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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.
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.
Population characteristic variables
Dwelling characteristic variables
City land-use variables
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.
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”.
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.
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.