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Incident-based Uniform Crime Reporting Survey
The Incident-Based Uniform Crime Reporting Survey (UCR2) collects detailed information on individual criminal incidents reported to the police, including characteristics of incidents, accused persons and victims. The Montréal police service has been reporting to the UCR2 Survey since 1992.
The UCR2 Survey allows a maximum of four offences per criminal incident to be recorded in the database. 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 major offence categories (violent offences, property offences, drug-related offences and other Criminal Code offences) undertaken in this study are based on the most serious offence in each incident, as are the crime rates published annually by the CCJS. In this type of classification, 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 in this study are based on major offence categories, such as violent offences and property offences, and take into account only the most serious offence in each incident. However, when the analysis is focused on individual offence types, all incidents in which the offence is reported are included, whatever the seriousness or the ranking of the offence in the incident. This method provides a more complete spatial representation of the different types of individual offences. For example, Table 1 provides information on selected individual offence types, such as theft $5,000 and under, theft over $5,000, vehicle theft, shoplifting, breaking and entering, drug offences, mischief, arson, prostitution, robbery, common assault, sexual assault, homicide and major assault.
This study includes most Criminal Code offences and all offences under the Controlled Drug and Substances Act, but excludes offences under other federal and provincial statutes and municipal bylaws. 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 violations, failure to appear and breaches 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 roadside stop programs). In total, more than 12,000 offences were excluded for 2001 and more than 13,000 offences for 2004.
Census of Population
On May 15, 2001, Statistics Canada conducted the Census of Population to produce 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 are derived from the long form of the Census, which is completed by a 20% sample of households. These data exclude the institutional population, that is, individuals living in hospitals, nursing homes, prisons and other institutions.
Island of Montréal land use data
Land use data were utilized to calculate the proportions of neighbourhoods with commercial, multi-family residential and single-family residential zoning. Land use data show the actual utilization of urban lands, while zoning data reflect planned and legislated use. Land use parcels were aggregated to the neighbourhood level in order to calculate proportions. They cover 438 km2, or 87.6% of the Island's 500 km2. Land use data were taken from the most up-to-date version of the geomatics department database at the Communauté métropolitaine de Montréal, and they date from 2005. The 2001 land use data were not archived.
To round out the picture provided by land use data, zoning data were used. These data, which were obtained from the Montréal planning department, increased coverage by an additional 40 km2. In all, land use data cover 96% of the Island territory. The census tracts (CTs) that remain uncovered are concentrated in the boroughs of Île-Bizard (CTs 550.2, 550.3 and 550.4) and in part of Pointe-Claire (CTs 450.0, 451.0 and 452.0).
The Business Register Division of Statistics Canada provided the addresses of all drinking places on the Island of Montréal in 2001 (code 7224 of the North American Industry Classification System). This code includes establishments known as bars, taverns or drinking places primarily engaged in preparing and serving alcoholic beverages for immediate consumption.
The distribution of criminal incidents across urban areas is often concentrated in or near the city centre, where residential populations are relatively low, but where there are high concentrations of people either working or engaging in other activities. Rates based on residential population alone will artificially inflate the crime rates in these urban core neighbourhoods, since the total population at risk in these areas has not been taken into account.
To more accurately gauge the risk of crime in CTs, crime rates are based on the population at risk. An approximation of the population at risk is obtained by adding the number of workers and the number of residents in each CT. Rates based on these combined populations more closely approximate the total number of people at risk of experiencing crime. This study uses the approach taken in the Winnipeg research project.1
2001 Census of Population variables
Dwelling characteristic variables
The results of the Winnipeg research project showed major differences between the socio-economic characteristics of high-crime neighbourhoods and those of low-crime neighbourhoods. High-crime neighbourhoods were characterized by reduced access to socio-economic resources (Fitzgerald, Wisener and Savoie 2004). A number of American studies have also demonstrated that inequality of socio-economic resources between neighbourhoods in American cities is strongly associated with the spatial distribution of crime (Morenoff, Sampson and Raudenbush 2001). In the present study, the following socio-economic variables are used:
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 an incident that was reported to the police, after aggregation to the block-face level—that is, to one side of a city block between two consecutive intersections. This is done by matching records in two databases, one containing a list of addresses, the other containing information about the street network and the address range within a given block. The geocoding tool will match the address with its unique position in the street network. Since the street network is geo-referenced (located in geographic space with reference to a coordinate system), it is possible to generate longitude and latitude values—or X and Y values—for each criminal incident. Where the incident location does not correspond to an address, geocoding is performed by creating a point on, say, an intersection of two streets, a subway station or the middle of a public park. X and Y values in the criminal incident database provide the spatial component that allows for points to be mapped, relative to the street or neighbourhood in which they occurred.
In 2001, the UCR2 Survey did not lend itself to collecting information on the geographic location of criminal incidents.3 For the purposes of this study, the Montréal police department sent the CCJS the addresses of approximately 136,000 incidents selected, reported and entered in the UCR2 database in 2001 and approximately 140,000 incidents in 2004. The Montréal police department also provided information on the home address of nearly 32,500 accused persons identified in 2001 incidents. This information was resolved by the CCJS into a set of geographical coordinates (X and Y) for each address. These coordinates were rolled up to the mid-point of a block-face in the case of specific addresses, and to intersection points in the case of streets, parks and subway stations.
The geocoding exercise was successful for more than 96% of 2001 incident location data and for more than 95% of 2004 data. All addresses of criminal incidents that were reported more than five times but failed the automated geocoding process were geocoded manually so as to represent crime concentrations as accurately as possible. The low percentage of incidents that failed geocoding did not create a bias in offence trends. Incidents that failed geocoding contained information that was too vague, such as a bus number or the trans-Canada registration.4 In fact, geocoded offences and offences prior to geocoding both account for the same proportion of overall crime.
In this project, the Montréal police department provided the addresses of accused persons that were entered in its information management system, without additional editing. This information therefore includes a number of missing and inaccurate address elements, which makes the geocoding process more difficult. The accused persons' home addresses supplied by the police service refer to persons against whom official charges were laid or recommended for offences in 2001, that is the persons charged. According to contacts at the Montréal police department, the information concerning the addresses of accused persons is of higher quality when the individual is formally charged, since a complete and valid address must be provided in the files submitted to the courts. Therefore the data do not take into account children under 12 years of age or adults whose case may have been processed informally by the police. The geocoded data on persons charged used in this study are a sample representing 75% of all persons charged in violent incidents, 73% of those charged with property offences, 78% of those charged with prostitution or gaming offences or offensive weapons-related crimes and 78% of those charged with drug-related offences, as reported by the Montréal police department to the UCR2 Survey. A comparison of the distribution of geocoded addresses of persons charged and the set of persons charged in the UCR2 database by age and sex shows no statistically significant difference based on T-test, p<0.001.
Two methods of presenting crime and other information are used in this study. The first method displays the total points for each CT (see description of CTs below). The second displays a pattern of points where each point corresponds to a criminal incident or the home address of a charged person. This method shows high-density crime locations or "hot spots."
Census tracts and natural neighbourhoods
Ecological studies recognize that the choice of neighbourhood boundaries can change how the distribution of neighbourhood characteristics is understood (Ouimet, 2000). The natural neighbourhoods used in this analysis correspond to CTs, which are delineated by Statistics Canada in conjunction with a committee of local experts (e.g., planners, social workers, health care workers and educators). The initial rules for delineation, in order of priority, are as follows:
In a study of the impact of neighbourhoods on health in Montréal, Ross, Tremblay and Graham (2004) found that analytical models using CTs as the geographic unit yielded results remarkably similar to the 'natural' neighbourhood model. These researchers concluded that the additional efforts invested in creating natural neighbourhoods other than CTs are not warranted "especially in studies where there are both a sufficient number of predefined geo-statistical units to draw from and where the units have some social meaning, as in the case of Canadian census tracts." (p. 1490)
Thus, CTs are by definition smaller and more homogeneous geographic entities than the boroughs whose boundaries are those of the former municipalities of Montréal and the territories served by the different police stations on the Island. Since CTs are also used in many studies, this makes it possible to add layers of additional information (health, education, economic factors, etc.) for an integrated approach toward prevention in neighbourhoods with a number of risk factors.
Of the 521 CTs that are part of the Island of Montréal, 520 were the location of at least one offence in 2001. However, the bivariate and multivariate analyses presented include only 506 CTs, namely those with more than 250 inhabitants. Statistics Canada suppresses income data for geographic areas under this threshold for reasons of confidentiality and data quality.
Mapping census tracts
By combining criminal incident codes with X and Y values, point distributions were generated for specific crime types. Using a geographic information system (GIS), point data were overlaid on top of CTs. The total number of criminal incidents was then calculated for each CT.
Mapping hot spots: kernel analysis
Kernel analysis is an alternative method of making sense of the spatial distribution of crime data. This method makes it possible to examine criminal incident point data across neighbourhood boundaries and to see natural distributions and the areas where these incidents are concentrated. 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 fine 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. Each observation contributes to the density value of that grid cell based on its distance from the centre of the cell. Nearby observations are given more weight in the density calculation than those farther away. In this study, the grid cell size is 100 square metres. The research radius used is 1,000 metres, and the higher the research radius, the smoother the image produced.
The product of the kernel estimation method is a simple dot matrix (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 the Spatial Analyst software of the Environmental Systems Research Institute.
The dual kernel method is also used in this study to examine the distribution of two variables simultaneously. Use of the dual kernel serves to standardize the distribution of crime based on the population at risk (the sum of the number of persons who reside or work in a neighbourhood). The dual kernel is calculated using an in-house procedure that standardizes single kernel density distributions.
Measuring the distance travelled by persons charged
The coordinates generated by the geocoding process are used to calculate the distance travelled by persons charged to the place of the offence. In this study, two methods are explored for measuring the distance between the point of origin (address of the person charged) and the point of destination (the location of the offence). A first measure is taken by calculating the Euclidian (straight-line) distance between the coordinates. This first measure is used largely for its relative simplicity, since most GIS software includes this feature. However, this method does not take account of the street network and topography, which are likely to increase the distance travelled between the origin and destination points. The Euclidian method may underestimate distances travelled. A second way to measure the distance travelled is to use the national road network,5 which yields a better estimate of the distance travelled, in that it takes account of obstacles to movement, such as a railway or stream. The distance is calculated by using the optimum trip length, that is, the shortest street route between the points of origin and destination. Despite the increased accuracy obtained by using the street network, the resulting measure of distances travelled is still an estimate; it is not possible to know whether the persons charged actually used the shortest route and whether the point of origin was their place of residence. According to research conducted in the United States (Groff and McEwen 2005; Rhodes and Conly 1981), these results must be considered as approximate measures of the area of activity of persons charged.
1. For more information on populations at risk and how they are calculated, see Fitzgerald, Wisener and Savoie, 2004.
2. An economic family is a group of two or more persons who live in the same dwelling and are related to each other by blood, marriage, common-law or adoption.
3. In January 2005, the CCJS implemented the UCR2.2 Survey, a revised version of the UCR2 Survey. The UCR2.2 Survey will collect information on the geographic location of every criminal incident as well as on hate crimes, organized crime and cybercrime.
4. For more information on the geocoding of UCR2 data in special projects, see: Josée Savoie, 2005, Geocoding Crime Data: Feasibility Study on Collecting Data from Police Forces, Ottawa, Canadian Centre for Justice Statistics, unpublished report.
5. Available at no charge on the GeoBase website: www.geobase.ca/geobase/en/data/nrnc1.html.