By: Uchenna Mgbaja, Nazmus Sakeef, Kendrick Moreno, Catrina Llamas, and Roe Alincastre; NorQuest College

Introduction

The Edmonton Food Drive (EFD) Project is a collaborative effort between NorQuest College, LDS Church, etc. to improve the logistics of one of Alberta’s largest community food donation initiatives. The current food donation management system faces challenges in coordinating drop-off locations, pick-up processes, and route planning. There is a need to automate and enhance these processes to ensure timely collection of donations and minimize logistical complexities.

This multi-stakeholder project supports over 40,000 people monthly by distributing over 400,000 meals to people in need. These figures show the significant demand within the community and highlight the critical role of collaborative efforts in combating food insecurity.

The objective of this project was to develop a machine learning solution to enhance the management of food donation activities in Alberta. The project aims to increase the efficiency and effectiveness of drop-off and pick-up processes, streamline route planning, and improve resource allocation.

Proposed Solutions

A key component of the Edmonton Food Drive is the role played by Wards and Stakes, organizational units within The Church of Jesus Christ of Latter-day Saints (LDS), which facilitate volunteer participation and logistical coordination.

In the LDS Church, a ward is a local congregation that serves a specific geographic area, while a stake is a larger administrative unit composed of multiple Wards. In the context of the EFD Project, Stakes oversee multiple Wards, providing organizational support and resources, while Wards coordinate volunteer efforts, donation collection, and route management within their respective areas.

Building on the objectives of the project, the following solutions were proposed and developed to tackle the identified challenges:

• Data Collection Improvements:

Create data-acquisition forms to collect data from Wards via structured surveys, enabling volunteers to answer questions as quickly and efficiently as possible.

• Trend Analysis:

Use data visualization and statistical techniques to perform a year-over-year analysis, revealing critical trends and performance indicators.

• Interactive Dashboards:

Create user-friendly, interactive dashboards that allow stakeholders to easily explore and compare data, facilitating more informed decision-making.

• Predictive Modeling:

Implement machine learning techniques to develop a predictive model that forecasts donation patterns and identifies emerging trends.

• Efficiency Forecasting:

Build a predictive model to estimate which Wards or Stakes will have the greatest impact in terms of efficiency for 2025.

• Route Mapping Application:

Develop a route digitization application that automatically generates digitized maps for volunteers, improving operational efficiency. Create a route mapping application that generates interactive maps for volunteers, focusing on high-demand or hot-zone addresses for long-term operational efficiency.

These proposed solutions aimed to streamline operational processes, enhance stakeholder engagement, and leverage predictive insights to improve the planning and execution of future food drives.

Methodology

Data Collection

Data on donation volumes, routes, and volunteer participation were gathered during the Edmonton Food Drive in September 2023 & September 2024.  Data was collected from 6 assigned Stakes and 27 Wards. This data was systematically collected from designated drop-off centers, as assigned by client representatives, ensuring accurate coverage of specific routes and regions. While comprehensive within the assigned scope, the data did not represent all collection points across Edmonton, limiting its full-city applicability.

Datasets:

We started our analysis on data collected in 2023 from Wards. In 2024, we added data validation rules to mitigate the risks of wrong data entries while ensuring that the time required for volunteers to complete the form remains as short as before.

The data collected in 2023 focused on essential information related to donation collection, volunteers, and routes. While it provided a solid foundation, it was limited in terms of data validation and feature richness. The dataset consisted of 13 features and 454 samples.

Column Name	Description
Date	The date of the food drive activity took place.
Location	The specific area or neighborhood where the food drive was conducted.
Stake	The organization or group responsible for managing the volunteers in the area.
# of Adult Volunteers	The number of adult volunteers who participated in the activity.
# of Youth Volunteers	The number of youth volunteers who participated in the activity.
Donation Bags Collected	The total number of donation bags collected during the activity.
Time to Complete (min)	The total time (in minutes) taken to complete the assigned route(s).
Completed More Than One Route	Indicates whether more than one route was completed (e.g., Yes/No).
Ward	The municipal ward where the food drive activity occurred.
Routes Completed	The total number of routes were completed by the volunteers.
Doors in Route	The total number of doors covered within the assigned route.
Route Number/Name.1	The identifier or name of the route assigned to the volunteers.
Time Spent	The total duration volunteers spent during the food drive activity.
# of Adult Volunteers	The number of adult volunteers who participated in the activity.

Table 1: Feature Information of EFD 2023 dataset

Description: This dataset comprises data collected via a Google Form during the Edmonton Food Drive 2023. Number of features: 13; Number of samples: 454

Column Name	Description
ID	A unique identifier assigned to each form submission.
Start time	The time the volunteer began filling out the form.
Completion time	The time the volunteer completed the form.
Email	The email address provided by the volunteer.
Name	The name of the volunteer.
How did you receive the form?	The method through which the volunteer received the form (e.g., email, link).
Email address	The contact email address for further communication.
Drop Off Location	The primary location where donations were dropped off.
Other Drop-off Locations	Additional locations where donations were dropped off.
Stake	The specific stake responsible for organizing the volunteer's participation.
Bonnie Doon Stake	Indicates involvement with the Bonnie Doon Stake.
Edmonton North Stake	Indicates involvement with the Edmonton North Stake.
Gateway Stake	Indicates involvement with the Gateway Stake.
Riverbend Stake	Indicates involvement with the Riverbend Stake.
Sherwood Park Stake	Indicates involvement with the Sherwood Park Stake.
YSA Stake	Indicates involvement with the Young Single Adults (YSA) Stake.
Route Number/Name	The identifier or name of the donation collection route.
Time Spent Collecting Donations	The total time spent collecting donations for the route.
# of Adult Volunteers who participated in this route	The number of adult volunteers involved in this specific route.
# of Youth Volunteers who participated in this route	The number of youth volunteers involved in this specific route.
# of Doors in Route	The total number of doors covered within the route.
# of Donation Bags Collected	The total number of donation bags collected from the route.
Did you complete more than 1 route?	Indicates whether the volunteer completed more than one route (e.g., Yes/No).
How many routes did you complete?	The total number of routes completed by the volunteer.
Additional Routes completed (2 routes)	Details about a second additional route completed, if applicable.
Additional routes completed (3 routes)	Details about a third additional route completed, if applicable.
Additional routes completed (3 routes)2	Details about another third route completed, if applicable.
Additional routes completed (More than 3 Routes)	Details about additional routes completed beyond three, if applicable.
Additional routes completed (More than 3 Routes)2	Further details about routes completed beyond three, if applicable.
Additional routes completed (More than 3 Routes)3	Further details about routes completed beyond three, if applicable.
Comments or Feedback	Any additional comments, suggestions, or feedback provided by the volunteer.

Table 2: Feature Information of EFD 2024 dataset

Description: This dataset comprises data collected via a Microsoft Form during the Edmonton Food Drive 2023. Number of features: 31; Number of samples: 653

Geographical Information Extraction: City of Edmonton Neighborhood Dataset

To complement the food drive data, the City of Edmonton Neighborhood Dataset [Link] was integrated into the analysis. This dataset provided geographic coordinates and neighborhood names, enabling a geospatial analysis of donation trends and route efficiency.

Geographical information was extracted from the Property Assessments dataset and merged to the Food Drive Data using the unique Neighborhood Names. This data was then used to generate maps that provide visual insights into neighborhood-level donation patterns and trends. The columns shown in Table 3 were specifically extracted for this purpose:

Column Name	Description
Neighborhood Name	The official name of the neighborhood in the City of Edmonton.
Latitude	The geographic coordinate specifying the north-south position of the neighborhood.
Longitude	The geographic coordinate specifying the east-west position of the neighborhood.

Table 3: Feature Information of City of Edmonton Neighborhood dataset

Description: The City of Edmonton Neighborhood Geographical coordinates data provides comprehensive information about neighborhood boundaries, demographics, land use, and other characteristics for urban planning and analysis. Number of features: 3; Number of samples: 427

This information was crucial for creating interactive geospatial visualizations and digitized route mapping for the Edmonton Food Drive

Exploratory Data Analysis

The collected data was cleaned and prepared for analysis to ensure accuracy and consistency. Key visualizations were generated to provide comparative insights, focusing on identifying trends and patterns in donation volumes, volunteer allocation, and route efficiency. Insights were limited to the data collected from the assigned drop-off centers, emphasizing the need for a more comprehensive data collection strategy in future drives. Our Exploratory Data Analysis strategy involved examining each feature individually and performing detailed analyses for each.

We conducted a comprehensive analysis of the Edmonton Food Drive data, focusing on uncovering patterns and relationships to improve the understanding of key variables and enhance future efforts. The analysis began by examining the frequency and distribution of drop-off locations, exploring their relationship with variables such as the number of donation bags collected, and the number of volunteers involved. The frequency of different "Stake" values was assessed, and their impact on numerical features, including the number of doors and donation bags, was closely analyzed.

Further, we explored time-related aspects, analyzing the frequency of various time categories and investigating how the time spent differed across "Stakes" and "Wards". The distribution of data across Wards was another area of focus, examining how specific Wards influenced other variables, such as the number of donation bags and routes. Volunteer participation was also analyzed, with particular attention given to the correlation between adult volunteers and other numerical features, as well as the overall distribution of volunteers across different areas.

The distribution of the number of doors was assessed in relation to categorical variables, and the average number of doors by "Stake" was calculated. Additionally, the relationship between donation bags and the number of routes was analyzed, comparing variations in donation bags across locations and Wards. Yearly trends were also explored, identifying changes in donation volumes and total volunteer numbers over time.

Through this analysis, we uncovered valuable insights into the relationships between drop-off locations, volunteers, and donation trends.

Data Refinement:

For the EFD 2024 dataset, we identified the following issues and applied the respective methods to address them.

Issues Detected	Refining Method
Too long column names	Rename column names for clarity
Inconsistent string formats	Removed leading and trailing spaces Converted to title format Removed unnecessary characters
Incorrect and inconsistent data types	Converted variables to the correct data types
Detected null values	Numeric Variables: Performed mean imputation to replace null values, preserving the dataset's distribution by using the feature's average. Categorical Variables: No null values detected
Detected empty values	Tagged empty categorical fields with placeholders (e.g., "Unknown Routes")
Duplicated values	Dropped duplicated values and columns
Too many irrelevant data	Dropped irrelevant columns
Identified outliers	Detected using IQR method and imputed using mean
Table 4: Identified Issues in the EFD 2024 Dataset and Their Respective Solutions

After performing data refining on the EFD 2024 dataset, we merged it with the EFD 2023 dataset and the City of Edmonton Neighborhood dataset. We used our final cleaned dataset for further analysis.

Data Visualization:

We created interactive visualizations using Tableau to make our EDA findings easy to understand. These visualizations allow users to explore the data and gain insights through dynamic charts and maps. The dashboard includes various charts and maps that present the key aspects of our analysis in a simple and clear way. Figure 2 shows the visualizations included in the dashboard that help support our overall analysis.

Key features of the dashboard include:

• KPI Card for Key Features: Displays the total number of donation bags, houses, routes, volunteers, and average time spent, based on the selected criteria.
• Total Number of Donation Bags by Ward: This map of Edmonton shows the distribution of donation bags across different wards, providing a clear comparison of how they are spread throughout the city.
• Leading 10 Wards in Efficiency: Highlights the top 10 wards with the highest efficiency, showcasing their performance across key metrics.
• Overall Volunteer Count: A bar chart comparing volunteer counts over different years, offering insights into trends and changes over time.
• Contribution Leaders by Ward: A heatmap showing the contributions from each ward, using color gradients to highlight the areas with the highest and lowest contributions.
• Donation Bags vs. Time Spent Chart: A visualization comparing the number of donation bags to the time spent, providing insights into the efficiency of the donation process.

Machine Learning

Before developing and evaluating machine learning models, we performed several data preparation steps to ensure high-quality inputs

Feature Engineering

To enhance the dataset, we introduced three new features:

• Total Volunteers: The sum of Total Adult Volunteers and Total Youth Volunteers.
• Donation Bags per Door: The number of donation bags divided by the number of doors.
• Donation Bags per Route: The number of donation bags divided by the number of routes.

Additionally, we applied one-hot encoding to the Wards feature to handle categorical data and ensure all variables were properly formatted for modeling.

Data Splitting and Normalization

We split the data into training and testing sets, using 2023 data for training and 2024 data for testing. This approach allowed us to validate model performance on unseen data. To maintain consistency across numerical features, we applied normalization, ensuring all values were on a comparable scale before feeding them into the models.

Model Development and Evaluation

Following data preparation, we implemented and tested six different machine learning models for two prediction tasks:

• Total number of donation bags.
• Time spent for each ward.

Each model was evaluated to identify the most accurate one for each prediction task. The results below summarize their performance and effectiveness.

Model	MSE	RMSE	MAE	R²	Adjusted R²
Linear Regression	3393.986256	58.257929	26.828851	-0.100185	-0.168338
Polynomial Regression	49.838645	7.059649	2.388835	0.983844	1.146869
Decision Tree Regression	2356.665557	48.545500	8.232945	0.236070	0.188747
Random Forest Regression	1990.524740	44.615297	8.457754	0.354757	0.314786
Gradient Boosting Regression	2144.987415	46.314009	8.164502	0.304687	0.261615
K-Nearest Neighbors Regression	3092.228686	55.607811	17.474875	-0.002368	-0.064461
Table 5: Performance Metrics for Models Predicting Total Donation Bags

Based on the results, the best model for predicting total donation bags is Polynomial Regression, as it achieves the lowest RMSE (7.059649) and MAE (2.388835) while attaining the highest R² score (0.983844), indicating a strong fit and high predictive performance.

Model	MSE	RMSE	MAE	R²	Adjusted R²
Linear Regression	1.583989	1.258566	0.917151	0.075887	2.771216
Polynomial Regression	0.708581	0.841772	0.634814	0.586608	1.014787
Decision Tree Regression	0.192435	0.438674	0.356527	0.887732	1.215181
Random Forest Regression	0.216073	0.464836	0.377927	0.873941	1.241613
Gradient Boosting Regression	0.256885	0.506838	0.391840	0.850131	1.287249
K-Nearest Neighbors Regression	0.278344	0.527583	0.394887	0.837612	1.311244
Table 6: Performance Metrics for Models Predicting Time Spent

For predicting time spent, the Decision Tree Regression model stands out as the best among the listed options. It achieves the lowest RMSE (0.438674) and MAE (0.356527), coupled with a high positive R² (0.887732) and Adjusted R² (1.215181), indicating superior accuracy and a strong fit to the data compared to the other models.

Model Optimization:

For the Polynomial Regression model used to predict total donation bags, we opted not to perform additional tuning to avoid the risk of overfitting. Since the metrics were already acceptable, with an R² score of 0.98, further increasing model complexity could lead to diminished generalization and overfitting the training data.

Advanced Analysis:

We used the Polynomial Regression and Decision Tree models to predict the number of donation bags and time spent per Ward for 2025. Below are some key insights based on the predicted values.

The predicted number of donation bags for next year shows a steady increase. Starting at 14,817 in 2023 and 14,751 in 2024, the total number of donation bags is expected to grow, reaching 16,600 in 2025.

The 12-month outlook for donation bags reveals the top and bottom-performing Stakes. The top three Stakes, which are expected to contribute the most to donation bags, are Gateway, Bonnie Doon, and Riverbend. On the other hand, the bottom three Stakes, contributing fewer donation bags, are YSA, Edmonton North, and Riverbend.

The 12-month outlook for donation bags reveals the top and bottom-performing Wards. The top 10 Wards expected to contribute the most donation bags are Lee Ridge, Crawford Plains, Silver Berry, Clareview, Blackmud Creek, Griesbach, Londonderry, Griesbach, Ellerslie, Rabbit Hill and Terwillegar. On the other hand, the bottom 10 Wards, which are projected to contribute fewer donation bags, include Mill Creek YSA, Lago Lindo, Onoway, Whitemud Creek YSA, Devon, Beaumont, Wild Rose, Wainwright, Windsor Park, and Pioneer. These insights show a notable variation in donation contributions across different Wards.

The top 3 Stakes with the highest effectiveness (i.e., they are expected to generate the most donation bags per unit of time spent) are Gateway, Riverbend and Bonnie Doon. On the other hand, the bottom 3 Stakes with the lowest effectiveness, meaning they are expected to have the least donation bags per unit of time spent, are YSA, Edmonton North, and Riverbend.

The top 10 Wards with the highest effectiveness, meaning they are expected to generate the most donation bags per unit of time spent, are Lee Ridge, Silver Berry, Clareview, Rio Vista, Woodbend, Coronation Park, Londonderry, Greenfield, Clareview, Blackmud Creek and Griesbach. These Wards are predicted to be more efficient in converting time spent into donation bags.

In contrast, the bottom 10 Wards with the lowest effectiveness, meaning they are expected to have the least donation bags per unit of time spent, include Mill Creek YSA, Lago Lindo Branch, Onoway, Whitemud Creek YSA, Devon, Beaumont, Strathcona Married Student, Wild Rose, Namao and Forest Heights. These Wards are projected to require more time to achieve similar numbers of donation bags, reflecting a lower efficiency in their donation efforts.

Deployment

The final application was divided into six sections: the Information Page, Dashboard Page, Trends Page, Donation Bags Prediction Page, Time Spent Prediction Page, and Route Mapping Application Page. Each page has a distinct feature designed to deliver specific insights and valuable information to its users, ensuring a comprehensive experience. Together, these sections allow users to easily navigate through different functionalities, making data-driven decisions more accessible and efficient. Figure 8 shows the application’s dashboard page.

The application was deployed on Tableau, where interactive visualizations were created to represent donation trends, volunteer participation, and route mapping insights.

• Route mapping was further enhanced using Hugging Face's Gradio, which allowed users to interactively explore donation routes.
• A chatbot, also embedded using Gradio, provided users with quick responses to queries related to routes and donation processes.

Route Mapping Application:

The Route Mapping Application was developed in response to the client's recurring challenges with generating accurate and efficient maps for volunteer routes. The previous process involved manually printing portions of the Edmonton map, highlighting routes by hand, and then distributing the maps to volunteers, which was time-consuming and prone to errors. This manual approach not only slowed down operations but also increased the risk of mistakes that could affect the efficiency of the donation collection process. Our application simplifies and automates route generation and visualization, enhancing overall efficiency, accuracy, and ease of use for volunteers. Below are images of the manually printed maps that were previously used, highlighting the need for this more efficient solution.

The application offers two modes: Fixed Mode and Custom Mode. The Fixed Mode aims to digitize the map generation process for our client, streamlining their workflow. Custom Mode, on the other hand, is designed for long-term planning, generating maps based on identified hot zones to enhance route efficiency.

To generate maps in Fixed Mode, the client only needs to select the desired ward and route, click "Submit," download the generated map, and then easily email it to the volunteers. This streamlined process eliminates the need for manual map creation, saving time and effort. The provided image shows the before and after results of generating maps using Fixed Mode, highlighting the efficiency and ease of the new approach.

This methodology not only highlights the strengths of the analysis but also shows areas for improvement in data collection and coverage to enhance future decision-making processes.

Results & Findings

The Edmonton Food Drive Project yielded several valuable insights and practical outcomes through the analysis and modeling of the collected data. These findings are categorized into key areas of operational improvement: Data collection, Data Analysis, Predictive Modelling, and Application Deployment.

Data Collection

Key Observations:

The data revealed notable year-over-year trends, with some Wards exhibiting consistent donation patterns, while others showed significant variability in donation volumes.

Belmead Ward, despite being the focus of detailed analysis, highlighted limitations in data completeness, as not all routes were accounted for due to the granularity of volunteer-reported data.

Data Analysis

Compared to the 2023 food drive, the 2024 results showed a decrease in several key metrics: the number of donation bags, number of volunteers, number of houses, and average time spent per route decreased by 0.4%, 38.17%, 38.17%, and 6.67%, respectively.

The top Stakes in 2024 remained largely consistent with 2023, with Gateway, Bonnie Doon, Riverbend, Edmonton North, and YSA leading the rankings. However, Riverbend and Bonnie Doon swapped positions, indicating a slight shift in their relative performance between the two years.

In 2024, Crawford Plains stayed in the top 5, just like in 2023. Some new Wards, like Terwillegar Park and Griesbach, joined the top ranks. On the other hand, Wards like Coronation Park, Drayton Valley, and Pioneer moved into the bottom 5 in 2024, replacing last year's bottom Wards like Devon and Mill Creek YSA.

Predictive Modeling

Prediction of Total Donation Bags

We developed six machine learning models to predict the total number of donation bags for each ward for 2025. Key insights from the model evaluation are summarized below:

Best Model: Polynomial Regression emerged as the most effective model, achieving the lowest RMSE (7.0596) and MAE (2.3888), coupled with the highest R² score (0.9838). This indicates excellent accuracy and consistency in predicting donation volumes.

Key Observations: Polynomial Regression outperformed other models, such as Random Forest and Gradient Boosting, due to its ability to capture non-linear relationships in the data effectively.

Prediction of Time Spent
For predicting the time required to complete donation routes, six models were evaluated. The following insights were observed:

Best Model: Decision Tree Regression provided the most accurate predictions, achieving the lowest RMSE (0.4387) and MAE (0.3565), along with a high R² score (0.8877) and Adjusted R² (1.2152). This model effectively balanced simplicity and performance.

Key Observations: Decision Tree Regression outperformed Polynomial Regression and Gradient Boosting for this task due to its flexibility in handling variations in the data, such as route complexities and volunteer differences.

Additionally, a geospatial analysis was integrated to design digitized donation route maps, identifying areas with the highest potential for donations. This task aimed to streamline logistics and maximize resource allocation in future drives.

We performed hyperparameter tuning on the Decision Regression model for predicting time spent, but it did not result in significant improvements. The tuned model achieved a Mean Squared Error (MSE) of 0.2041, Root Mean Squared Error (RMSE) of 0.4517, Mean Absolute Error (MAE) of 0.3652, R-squared (R²) of 0.8810, and Adjusted R-squared of 1.2282.

These visualizations from the model evaluation highlight the model's strengths, such as its low error rates and high predictive performance for most predictions, while also identifying areas, like residual biases, that could be optimized for better results.

The models successfully predicted both donation volumes and time spent, enabling stakeholders to make informed decisions for future food drives.

Application Deployment

The application was deployed to provide stakeholders with an interactive, user-friendly platform for predicting donation outcomes and enhancing logistics. The best performing model was deployed on Hugging Face's Gradio and embedded in Tableau to aid decision-making for future food drives.

Application Input Parameters for Prediction:

Ward: Selects the specific ward for which predictions are needed.
Time Spent (Minutes): Captures the estimated time volunteers spend completing routes.
Number of Doors: Inputs the total number of doors covered in the selected ward.
Number of Routes: Allows users to specify the number of routes included in the analysis.
Year: Enables predictions for future food drives, ranging from 2025 to 2030.
Total Volunteers: Specifies the number of volunteers assigned to the task.

The application uses the provided inputs to generate a Predicted Total Donation Bags value. This prediction helps stakeholders gauge the effectiveness of their planning and resource allocation for upcoming drives.

Challenges Faced:

The Edmonton Food Drive Project encountered several challenges that impacted data collection, analysis, and prediction accuracy. These challenges, though significant, provided valuable insights for improving future food drives.

Data Collection Limitations:

Due to resource constraints, data was collected only from select drop-off locations in Bearspaw, Londonderry, Riverbend, Gateway, and Bonnie Doon. This limited coverage resulted in incomplete datasets that did not fully represent all participating areas in Edmonton.

Multiple volunteers managing the same route and dropping off large numbers of donation bags led to incomplete or duplicate data entries, further complicating the accuracy of the collected data.

Inconsistencies in Dataset Structures:

The datasets for 2023 and 2024 contained discrepancies due to adaptations made in the new form to improve user entries. While these changes aimed to enhance usability, they introduced differences in feature structures, requiring significant effort to reconcile and standardize the data for analysis. Additionally, the absence of uniform data entry standards across Wards contributed to inconsistencies, creating additional challenges during preprocessing.

Prediction Discrepancies:

Predicted donation growth figures based on the collected data did not align with the client’s internal reports, which indicated an overall increase in donations in 2024 compared to 2023.

To address this discrepancy, data refilling was performed to adjust the 2024 figures and bring them closer to actual trends.

Operational Challenges:

The granularity of route information made it difficult to standardize data inputs across multiple Wards.
The lack of a centralized system for data entry led to variations in how data was recorded and submitted, further complicating the analysis.

Conclusions & Recommendations:

To enhance overall effectiveness, a more balanced allocation of volunteers should be considered, with a focus on both improving the performance of lower-performing areas and maintaining the momentum in top-performing wards and stakes. The following recommendations are proposed:

• Polynomial Regression is recommended for forecasting donation volumes, particularly when capturing complex patterns in historical data.
• Decision Tree Regression is ideal for predicting time spent, providing actionable insights for route optimization and volunteer allocation.

These predictions can guide planning and resource allocation by Identifying wards expected to generate the highest donation volumes and estimating the time required for volunteers to complete routes efficiently, improving logistical coordination.

Continued improvement in data collection processes (e.g., standardizing volunteer data and digitizing route information) will further enhance prediction accuracy and the utility of these models.

By combining data insights and predictive modeling, this project provides actionable recommendations for improving the logistics of Edmonton's Food Drive initiative.

The project successfully achieved its goals of recommending improvements in the food donation process in the Edmonton Food Drive. Tools to predict donation trends and time requirements were introduced, helping volunteers and organizers plan better. The route mapping application simplifies volunteer coordination and saves significant effort compared to the traditional manual processes. Additionally, interactive dashboards make it easier for stakeholders to understand and analyze the data, leading to better decision-making. Overall, the project streamlines operations and contributes to a more effective and efficient food donation drive.

If you have any questions about my article or would like to discuss this further, I invite you to Meet the Data Scientist, an event where authors meet the readers, present their topic, and discuss their findings.

Register for the Meet the Data Scientist event. We hope to see you there!

References

Edmonton's Food Bank Fundraising Efforts. (n.d.). Edmonton Journal.
https://edmontonjournal.com/news/local-news/edmontons-food-bank-fundraising

Where to Build Food Banks: A Machine Learning Approach. (n.d.). Purdue University.
https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1661&context=jpur

Edmonton’s Food Bank. (2024) Winter Gleanings 2024.
https://www.edmontonsfoodbank.com/documents/267/Winter_Gleanings_2024_-_Final.pdf

Food Bank Operations Web-Based Software. (n.d.). Gao Group, Cornell University.
https://gao.cee.cornell.edu/software-2/food-bank-operations-web-based-software/

Researchers Use Machine Learning to Assist State Food Pantries with Distribution. (2022). Auburn University Newsroom.
https://ocm.auburn.edu/newsroom/news_articles/2022/10/070927-researchers-machine-assists-food-pantries.php

Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach. (n.d.). arXiv.
https://arxiv.org/pdf/2410.15420

Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation. (2024). arXiv. https://arxiv.org/abs/2406.06363

Edmonton Food Drive Dashboard. (2024). Tableau Public.
https://public.tableau.com/app/profile/kendrick.kent.moreno/viz/EFD2024Dashboard/EFDDashboard-Main

Government of Alberta. (n.d.). Property assessments: Edmonton region. Alberta Regional Dashboard. https://regionaldashboard.alberta.ca/region/edmonton/property-assessments/#/?from=2018&to=2022

In this talk, we explore the role of survey weights and replicate weights in analyzing complex survey data. Using analogies and accessible language, we highlight the key intuitive ideas behind why these tools are essential for drawing sound statistical conclusions from survey data. To bridge theory and practice, we also review software options for working with survey data and demonstrate how to apply different types of weights—including cross-sectional, longitudinal, normalized (or standardized), and bootstrap weights.

Presenter: Claude Girard, Senior Methodologist, Data Analysis Resource Centre

To register for the English webinar, fill out the following form:

• Wednesday, December 10, 2025, 1:00pm - 2:00pm ET (10:00am – 11:00am PT / 11:00 – 12:00pm MT / 12:00pm – 1:00pm CT / 1:00pm – 2:00pm ET / 2:00pm – 3pm AT)

To register for the French webinar, fill out the following form:

• Wednesday, December 10, 2025, 11:00am - 12:00pm ET (8:00am - 9:00am PT / 9:00am - 10:00am MT / 10:00am - 11:00am CT / 12:00pm - 1:00pm AT)

If you participated in the Canadian Health Measures Survey (CHMS) between January 2023 and December 2024, we are now seeking your informed consent to allow the biospecimens you provided during the survey to be used in genetic health research projects. Your consent will help support important research into how human genetic information is linked to health outcomes.

What you need to know:

Your sample and genetics

DNA (deoxyribonucleic acid) is the molecule that contains genetic information. It is found in many parts of the body, including cells, skin and blood. DNA can be taken from your blood sample to help us understand what makes people sick and what keeps them healthy. Genetic information collected from your samples can be used in research and shared as combined data from groups of people (aggregated or pooled data). Your samples and genetic data will not be used or published at the individual level.

What is genetic research?

Genetic research studies human DNA to learn how genes and environmental factors affect health and disease. This research can help us discover what causes diseases, improve how they are detected and treated, and even help prevent them.

Keeping your data safe

To protect your privacy, each biological sample has a barcode instead of your name or personal details. Your samples are frozen and stored safely at the Statistics Canada Biobank in the Public Health Agency of Canada's National Microbiology Laboratory. This secure facility follows international standards. Strict security measures are in place to ensure the risk of anyone identifying you from your samples is very low.

Only authorized Statistics Canada employees can access your information when needed for their work. A small number of employees can connect the barcode on your sample to your personal information, which is stored separately at our head office. No one outside Statistics Canada will have access to your personal details.

Authorized researchers can use your samples for research, but only if they

1. apply for access to the samples
2. get approval from the Statistics Canada Biobank Advisory Committee.

This process ensures that your information is always protected. The approval process includes

• a research project application submitted to Statistics Canada
• a review by a committee of scientists, experts and ethicists to ensure the research adheres to all guidelines.

All health research is carefully monitored by the Health Canada and Public Health Agency of Canada Research Ethics Board, the Office of the Privacy Commissioner of Canada and the researcher's institutional ethics board. This ensures the research follows ethical guidelines and protects your privacy.

Statistics Canada follows strict privacy laws under the Statistics Act. Published research results will show only combined data from groups of people, and no personal or identifiable information will be shared.

Your right to withdraw

If you no longer want to participate in any part of this survey, including the genetic research, you can withdraw at any time. To remove your genetic samples from current or future research, you can send a written request by email to statcan.biobankinfo-infobiobanque.statcan@statcan.gc.ca. Please include your full name, the approximate date and home address at the time of your visit at the temporary examination centre, and your date of birth. This information will be used only to ensure that the correct samples are identified and properly destroyed. Please note that any data already used in research and published before your withdrawal request cannot be removed.

Genetic research can help identify genes linked to diseases. With the advances in genetic research, researchers have discovered and continue to discover genes or variations of genes that are associated with an increase in the risk of certain diseases. In future genetic research studies using your biological samples, if researchers discover genetic information that could impact your health, it will not be shared with you or your doctor. Your sample will be used solely for research purposes, and the findings will not influence your medical care. By consenting to the storage and use of your samples for genetic research, you understand that you will not receive any personal genetic results or health information.

Genetic research at the individual level

DNA is like a giant instruction book that tells your body how to grow, develop and function. Researchers can read this book using DNA sequencing, which helps them understand different things about you. To do so, researchers use a DNA sequencing laboratory method that analyzes your DNA, providing information about your genetic traits, the risks of getting certain diseases and other health-related characteristics. For this cycle of the CHMS, the publishing of genetic research involving data outputs at the individual level will not be permitted with your samples.

Genetic research at the population level

When genetic data are used at the population level, your results are combined with those of other CHMS participants. This means your personal information and individual results are not included. Any genetic data that could potentially reveal your identity or the identity of other participants are removed before research results are published. Here is a summary:

• Your DNA results will be included in statistical summaries (for example, showing the prevalence of certain genetic traits or diseases).
• Your genetic data will be combined with data from many other participants, with no personal details included.
• The data shared in research publications cannot be linked back to any individual, ensuring robust privacy protection.
• The goal of this research is to identify patterns and relationships within large groups of people, helping scientists better understand genetics and disease.

For more information, please visit

• Information for Survey Participants - Canadian Health Measures Survey (CHMS)
• Surveys and statistical programs - Canadian Health Measures Survey (CHMS) Cycle 7

Please use the secure access code included in the invitation you received from Statistics Canada to access our secure web portal and provide your consent.

In November 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.

SEC_R01 - The following questions are about your job security and employability in relation to your main job.

SEC_Q01 / EQ2 - To what extent do you agree or disagree with the following statements?

SEC_Q01_1 - You might lose your job in the next 6 months.

• Strongly agree 
• Agree 
• Neither agree nor disagree
• Disagree 
• Strongly disagree

SEC_Q01_2 - If you were to lose or quit your current job, it would be easy for you to find a job with a similar salary. 

• Strongly agree 
• Agree 
• Neither agree nor disagree
• Disagree 
• Strongly disagree

EMP_Q01 / EQ3 - In your job search, how helpful would your overall work experience and job skills be to get hired? 

Is it:

1. Very helpful 
2. Fairly helpful 
3. Not so helpful 
4. Not helpful at all

From October-December 2025, the following questions measuring the Labour Market and Socio-economic Indicators were added to the Labour Force Survey as a supplement.

The purpose of this survey is to identify changing dynamics within the Canadian labour market, and measure important socio-economic indicators by gathering data on topics such as type of employment, quality of employment, support payments and unmet health care needs.

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 and Socio-economic 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.
Employee block

The following questions test a new way of measuring temporary employment. Some questions address topics that were previously covered by the Labour Force Survey, but in a slightly different way.

LMI_Q01 / EQ 2 - What type of contract or agreement do you have in your main job?
Is it:

1. Permanent or until retirement
2. Ongoing with no specified end date
   Exclude temporary or seasonal contracts that are regularly renewed.
3. For a specific duration
   e.g., seasonal, term
   Include temporary or seasonal contracts that are regularly renewed.
4. Until a task or project is completed

LMI_Q02 / EQ 3 - Which of the following currently applies to your main job?

1. It is a seasonal job
   e.g., you only work during a specific season
2. You worked as an apprentice, trainee or intern in that job
   e.g., electrician apprentice, nursing trainee, police cadet, marketing intern, etc.
   OR
3. None of the above

LMI_Q03 / EQ 4 – In your main job, are you paid by a private employment or placement agency that is different from the company or organization you work for?

1. Yes, paid by a private placement agency
2. No

LMI_Q04 / EQ 5 - What is the total duration of your contract or agreement in your main job?
Is it:

1. Less than 3 months
2. From 3 months to less than 6 months
3. From 6 months to less than 12 months
4. 12 months or longer
   OR
5. Casual job with no specific end date

LMI_Q05 / EQ 6 - In your main job, do you have a specific number of hours you are supposed to work?

1. Yes
2. No

HRS_Q01 / EQ 7 – Are you at least guaranteed that you will get some work or hours in your main job?

Would you say:

1. Yes
2. No minimum number of hours guaranteed, contacted when needed

LMI_Q06 / EQ 8 - What would you say best describes your current situation in your main job?
You:

1. Work based on a series of successive contracts with the same employer
   e.g., your employer renews your contract
   Include situations with short breaks between contracts
2. Have a casual job with an employer that lets you choose when you work
   e.g., can decide which days or shifts you work.
3. Only work when called-in or assigned a shift by your employer
4. Work based on a series of successive contracts with different employers
   Include situations with short breaks between contracts
5. Work as a day labourer
   e.g., hired and paid by the day or for a single shift
6. Have received a permanent job offer
7. Will return to school or do something else at the end of your contract
8. re uncertain about your future contract situation
9. None of these

REAT_Q01 / EQ 9 – Do you want a permanent job at this time?

1. Yes
2. No

REAT_Q02 / EQ 10 - What is the main reason why you do not want a permanent job?
Would you say:

1. To combine employment with education
2. To combine employment with a pension
3. To combine employment with caring for children
4. To combine employment with other family or care responsibilities
5. Other reason

Self-employed block
You mentioned earlier that you are self-employed in your main job. The following section of the survey will refer to this as your main business.

LMI_Q07 / EQ 11 - What is the main reason why you are self-employed in your main job?
Is it:

1. To have autonomy and control over work hours, wage rate or location
2. Unable to find work as an employee
3. To earn more money than you would as an employee/ To earn extra money
4. To engage in work that you are passionate about
5. Lost job as an employee
6. To practice or master a new skill
7. To work in your field of expertise
8. To join or take over a family business
9. To achieve a better work-life balance
10. To experience less stress or for health reasons
11. Other

LMI_Q08 / EQ 12 – Do you have any partners or co-owners in your main business?

1. Yes
2. No

LMI_Q09 / EQ 13 – Do you own or lease a building or space dedicated to your main business?

1. Yes
2. No

LMI_Q10 / EQ 14 - In your main business, are you required to belong to a professional association or regulatory college to do your job?

1. Yes
2. No

LMI_Q11 / EQ 15 - Does your main business operate…?

1. All year round
2. During most of the year
3. During a specific season
4. Intermittently

EMP_Q01 / EQ 16 - How many employees in total work at your business?

1. 5 or less
2. 6 to 20
3. 20 to 99
4. 100 to 500
5. Over 500

LMI_Q12 / EQ 17 - What is the current mix of clients in your main business?
Is your main business:

1. Mostly based on getting new clients
2. Based on an equal mix of new and returning clients
3. Mostly based on returning clients
4. Based on a single client
5. OR
6. Your main business has not had any clients yet

LMI_Q13 / EQ 18 - Would you be able to continue operating your main business for the next five years based on returning or existing clients alone?

1. Yes
2. No

LMI_Q14 / EQ 19 - To what extent do you agree or disagree with the following statement?
In normal times, it is easy for you to find new clients in your main business.

1. Strongly agree
2. Agree
3. Neither agree nor disagree
4. Disagree
5. Strongly disagree

CLI_Q01 / EQ 20 – Do you currently have contracts with any of the following types of clients in your main business?

	Yes	No
Private businesses
Non-profit organizations or charities
Government agencies or departments
Private individuals

LMI_Q16 / EQ 21 – Thinking of your largest contract, what is the total duration of that contract?

Is it:

1. Less than 3 months
2. From 3 months to less than 6 months
3. From 6 months to less than 12 months
4. 12 months or longer

LMI_Q17 / EQ 22 - During the last 12 months, did you have any full days with no clients or work in your main business even though you wanted to work?

1. Yes
2. No

LMI_Q18 / EQ 23 - What would you say is your plan with your main business over the next 12 months?

Do you plan to:

1. Expand and hire more employees
2. Expand without hiring more employees
3. Keep things about the same
4. Scale-down the business
5. Stop working or close the business

LMI_Q19 / EQ 24 - What is the main reason why you expect to stop working or close your main business?

1. Low sales
2. Clients pay late or do not pay
3. Excess debt
4. Issues with suppliers
5. Lack of access to financing
6. Other business reasons
7. To accept a job with more income
8. To accept a job with more benefits
9. Attending school
10. Family responsibilities
11. Retirement
12. Health
13. Other personal reasons
14. Other

LFI_CHECK1 / EQ 25 - Last week, did you work at a job or business?

1. Yes
2. No

LFI_CHECK2 / EQ 26 - Last week, did you have a job or business from which you were absent?

1. Yes
2. No

LFI_CHECK3 / EQ 27 - Did you have more than one job or business last week?

1. Yes
2. No

LFI_CHECK4 / EQ 28 - Was this because you changed employers?

1. Yes
2. No

LFI_CHECK5 / EQ 29 - Have you ever worked at a job or business?

1. Yes
2. No

LFI_CHECK6 / EQ 30 - When did you last work?

• Year:
• Month:

LMI_Q20 / EQ 31 - Excluding your main job or business, have you earned any money by freelancing, doing a paid gig, or completing a short-term job or task during the last 12 months?

1. Yes
2. No

LMI_Q21 / EQ 32 - Was this freelancing, paid gig, or short-term task or job one of the jobs you had last week, or something else entirely?

1. Yes, one of the jobs or businesses you had last week
2. No, it was something else

EMP_Q02 / EQ 33 - Were you paid as an employee when you freelanced, did a paid gig, or got paid to do a short-term task or job in the last 12 months?

1. Yes, only as an employee
2. Yes, both as an employee and as a self-employed worker
3. No, only as a self-employed worker

LMI_Q24 / EQ 34 - When was the last time you freelanced, did a paid gig, or got paid to do a short-term task or job?

1. Last week or after
2. In the last 3 months, but before last week
3. In the last 3 to 6 months
4. In the last 6 to 12 months

SCC1_Q05 / EQ 35 - In the last 12 months, did you receive support payments from a former spouse or partner?

1. Yes
2. No

SCC1_Q10 / EQ 36 - What is your best estimate of the amount of support payments you received in the last 12 months?

SCC2_Q05 / EQ 37 - In the last 12 months, did you make support payments to a former spouse or partner?

1. Yes
2. No

SCC2_Q10 / EQ 38 - What is your best estimate of the total amount you paid in support payments in the last 12 months?

SCC3_Q05 / EQ 39 - In the last 12 months, did you pay for child care, so that you could work at a paid job?

1. Yes
2. No

SCC3_Q10 / EQ 40 - What is your best estimate, of the total amount you paid for child care in the last 12 months?

DSQ_Q01 / EQ 41 – Do you have any difficulty seeing?

1. No
2. Sometimes
3. Often
4. Always
5. Don't know

DSQ_Q02 / EQ 42 – Do you wear glasses or contact lenses to improve your vision?

1. Yes
2. No
3. Don't know

DSQ_Q03 / EQ 43 - With your glasses or contact lenses, which of the following best describes your ability to see?

1. No difficulty seeing
2. Some difficulty seeing
3. A lot of difficulty seeing
4. you are legally blind
5. you are blind
6. Don't know

DSQ_Q04 / EQ 44 - How often does this difficulty seeing/seeing condition limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q05 / EQ 45 – Do you have any difficulty hearing?

1. No
2. Sometimes
3. Often
4. Always
5. Don't know

DSQ_Q06 / EQ 46 – Do you use a hearing aid or cochlear implant?

1. Yes
2. No
3. Don't know

DSQ_Q07 / EQ 47 - With your hearing aid or cochlear implant, which of the following best describes your ability to hear?

1. No difficulty hearing
2. Some difficulty hearing
3. A lot of difficulty hearing
4. You cannot hear at all
5. You are Deaf
6. Don't know

DSQ_Q08 / EQ 48 - How often does this difficulty hearing/hearing condition limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q09 / EQ 49 – Do you have any difficulty walking, using stairs, using your hands or fingers or doing other physical activities?

1. No
2. Sometimes
3. Often
4. Always
5. Don't know

DSQ_Q10 / EQ 50 - How much difficulty do you have walking on a flat surface for 15 minutes without resting?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q11 / EQ 51 - How much difficulty do you have walking up or down a flight of stairs, about 12 steps without resting?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q12 / EQ 52 - How often does this difficulty walking limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q13 / EQ 53 - How much difficulty do you have bending down and picking up an object from the floor?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q14 / EQ 54 - How much difficulty do you have reaching in any direction, for example, above your head?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q15 / EQ 55 - How often does this difficulty bending down and picking up an object limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q16 / EQ 56 - How much difficulty do you have using your fingers to grasp small objects like a pencil or scissors?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q17 / EQ 57 - How often does this difficulty using your fingers limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q18 / EQ 58 - Do you have pain that is always present?

1. Yes
2. No
3. Don't know

DSQ_Q19 / EQ 59 – Do you also have periods of pain that reoccur from time to time?

1. Yes
2. No
3. Don't know

DSQ_Q20 / EQ 60 - How often does this pain limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q21 / EQ 61 - When you are experiencing this pain, how much difficulty do you have with your daily activities?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
5. Don't know

DSQ_Q22 / EQ 62 – Do you have any difficulty learning, remembering or concentrating?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q23 / EQ 63 - Do you think you have a condition that makes it difficult in general for you to learn? This may include learning disabilities such as dyslexia, hyperactivity, attention problems, etc.

1. Yes
2. No
3. Don't know

DSQ_Q24 / EQ 64 - Has a teacher, doctor or other health care professional ever said that you had a learning disability?

1. Yes
2. No
3. Don't know

DSQ_Q25 / EQ 65 - How often are your daily activities limited by this condition?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q26 / EQ 66 - How much difficulty do you have with your daily activities because of this condition?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
5. Don't know

DSQ_Q27 / EQ 67 - Has a doctor, psychologist or other health care professional ever said that you had a developmental disability or disorder? This may include Down syndrome, autism, Asperger syndrome, mental impairment due to lack of oxygen at birth, etc.

1. Yes
2. No
3. Don't know

DSQ_Q28 / EQ 68 - How often are your daily activities limited by this condition?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q29 / EQ 69 - How much difficulty do you have with your daily activities because of this condition?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
5. Don't know

DSQ_Q30 / EQ 70 – Do you have any ongoing memory problems or periods of confusion?

1. Yes
2. No
3. Don't know

DSQ_Q31 / EQ 71 - How often are your daily activities limited by this problem?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q32 / EQ 72 - How much difficulty do you have with your daily activities because of this problem?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
5. Don't know

DSQ_Q33 / EQ 73 – Do you have any emotional, psychological or mental health conditions?

1. No
2. Sometimes
3. Often
4. Always
5. Don't know

DSQ_Q34 / EQ 74 - How often are your daily activities limited by this condition?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q35 / EQ 75 - When you are experiencing this condition, how much difficulty do you have with your daily activities?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
5. Don't know

DSQ_Q36 / EQ 76 – Do you have any other health problem or long-term condition that has lasted or is expected to last for six months or more?

1. Yes
2. No
3. Don't know

DSQ_Q37 / EQ 77 - How often does this health problem or long-term condition limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q38 / EQ 78 – Do you have pain that is always present?

1. Yes
2. No
3. Don't know

DSQ_Q39 / EQ 79 - Do you also have periods of pain that reoccur from time to time?

1. Yes
2. No
3. Don't know

DSQ_Q40 / EQ 80 - How often does this pain limit your daily activities?

1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
6. Don't know

DSQ_Q41 / EQ 81 - When you are experiencing this pain, how much difficulty do you have with your daily activities?

1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
5. Don't know

UCN_Q005 / EQ 82 - During the past 12 months, was there ever a time when you felt that you needed health care, other than homecare services, but you did not receive it?

1. Yes
2. No

UCN_Q010 / EQ 83 - Thinking of the most recent time you felt this way, why didn't you get care?

1. Care not available in the area
2. Care not available at time required (e.g. doctor busy, away from office or no longer at that practice, inconvenient hours)
3. Do not have a regular health care provider
4. Waiting time too long
5. Appointment was cancelled
6. Felt would receive inadequate care
7. Cost
8. Decided not to seek care
9. Doctor didn't think it was necessary
10. Transportation issue
11. Other

UCN_Q015 / EQ 84 - Again, thinking of the most recent time, what was the type of care that was needed?

1. Treatment of a chronic physical health condition diagnosed by a health professional
2. Treatment of a chronic mental health condition diagnosed by a health professional
3. Treatment of an acute infectious disease (e.g. cold, flu and stomach flu)
4. Treatment of an acute physical condition (non-infectious)
5. Treatment of an acute mental health condition (e.g. acute stress reaction)
6. A regular check-up (including pre-natal care)
7. Care of an injury
8. Dental care
9. Medication / Prescription refill
10. Other

UCN_Q020 / EQ 85 - Did you actively try to obtain the health care that was needed?

1. Yes
2. No

UCN_Q025 / EQ 86 - Where did you try to get the service you were seeking?

1. A doctor's office
2. A hospital outpatient clinic
3. A community health centre
4. A walk-in clinic
5. An emergency department or emergency room
6. Other

What we Asked, What we Learned and What we Did

October 2025

Introduction

In September 2021 the NOC 2021 Version 1.0 was released. The NOC for 2026 will be released in December of 2026.The NOC is jointly developed by Employment and Social Development Canada (ESDC) and Statistics Canada (StatCan) and has been maintained in partnership since the first edition published in 1991/1992. Revision proposals are analyzed by internal working groups within StatCan and ESDC as part of our interdepartmental revision working group process.

The purpose of the NOC is primarily to provide a standardized framework to support consistent statistical information on Canadian occupations. It is accessorily used for employment-related program administration and for compiling, analyzing, and communicating occupational information, such as labour market data.

The NOC organizes the world of work—performed for pay or profit—into a manageable, understandable, and coherent system.

Revising the NOC 2021 Version 1.0

In line with good statistical practice, the NOC is reviewed and revised periodically to reflect changes in the labour market, ensuring continued relevance and accuracy. For the NOC, major revisions – which cover both "real changes" and "virtual changes" – are planned on a 10-year cycle. While "real changes" affect the scope of classification items and/or categories, and therefore impact the data collected and disseminated, "virtual changes" are meant only to capture job titles and clarifying existing descriptions. The release of NOC 2021 Version 1.0 marked a significant revision aligned with this 10-year cycle.

In addition to the 10-year cycle, a 5-year "virtual change" cycle is in place to support improved coding and understanding of occupational classifications. In exceptional cases, when there is consensus between StatCan and ESDC, both real and virtual changes may occur outside the regular revision cycles.

For the upcoming release of NOC 2026, a consensus was reached between Statistics Canada and ESDC to introduce real changes outside the standard 10-year cycle – this marks a first attempt to enhance responsiveness to labour market shifts between revision cycles. The NOC 2026 will include real changes aimed at improving data collectability and reportability. Following its release, both organizations will assess the feasibility of incorporating real changes into the regular 5-year revision cycle moving forward.

In January 2024, Statistics Canada's Social Standards Steering Committee (SSSC) approved a permanent consultation process for the NOC. Proposals for changes can now be submitted and reviewed on an ongoing basis. A cut-off date for considering proposed changes for inclusion in a new version of the NOC will be posted well in advance.

The NOC consultation webpage was launched in April 2024. For NOC 2026 Version 1.0, the cut-off date for submissions was set as November 15, 2024.

What we asked

The consultation aimed to gather input from NOC users on potential revisions. Proposed changes could encompass any element of the classification, including the structure of the classification, definitions, and details such as the main duties, employment requirements, examples and exclusions attached to unit groups.

In addition to internal feedback from StatCan's and ESDC's NOC working groups, organisations and individuals were invited to submit proposals. Some participated in workshops to provide additional input focused on the following occupational categories:

• Health care Services
• Childcare and community services
• Applied Sciences
• Law, education, social, community and government services
• Food and Beveridge services
• Transportation officers and controllers
• Agriculture
• Forestry and forest firefighting services

Community input was also sought to update Indigenous occupation titles and duty descriptions.

Engagement and Outreach activities included:

• Posting the NOC 2026 Version 1.0 consultation notice on:
  • Government of Canada's Consulting with Canadians
  • Statistics Canada's Consulting Canadians and Standards websites
• Public consultation period at StatCan: April to November 2024.
• ESDC maintained an invitation-based submission process since the release of the NOC 2021.
• StatCan focal points such as provincial/territorial statistical departments as well as occupation and labour market related organizations and associations were also invited to provide feedback.
• Feedback was gathered through:
  • Public submissions
  • Meetings with organizations, associations and unions
  • Ad hoc submissions via ESDC and StatCan inboxes
  • Internal StatCan working group with subject matter experts and methodologists using the NOC.

What we learned

The interdepartmental NOC working group received over 150 change requests from:

• All levels of government
• Businesses
• Sector councils
• Industry and professional bodies/associations
• Unions
• Academics
• Individuals

Overview of proposed changes

Feedback included both virtual (content only) and real (data impacted) changes. These proposed revisions reflect the evolution of existing occupations and the emergence of new ones.

Proposed virtual changes included:

• Improving occupation descriptions
• Adding job titles
• Revising main duties
• Clarifying and updating employment requirements

Proposed real changes included:

• Creating new unit groups
• Transferring items between unit groups

Some proposals fell outside the scope of statistical classification principles, such as:

• Adding occupational groups to create career paths between TEER (Training, Education, Experience and Responsibilities) levels

Proposed changes grouped by Broad Occupational Categories (BOC)

The NOC includes ten Broad Occupational Categories (BOCs), representing the highest level of the classification system and numbered from 0 to 9. Each BOC is defined by a combination of the type of work performed, the field of study, and the industry of employment.

Broad Occupational Categories	Percentage of proposed changes
BOC 0 - Legislative and senior management occupations This broad category comprises legislators and senior management occupations.	4%
BOC 1 - Business, finance and administration occupations This broad category comprises specialized middle management managers and occupations in administrative services, financial and business services and communication (except broadcasting).	12%
BOC 2 - Natural and applied sciences and related occupations This broad category comprises occupations in natural sciences (including basic and applied sciences and experimental development), engineering, architecture and information technology.	15%
BOC 3 - Health occupations This broad category comprises specialized middle management managers and occupations in health care, health care services and support to health services.	11%
BOC 4 - Occupations in education, law and social, community and government services This broad category comprises managers in public administration and occupations concerned with law, social and community services, public protection services and teaching.	22%
BOC 5 - Occupations in art, culture, recreation and sport This broad category comprises specialized middle management managers, professional, technical and support occupations in art, culture, recreation and sport.	8%
BOC 6 - Sales and service occupations This broad category comprises middle management managers and sales and service occupations in wholesale and retail trade, and customer services.	8%
BOC 7 - Trades, transport and equipment operators and related occupations This broad category comprises middle management managers and occupations in trades, transportation and equipment.	5%
BOC 8 - Natural resources, agriculture and related production occupations This broad category comprises middle management managers and occupations in natural resources, agriculture and related production.	5%
BOC 9 - Occupations in manufacturing and utilities This broad category comprises middle management managers and occupations in manufacturing and utilities.	8%

What we did

Following good practices, all changes implemented in our classifications are guided by statistical classification principles and concepts^{Footnote 1}, including the documentation of types of changes aligned with the General Statistical Information Model (GSIM). These principles form the foundation for developing, implementing, and revising statistical classifications.

Stakeholders and public recommendations are assessed using the same rigorous standards. To support a thorough evaluation, the NOC team and working group require detailed information demonstrating alignment with the key classification principles. While changes are typically drafted to minimize disruption to existing data structures, disruptive changes are implemented when justified to maintain the integrity and relevance of the classification.

This approach applies to:

• Revising existing groupings or groups
• Creation of new groups
• Movement or placement of groups within the classification structure
• General content updates.

We reviewed stakeholder submissions and conducted follow-up meetings when additional information was required or requested. Revisions to the NOC for 2026 reflect the collaborative efforts and insights of stakeholders and working group members, ensuring the classification remains relevant, responsive to user needs and accurately represents the evolving Canadian labour market.

What changes

A total of 165 unit groups were impacted by real and virtual changes.

• 18 unit groups underwent real (structural) changes, in addition to virtual changes

Real changes included:

• Creation of new unit groups affecting BOC 4 and BOC 6
• Splits-offs (existing item split-off to emerging group) affecting BOC 1, BOC 4 and BOC 6
• Take-overs (item expires and moved to existing group(s) affecting BOC 6
• Transfers (item moved between unit groups) affecting BOC 1, BOC 2, BOC 3, BOC 4, BOC 5, BOC 6, and BOC 8

Certain aspects of the classification were revised to better align with available data and reporting practices. However, the extent of changes was limited by data granularity, as further disaggregation would have resulted in non-reportable or suppressed data due to confidentiality or quality concerns.

For example,

• Modifications were made to improve coherence with observed data trends, though some potential refinements were not feasible due to limitations in data availability at more detailed levels.
• The classification was adjusted where possible, balancing analytical needs with the constraints of data reporting.
• Updates reflect a compromise between ideal classification structure and practical reporting limitations, particularly where finer breakdowns would compromise data reliability or confidentiality.

Although most changes occur at the 5^th level (unit groups) of the NOC – the most detailed level of the classification – updates at this level also affect higher levels of the classification. A full overview of changes across all levels will be available in the NOC 2021v1.0 to NOC 2026V1.0 correspondence table, to be released in December 2026.

The remaining 147 unit groups were subject to virtual (content-only) changes aimed at improving clarity and maintaining occupational relevancy. These updates included:

• Revised titles and/or definitions
• New, revised or suppressed example job titles
• Updates to definitions, lead statements, main duties, employment requirements and exclusions.

Virtual changes affecting all BOCs:

• Improved occupation descriptions and titles
  • Example: The unit group title and lead statement for 21211 – Data Scientists were updated to clarify the appropriate coding for data analysts.
• Addition of job titles
  • Example: A specific surveying technologist title was added to 22213 - Land survey technologists and technicians to better guide coding within this unit group.
• Revision of main duties
  • Example: The main duties, and employment requirements for 11100 – Financial Auditors and Accountants were revised to more accurately reflect the regulated title and responsibilities of Chartered Professional Accountants.
• Clarification and update of employment requirements
  • Example: The employment requirements for 31202 – Physiotherapists were revised to clarify the necessary degree and credential requirements.

Appendix: Governing principles and underlying concepts and criteria

Statistical Classification Principles

Principle 1: Follow internationally accepted definitions and guidelines on how to classify occupations and jobs as a statistical unit (also see National Occupational Classification - Introduction). Because the purpose of the NOC is primarily to provide a framework to support consistent statistical information on Canadian occupations, it is important to specify the scope of each category in the classification. By following standard definitions and coding practices, Principle 1 support consistent and sound statistics to be produced and disseminated. The NOC team and working group uses this information to evaluate whether proposed changes are properly placed in the classification structure.

Principle 2: Respect of the internationally recognized statistical classification principles, being:

• well defined universe: categories at each level of the classification structure must reflect a well-defined universe or scope;
• classification is exhaustive: it covers all possible elements in the universe even if all examples of such universe are not provided in the publication;
• categories are mutually exclusive: no overlapping in the scope of each classification item or category (to avoid double counting);
• classification structure is hierarchical: lower categories are dependent of their higher categories;
• classification structure is rectangular: the classification has a code represented at every level across its whole structure, regardless of the scope of each category;
• classification is comparable to other classifications (of the same domain);
• classification categories are empirically significant;
• classification is organized around one or few concepts (e.g., job; occupation);
• classification contains groupings meaningful to users;
• classification is widely adopted.

Principle 3: the classification is related to data that is collectible and publishable (collectability and reportability): whether data can be collected and reported on the occupational grouping. For a detailed occupation to be included in the NOC and expecting statistics to come out of it, Statistics Canada must be able to collect and report data, otherwise, categories will not provide opportunities to produce relevant statistics. Statistics Canada is responsible for producing data across the entire range of occupations in Canada and conducts comprehensive surveys that collect occupation and labour market data.

Collectability and reportability are partly a function of the size of the occupational grouping and other measure of empirical significance (meaning the occupation must be large enough to be detected in sample of surveys). In evaluating collectability and reportability, however, the NOC team and working group will not use a specific occupation size cut-off. This is because occupations that are concentrated in certain industries or geographic areas may be collectable and reportable, while occupations of similar or larger size that are spread throughout the economy may not be collectable and reportable. Therefore, size is not the only consideration in collectability and reportability. Collectability and reportability are also related to the type of data collection used by surveys or statistical programs.

Principle 4: the classification supports the maintenance of time series continuity to the extent possible; that is, the ability to maintain data series over time without interruption due to classification changes. To the extent possible, new occupational categories proposed for the current version of the NOC and beyond should be easily linked by appropriate correspondence to previous version the NOC (e.g., NOC 2021 to NOC 2016 and NOC 2016 to NOC 2011).

Guidelines developed by Statistics Canada provided for the launch of the permanent consultation process for the NOC will assist users and the NOC team and working groups in consistently making changes to the NOC.

Principle 5: the classification continues to be relevant, that is, it must be of analytical interest, result in data useful to users, and be based on appropriate statistical research, subject-matter expertise and administrative relevance aligned with statistical classification principles and needs.

Principle 6: the prevalence of classification principles and statistical needs: the NOC is designed primarily for statistical purposes. Although there can be various uses of the NOC for non-statistical purposes (e.g., for administrative, regulatory, or policy functions), the requirements of government agencies or private users that choose to use the NOC for non-statistical purposes are responsible for such use of the classification. As a result, the NOC team reviews comments and develops its recommendations based on established statistical classification principles and guidelines. Information provided unrelated to the accurate gathering of information for statistical purposes, such as perceived importance or visibility of an occupation, does not determine the NOC team recommendations. Similarly, the volume of comments does not determine what the recommendations will be, and just submitting a request for a change does not automatically result into a change in the NOC.

Underlying Concepts and Classification Criteria of the NOC

The statistical unit

The basic principle of the NOC is the kind of work performed. The statistical unit or object being classified using the NOC is the concept of a "job". A job encompasses all the tasks carried out by a particular person to complete their duties. A job title represents the name given to a job or a position. The term job is used in reference to employment or in self-employment.

An occupation is defined as a collection of jobs, sufficiently similar or identical in work or tasks performed to be grouped under a common label for classification purposes.

The scope of the classification

The scope of the NOC is all occupations and jobs in the Canadian labour market undertaken for pay or profit, including people who are self-employed.

The NOC is not designed to include work or tasks not undertaken for pay or profit, for example, voluntary work. However, a person may complete work not for pay or profit where the tasks completed may be described within some occupational groups.

The Criteria

The structure of the NOC is based on two key criteria:

1. Broad Occupational Category (BOC): Defined by the type of work performed, the field of study, or the industry of employment—often a combination of these factors.
2. TEER (Training, Education, Experience, and Responsibilities): Reflects the training (formal and on-the-job), qualifications and responsibilities required to competently perform the tasks associated with an occupation.

These criteria determine the placement of occupations within the classification and guide decisions to merge, split, or create new groups^{Footnote 2}.

Within unit groups, distinctions between occupations are based on differences in tasks performed. In most cases, all occupations within a unit group share the same TEER level. A split is justified only when the resulting occupations differ significantly in TEER levels and/or have distinct duties, ensuring the principle of mutual exclusiveness is upheld. A merger is justified when labour market changes result in limited ability to collect and report data on an occupation, making it more practical to combine it with another group to maintain statistical integrity and usability.

Resources

• Calgary-based Strathcona Resources Ltd. announced on October 10th that it was terminating its take-over bid for MEG Energy Corporation.
• Calgary-based Cenovus Energy announced it had acquired 8.5% of MEG Energy's common shares. Later, Cenovus announced it had acquired additional MEG shares, brining its total to 9.8% of MEG shares issued and outstanding.
• Calgary-based Kiwetinohk Energy Corp. and Cygnet Energy Ltd. announced they had entered into an arrangement agreement under which Cygnet would acquire all of the issued and outstanding common shares of Kiwetinohk for an enterprise value of $1.4 billion. The companies said closing would occur after December 16th, subject to satisfaction or waiver of all conditions, including required shareholder approvals, court approval and customary closing conditions.
• Burnaby, British Columbia-based Interfor Corporation announced it would further temporarily reduce lumber production in the fourth quarter of 2025 across its operations in British Columbia, Ontario, the U.S. Pacific Northwest, and the U.S. South. Interfor said that these curtailments were expected to reduce lumber production by approximately 250 million board feet, or 26%, as compared to the second quarter of 2025.
• Pennsylvania-based Westinghouse Electric Company, Cameco Corporation of Saskatoon, and Brookfield Asset Management of New York announced that the United States Government had entered into a strategic partnership to accelerate the deployment of nuclear power and that at the center of the new strategic partnership, at least USD $80 billion of new reactors would be constructed across the United States using Westinghouse nuclear reactor technology.

Minimum wage

• Manitoba's minimum wage increased from $15.80 to $16.00 per hour on October 1st.
• Nova Scotia's minimum wage increased from $15.70 to $16.50 per hour on October 1st.
• Ontario's minimum wage increased from $17.20 to $17.60 per hour on October 1st.
• Prince Edward Island's minimum wage increased from $16.00 to $16.50 per hour on October 1st.
• Saskatchewan's minimum wage increased from $15.00 to $15.35 per hour on October 1st.

Wildfires

• The Government of Nova Scotia announced on October 1st that wildfire season was extended provincewide to October 31st due to continued fire activity in the province. The Government said the full burn ban would remain in effect in Annapolis County and daily burn restrictions would continue to apply in all other counties to the end of the month.
• The Government of Quebec announced on October 21st it had completely lifted the ban on open fires in or near the forest due to the significant amounts of rain that had fallen. The Government said the measure had been in force since September 30th.

Other news

• The Government of Canada announced relief to support Canadian businesses affected by the countermeasures Canada had announced in response to the tariffs imposed by the United States and that:
  • the exemption for U.S. goods used in manufacturing, processing, or food and beverage packaging had been extended for an additional two months, and now included goods used in agricultural production;
  • the temporary exemption from tariffs on imports of U.S. goods that are used to support public health, health care, public safety, and national security objectives had also been extended for an additional two months; and
  • further relief from Canada's tariffs on imports from the U.S. and China had now been implemented for companies that met strict conditions such as demonstrating short supply or existing contractual obligations.
• The Government of Canada announced reductions to the import quotas of General Motors (GM) and Stellantis following the automakers' decisions to scale back their manufacturing presence in Canada. The Government said it was reducing GM's annual remission quota by 24.2%, and Stellantis' annual remission quota by 50%.
• The Government of Ontario announced it was investing $1 billion in small modular reactors (SMRs) at Darlington Nuclear Station. The Government said construction on the first SMR began in May 2025, with the SMR expected to come online in 2030. The Government also said that the Federal Government was investing $2 billion.
• The Bank of Canada lowered its target for the overnight rate by 25 basis points to 2.25%. The last change in the target for the overnight rate was a 25 basis points cut in September 2025.
• TD Canada Trust, RBC Royal Bank of Canada (RBC), BMO Bank of Montreal, Canadian Imperial Bank of Commerce (CIBC), Scotiabank, and Laurentian Bank of Canada announced they were decreasing their Canadian dollar prime lending rates by 25 basis points from 4.70% to 4.45%, effective October 30th.
• The Canadian Union of Postal Workers (CUPW) announced that starting October 11th it would move from a nation-wide strike action to rotating strikes. Canada Post said plans were now under way to ensure a safe and orderly restart of its national operations, which were shut down on September 25th.
• The Alberta Teacher's Association (ATA) announced on October 6th that 51,000 teachers had gone on strike. The Government of Alberta announced on October 3rd that ahead of the teacher strike, it was introducing more supports for families and students, including increasing October funding rates for eligible children in grades 1 to 6 who were attending out-of-school care full time. On October 27th, the Government of Alberta announced it had proposed Bill 2, the Back to School Act, that, if passed, would end the province-wide teachers' strike and legislate a new collective agreement. On October 28th, the ATA announced that given the imminent passage and coming into force of Bill 2, the Association would instruct teachers to return to their classrooms.
• Oshawa-based General Motors of Canada announced the end of production of the BrightDrop electric delivery van built at CAMI Assembly in Ingersoll, Ontario. GM said BrightDrop production would not be moved to another site and had been suspended since May 2025.

United States and other international news

• The U.S. Federal Open Market Committee (FOMC) lowered the target range for the federal funds rate by 25 basis points to 3.75% to 4.00%. The last change in the target range was a 25 basis points cut in September 2025. The Committee also said that it had decided to conclude the reduction of its aggregate securities holdings on December 1st.
• The Reserve Bank of New Zealand (RBNZ) lowered the Official Cash Rate (OCR), its main policy rate, by 50 basis points to 2.50%. The last change in the OCR was a 25 basis points cut in August 2025.
• The Bank of Japan (BoJ) announced it will encourage the uncollateralized overnight call rate to remain at around 0.50%. The last change in the uncollateralized overnight call rate was a 25 basis points increase in January 2025.
• The European Central Bank (ECB) left its three key interest rates unchanged at 2.00% (deposit facility), 2.15% (main refinancing operations), and 2.40% (marginal lending facility). The last change in these rates was a 25 basis points reduction in June 2025.
• The eight OPEC+ countries - Saudi Arabia, Russia, Iraq, UAE, Kuwait, Kazakhstan, Algeria, and Oman - announced they would implement a production adjustment of 137 thousand barrels per day from the 1.65 million barrels per day additional voluntary adjustments announced in April 2023. OPEC+ said this adjustment would be implemented in November 2025.
• Nebraska-based Berkshire Hathaway and Occidental Petroleum Corporation of Texas announced a definitive agreement for Berkshire Hathaway to acquire Occidental's chemical business, OxyChem, in an all-cash transaction for USD $9.7 billion. The companies said the transaction is expected to close in the fourth quarter of 2025, subject to regulatory approvals and other customary closing conditions.
• California-based AMD and OpenAI announced a 6 gigawatt agreement to power OpenAI's next-generation AI infrastructure across multiple generations of AMD Instinct GPUs. The companies said that under the agreement, OpenAI would work with AMD as a core strategic compute partner to drive large-scale deployments of AMD technology and that the first 1 gigawatt deployment of AMD GPUs is set to begin in the second half of 2026.
• The Artificial Intelligence Infrastructure Partnership (AIP), MGX of the United Arab Emirates, and Blackrock's Global Infrastructure Partnership of New York announced they will acquire 100% of the equity in Texas-based Aligned Data Centers for an implied enterprise value of approximately USD $40 billion. AIP said the transaction is expected to close in the first half of 2026, subject to regulatory approvals and customary closing conditions.
• Ohio-based Fifth Third Bancorp and Comerica Incorporated of Texas announced they had entered into a definitive merger agreement under which Fifth Third will acquire Comerica in an all-stock transaction valued at USD $10.9 billion. The banks said the transaction is anticipated to close at the end of the first quarter of 2026, subject to shareholder and customary regulatory approvals and closing conditions.
• California-based Meta announced that it and funds managed by Blue Owl Capital of New York had entered into a joint venture agreement which will develop and own the Hyperion data center campus in Louisiana. Meta said the parties had committed to fund their respective pro rata of the approximately USD $27 billion in total development costs for the buildings and long-lived power, cooling, and connectivity infrastructure at the campus.
• Netherlands-based Stellantis announced plans to invest USD $13 billion over the next four years to grow its business in the United States market and to increase its domestic manufacturing footprint. Stellantis said the investment will support the addition of more than 5,000 jobs at plants in Illinois, Ohio, Michigan, and Indiana.
• Switzerland-based Nestlé S.A. announced it planned to reduce its global headcount by around 16,000 over the next two years. Nestlé said the reduction includes around 12,000 white-collar professionals across functions and geographies and a further 4,000 as part of ongoing productivity initiatives in manufacturing and supply chain.
• Switzerland-based Novartis AG announced that it had entered into an agreement to acquire Avidity Biosciences, Inc., a California-based biopharmaceutical company, for total consideration of approximately USD $12 billion. Novartis said it expects the merger to close in the first half of 2026, subject to customary closing conditions.

Financial market news

• West Texas Intermediate crude oil closed at USD $60.98 per barrel on October 31st, down from a closing value of USD $62.37 at the end of September. Western Canadian Select crude oil traded in the USD $44.00 to $51.00 per barrel range throughout October. The Canadian dollar closed at 71.34 cents U.S. on October 31st, down from 71.83 cents U.S. at the end of September. The S&P/TSX composite index closed at 30,260.74 on October 31st, up from 30,022.81 at the end of September.