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Data sources
Description of variables
Mapping techniques

Data sources

The Incident-based Uniform Crime Reporting Survey (UCR2)

The incident-based UCR2 survey captures detailed information on individual criminal incidents reported to police, including characteristics of victims, accused persons and incidents. The Winnipeg Police Service has been reporting to the UCR2 since 2000.

The UCR2 Survey allows for a maximum of four offences committed during the same criminal incident to be recorded in the data base. The selected offences are classified according to their level of seriousness, which is related to the maximum sentence that can be imposed under the Criminal Code.

Analyses of broad offence categories (e.g., total offences against the person, total property offences, total drug-related offences and total other Criminal Code offences) undertaken in this study are based on the most serious offence in each incident. This coincides with the crime rates published annually by the CCJS, which are based on the most serious offence in each police-reported incident. In classifying offences this way, a higher priority is given to violent offences than to non-violent offences. As a result, less serious offences may be under-represented when only the most serious offence is considered.

The majority of analyses undertaken in this study are based on broad categories of crime such as violent and property crime, which are based on a count of the most serious offence. However, in some analyses individual offence types are examined. In these cases, all incidents in which the offence was reported are included. For example, Table 1 provides information on selected individual offence types including theft under $5,000, theft over $5,000, car theft, shoplifting, break and enter, drug offences, mischief, arson, prostitution, robbery, common assault, sexual assault, homicide and serious assault. For these specific offence types, all incidents in which the offence was reported are included, regardless of the seriousness of the ranking given to the offence in the incident. This method provides a more complete representation of the distribution of individual offence types.

This study includes most Criminal Code offences, but excludes offences under other Federal, Provincial and Municipal statutes with the exception of the Controlled Drug and Substances Act. Also excluded are Criminal Code offences for which there is either no expected pattern of spatial distribution or a lack of information about the actual location of the offence. For example, administrative offences including bail violation, failure to appear and breach of probation are typically reported at court locations; threatening or harassing phone calls are often reported at the receiving end of the call; and impaired driving offences may be more likely to be related to the location of apprehension (for example, apprehensions resulting from road-side stop programs). In total, roughly 7,000 offences were excluded.

The Census of Population

On May 15, 2001, Statistics Canada conducted the Census of Population to develop a statistical portrait of Canada and its people. The Census of Population provides the population and dwelling counts not only for Canada but also for each province and territory, and for smaller geographic units such as cities or districts within cities. The Census also provides information about Canada’s demographic, social and economic characteristics.

The detailed socio-economic data used in this study is derived from the long form of the Census, which is based on a 20% sample of households. These data exclude the institutional population, which includes individuals living in hospitals, nursing homes, prisons and other institutions.

City of Winnipeg Zoning Data

Zoning data from the City of Winnipeg’s Planning, Property and Development Department were used to calculate the proportion of the area within neighbourhoods designated as either commercial, multiple-family residential or single-family residential land-use zones. Individual zoning parcels defined by City by-laws1 were aggregated to the neighbourhood level in order to calculate proportions.

Zoning data were also included for parcels in the downtown core. In these areas in particular, zoning types were frequently overlapping such that the same parcel of land could be zoned as commercial and residential (e.g., multiple-family) in cases where buildings served mixed purposes. Since historical data are not available, the zoning data used in this study are based on current (2003-04) information from the City of Winnipeg.

Description of variables

Crime variables

While selected individual offence types are displayed in tables and maps, analyses exploring the relationship between crime and neighbourhood characteristics are limited to the broad offence categories of violent and property crime to maximize the number of incidents being considered.

For this report, rates of both violent and property crime are calculated based on the “population at risk” rather than the residential population alone (see Text Box 2 for an explanation of this calculation). Violent crime includes homicide, attempted murder, sexual assault, assault, violations resulting in the deprivation of freedom, robbery, extortion, criminal harassment, explosives causing death or bodily harm, uttering threats and other violent violation. Property crime includes arson, break and enter, theft under $5,000, theft $5,000 and over, possessing stolen goods, fraud and mischief.

2001 Census of Population variables

Socio-economic disadvantage variables

Socio-economic disadvantage was derived from the set of five variables listed below. Boyle and Lipman (2002) found this composite variable to be linked to delinquent or problem behaviour in a Canadian sample of children and youth. Moreover, inequality of socio-economic resources across US cities has been demonstrated to be strongly associated with the spatial distribution of crime (Morenoff, Sampson and Raudenbush 2001).

Based on the approach taken by Boyle and Lipman (2002), the five socio-economic disadvantage variables were standardized to have a mean of 0 and a standard deviation of 1 (z-score). The Disadvantage Score was calculated by taking an unweighted average of the five standardized variables. The variables are highly correlated and yield an Alpha coefficient of 0.81 which reflects a high degree of internal consistency between the variables, and suggests that the variables successfully measure the same concept.

  • Percent of population receiving government transfer payments including Employment Insurance; Old Age Security including Guaranteed Income Supplement and Spousal Allowance; Net Federal Supplements; Canada and Quebec Pension Plan benefits; Child Tax Benefit; New Brunswick, Quebec, Alberta and British Columbia Family Allowance; Goods and Services Tax Credit; Workers’ Compensation; Social Assistance; and provincial/territorial Refundable Tax Credits.
  • Percent of neighbourhood population aged 20 years and older without a secondary school certificate.
  • Percent of neighbourhood population in private households with low income in 2000. Low income refers to private households who spend 20% more of their disposable income than the average private household on food, shelter and clothing. Statistics Canada’s low-income cut-offs (LICOs) are income thresholds that vary according to family and community size. Although LICOs are often referred to as poverty lines, they have no official status as such.
  • Neighbourhood unemployment rate for population aged 15 and older participating in the labour force.
  • Median household income in $1,000s or the dollar amount above and below which half the cases fall, the 50th percentile.

Population characteristic variables

  • Males aged 15-24 years as a percentage of the total neighbourhood population. This age group represents the highest risk age group for offending (see Figure 2). In Winnipeg in 2001 about 35% of all identified accused were males aged 15-24 years who were responsible for 26% of reported violent offences and 43% of property crimes.
  • Percent of neighbourhood population aged 65 years and over. Results from the General Social Survey on Victimization suggest that Canadian rates of criminal victimization among the elderly are relatively low compared to the population as a whole, though they report feeling less safe (Besserer and Trainor 2000).
  • Percentage of the neighbourhood population immigrating to Canada between 1991 and 2001. Initially, immigration may hinder integration into society; however this condition decreases with the length of residence in the country (Breton 2003). Recent immigrants may be more likely to face reduced social participation and consequently reduced social capital or the benefits gained from relationships within the community. Numerous studies have demonstrated links between reduced levels of social participation and increased levels of crime (Morenoff et al. 2001; Sampson, Raudenbush & Earls 1997; Sampson 1997).
  • Percentage of Aboriginal identity population living in the neighbourhood. Included are those persons who reported identifying with at least one Aboriginal group, that is, "North American Indian", "Métis" or "Inuit (Eskimo)", and/or who reported being a Treaty Indian or a Registered Indian, as defined by the Indian Act of Canada, and/or who reported they were members of an Indian Band or First Nation. The Aboriginal population in Canada is over represented with respect to victimization and offending. For instance, about 35% of Aboriginal people reported being the victim of at least one crime in 12 months preceding the 1999 General Social Survey on Victimization, in comparison to about 26% for non-Aboriginal people (Statistics Canada 2001a). In 1998-99 Aboriginal peoples aged 18 and over represented about 2% of the 18 and over population, but about 17% of admission to provincial/territorial custody and the same proportion to federal custody (Thomas 2000).
  • Percentage of female lone-parent families among economic families living in private households.2 Although the after-tax income of female lone-parent families is increasing in Canada, these families continue to be among the lowest earners (Statistics Canada 2001c), and consequently may be concentrated in more disadvantaged areas of the city. Additionally, an increase in labour force participation among female lone-parents from 65% in 1995 to 82% in 2001 may be tied to the notion of decreased guardianship or supervision in neighbourhoods, which has been associated with higher crime rates (Cohen and Felson 1979).
  • Percentage of population in a neighbourhood living at another residence one year prior to the Census. Residential mobility has been associated with higher crime rates through reduced guardianship or social involvement that frequent movers exhibit. For instance, studies of American cities indicate that streets where neighbours knew each other or felt responsible for their community had significantly lower rates of violent crime than those where social interaction was lower (Block 1979; Sampson 1993).

Dwelling characteristic variables

  • Percentage of dwellings built before 1961. In combination with other variables related to signs of physical decay within urban neighbourhoods the age of buildings may be associated with higher crime rates through a perception of increased physical disorder (Kelling & Coles 1998).
  • Percentage of dwellings in need of major repairs. Refers to whether, in the judgement of the respondent, the dwelling requires any repairs (excluding desirable remodelling or additions). Major repairs refer to the repair of defective plumbing or electrical wiring, structural repairs to walls, floors or ceilings, etc. This variable may similarly be associated with higher crime rates through the perception of increased physical disorder in the neighbourhood (Kelling & Coles 1998).
  • Percentage of households spending more than 30% of total household income on shelter, including both owner-occupied and tenant occupied households. This is a measure of housing affordability. The 30% figure is based on research indicating that when the shelter costs of low income households exceed 30% of their incomes, their consumption of other life necessities is reduced. Shelter expenses include payments for electricity, oil, gas, coal, wood or other fuels, water and other municipal services, mortgage payments, property taxes, condominium fees and rent. Decreased housing affordability within a neighbourhood is another indicator of socio-economic disadvantage.
  • Percentage of owner-occupied dwellings in the neighbourhood. Collective dwellings are excluded from both the numerator and denominator. Greater proportions of owner-occupied housing in a neighbourhood may increase residential stability, social involvement among neighbours and a collective commitment to the neighbourhood.

City land-use variables

  • Commercial zoning – the proportion of square area within a neighbourhood zoned for commercial land-use. Types of land-use falling under commercial zoning include stores, supermarkets, discount stores, furniture stores, banks, hotels, beverage hotels (licensed off-sales beer vendors), motels, restaurants, service garages, service stations, auto dealers, car washes, residential/commercial split properties and commercial offices.
  • Multiple-family residential zoning – the proportion of square area within a neighbourhood zoned for multiple-family, two-family (duplex) or transitional dwellings which include short- and longer-term subsidized housing for those in need.
  • Single-family residential zoning – the proportion of square area within a neighbourhood zoned for single-family dwellings.


What is Geocoding?

Geocoding is the process of matching a particular address with a geographic location on the Earth’s surface. In this study the address corresponds to the location of the incident reported to the police and aggregated to the block-face level, or to one side of a city block between two consecutive intersections. This is done through matching records in two databases, one containing a list of addresses, the other containing information about a street network and the address range within a given block. The geocoding tool will match the address with its unique position along the street network. Since the street network is geo-referenced, or located in geographic space with reference to a coordinate system, longitude and latitude values—or X and Y values—can be generated for each crime incident.  X and Y values in the crime incident database provide the spatial component that allows for points to be mapped, relative to the street or neighbourhood in which they occurred.

While the UCR2 does not currently collect information on the geographic location of crime incidents, for the purposes of this study these data were provided by the Winnipeg Police Service (WPS) for each of the approximately 73,000 incidents reported in 2001.3 The WPS collects the street address of each reported incident. This information was resolved by the WPS to a set of geographical coordinates (X and Y) for each address. These coordinates were rolled up to the mid-point of a block-face, and intersection data were compiled.  

Mapping techniques

Two methods of displaying crime and other information are used in this study. First, data are displayed as a total for each NCA (see Text Box 1 for NCA description), and second, the pattern of points (individual criminal incidents) is displayed across the City of Winnipeg to indicate the location of high density crime locations or “hot spots”.

Text Box 1: Neighbourhood Characterization Areas

The 230 ‘neighbourhoods’ in this study reflect Neighbourhood Characterization Areas (NCAs) (Map 1). The NCA boundaries were formally adopted in the 1980s by the Community Data Network (CDN), a consortium of government and non-government agencies in Winnipeg. The boundaries are based on the collective knowledge of many local agencies that helped to establish these and other geographies including the inner city.

Map 1. Neighbourhood Characterization Area (NCA) boundaries, Winnipeg, 2001

Boundaries were defined based on information about housing and existing neighbourhoods, natural conditions such as rivers and streams, transportation routes (rail lines and major roadways), and land usage (residential, commercial and industrial). NCAs are typically smaller and more demographically and socio-economically homogeneous than Statistics Canada’s neighbourhood level geographies (i.e., Census Tracts) and more accurately match boundaries used by the City and other agencies to direct programs. The smaller size of the NCA units makes them a critical geography for many Winnipeg groups and they have effectively become the standard for assessing neighbourhood issues.

Map 2. A comparison of NCA and Census Tract boundaries, West Broadway and Armstrong Point, Winnipeg, 2001

The choice of neighbourhood boundaries can change the understanding of the distribution of neighbourhood characteristics. Map 2 shows the greater specificity of NCA boundaries than Census Tract boundaries. In this example, Census Tract 15 encompasses two NCAs, Armstrong Point and West Broadway, with different levels of socio-economic disadvantage.

Mapping NCAs

By combining the crime incident codes with an X and Y value, point distributions were generated for specific crime types, time of incidents, and other data from the UCR database.  Using the Geographic Information System (GIS), point data were overlaid on top of NCAs. Crime incidents were then calculated as a total for each NCA. 

Mapping “hot spots”: Kernel analysis

Kernel analysis is an alternative method of making sense of the spatial distribution of crime data. The method makes it possible to examine crime incident point data across neighbourhood boundaries and to see natural distributions and the location of concentrations of incidents. The goal of kernel analysis is to estimate how the density of events varies across a study area based on a point pattern. Kernel estimation was originally developed to estimate probability density from a sample of observations (Bailey and Gatrell 1995). When applied to spatial data, kernel analysis creates a smooth map of density values in which the density at each location reflects the concentration of points in a given area.

In kernel estimation, a grid is overlaid on the study area. Distances are measured from the centre of a grid cell to each observation that falls within a predefined region of influence known as a bandwidth. The grid cell size for single kernel estimation in this study was about 110 meters squared. Each observation contributes to the density value of that grid cell based on its distance from the centre. Nearby observations are given more weight in the density calculation than those farther away.

The product of the kernel estimation method is a simple matrix of dots (raster image) displaying contours of varying density. Contour loops define the boundaries of hot spot areas. Hot spots may be irregular in shape, and they are not limited by neighbourhood or other boundaries. This method of analysis was applied using Environmental Systems Research Institute (ESRI) Spatial Analyst software.

The dual kernel method is also used in this study in order to examine the distribution of two variables simultaneously (for example crime and population at risk).4 The dual kernel method was applied using CrimeStat 2.0 spatial statistics modelling software.

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Date modified: 2004-09-16 Important Notices
Online catalogue 85-561-MWE Online catalogue - Neighbourhood characteristics and the distribution of crime in Winnipeg Main page Background Findings Tables, maps and appendices Methodology Bibliography More information PDF version Previous issues of the Crime and Justice Research Paper Series