic. It includes priority freight, freight, mail and excess baggage for which revenue is obtained. Enplaned goods should be reported to the nearest kilogram.

Conversion factor

To convert pounds (lbs.) into kilograms (kg), multiply by 0.453592.

Goods tonne-kilometres

Represents the carriage of one tonne of goods on each flight stage multiplied by the number of kilometres flown on that stage. Goods tonne-kilometres are obtained by totalling the number of kilometres flown with all tonnes of goods.

Let's take an example with two flight stages, where:

Flight stage A to B
Tonnes of goods = 5
Distance between points (km) = 161
Goods tonne-kilometres = 805

Flight stage B to C
Tonnes of goods = 4
Distance between points (km) = 322
Goods tonne-kilometres = 1,288

The total number of goods tonne-kilometres for the flights covering A to B and B to C is 2,093.

Conversion factor

To convert nautical miles (6,080 feet) into kilometres (km), multiply by 1.852.

To convert statute miles (5,280 feet) into kilometres (km), multiply by 1.609344.

Charter services - revenue

Include fixed wing and helicopter services.

Passenger revenue

Refers to the revenue earned from the transportation of passengers on charter services. Include revenue from all surcharges (baggage, fuel, seat selection, and so on) that are retained by the air carrier. Exclude amounts such as taxes, navigation fees, security fees, and so on that are collected but passed on to other entities.

Goods revenue

Refers to the revenue earned from the transportation of goods on charter services. Exclude taxes such as the Goods and Services Tax (GST), Harmonized Sales Tax (HST) or Provincial Sales Tax (PST).

Labour Market Indicators – April 2025

In April 2025, questions measuring the Labour Market Indicators were added to the Labour Force Survey as a supplement.

Questionnaire flow within the collection application is controlled dynamically based on responses provided throughout the survey. Therefore, some respondents will not receive all questions, and there is a small chance that some households will not receive any questions at all. This is based on their answers to certain LFS questions.

Labour Market Indicators

ENTRY_Q01 / EQ 1 - From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

SCH_Q01 / EQ 2 - Which of the following best describes your usual work schedule at your main job or business?

  1. Regular daytime hours or daytime shift
  2. Regular evening hours or evening shift
  3. Regular night hours or night shift 
  4. Irregular hours, irregular shifts, or on-call 
  5. Other

SCH_Q02 / EQ 3 - At your main job or business, do you have a flexible schedule that allows you to choose the time when your work day begins and ends?

  1. Yes
  2. No

WFR_Q01 / EQ 4 - At your main job or business, how easy is it for you to take an hour or two off for personal or family matters during working hours?

  1. Very easy
  2. Easy
  3. Difficult
  4. Very difficult

WFR_Q02 / EQ 5 - Over the last month, how often has it been difficult for you to fulfill your family responsibilities because of the amount of time you spent working?

  1. Always
  2. Often
  3. Sometimes
  4. Rarely
  5. Never

OR 

6. Did not work over the last month

SEC_Q01 / EQ 6 - To what extent do you agree or disagree with the following statement?

You might lose your main [job/business] in the next six months.

  1. Strongly agree
  2. Agree
  3. Neither agree nor disagree
  4. Disagree
  5. Strongly disagree

SEC_Q02 / EQ 7 - Over the next six months, do you anticipate that the number of employees at your workplace will increase, stay the same or decrease?

  1. Increase a lot
  2. Increase a little
  3. No change
  4. Decrease a little
  5. Decrease a lot
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