Patterns of Crime in Canadian Cities :  A Multivariate Statistical Analysis

Section 2.  Methodology

2. Methodology

The present study uses crime data reported by individual police departments across Canada to the aggregate Uniform Crime Reporting Survey (UCR1), conducted by the Canadian Centre for Justice Statistics, Statistics Canada. Data are from 1999. It should be noted that these data do not reflect the entire picture of crime because it is widely known from many different victimization surveys that only about half of all crimes are reported to the police.

While data were available for all police departments and detachments in Canada, data from rural areas were excluded from this study because there were no accurate population estimates and subsequently rates of crime cannot be calculated. However, some regional police departments may include some rural areas in their administrative area. In addition, a few aboriginal police forces for Indian reserves reported their population and are therefore included in this analysis. With these minor exceptions, data analyzed do overwhelmingly represent reported crimes that occurred in cities.

In all, the UCR1 database provides information from exactly 600 cities. Table 1 shows the distribution of these cities in the ten provinces. While there certainly are small towns in the territories (Yukon, Northwest Territories, Nunavut), population estimates for those towns were not available and they are therefore not included in this study.

Table 1. Distribution of Cities in this Study
Province Number Percent
Newfoundland 4 0.7
Prince Edward Island 6 1.0
Nova Scotia 29 4.8
New Brunswick 23 3.8
Quebec 157 26.2
Ontario 149 24.8
Manitoba 32 5.3
Saskatchewan 50 8.3
Alberta 75 12.5
British Columbia 75 12.5
TOTAL 600 100.0

The first step of the analysis involves combining the offences into offence groups or offence categories to facilitate the analysis. This is necessary because crime rates for small towns would be extremely low for certain individual offences and a large amount of near zero values will bias the results. Table 2 shows the groupings used in this study.

Table 2. Offence Categories
1 Homicide murder, manslaughter, infanticide
2 Sexual assault I common sexual assault (level 1)
3 Major sexual assault sexual assault with weapon (level 2), aggravated sexual assault (level 3)
4 Other sexual offences e.g. sexual relation with persons under 14
5 Non-sexual assault I common non-sexual assault (level 1)
6 Major non-sexual assault attempted murder, non-sexual assault with weapon (level 2), aggravated non-sexual assault (level 3)
7 Other non-sexual assault e.g. assaulting a police officer
8 Robbery
9 Abduction & kidnapping abduction, kidnapping
10 Break & enter
11 Theft motor vehicle
12 Theft over $5000
13 Theft $5000 & under
14 Possession of stolen goods
15 Fraud & counterfeiting fraud, counterfeiting currency
16 Arson
17 Vandalism wilful damage
18 Moral offences prostitution, gaming and betting, indecent acts, public morals
19 Offensive weapons e.g. possession of prohibited or restricted weapons
20 Miscellaneous Criminal Code e.g. bail violations, escape custody
21 Narcotics possession including heroin, cocaine, cannabis, other narcotics
22 Narcotics trafficking including heroin, cocaine, cannabis, other narcotics
23 Controlled & restricted drugs
24 Misc. Federal Statutes e.g. Customs Act, Young Offenders Act
25 Criminal Code traffic e.g. impaired driving, fail to provide breath sample

The crime rates (per 100,000 population) of these 25 offence groups for all 600 cities were analyzed using a statistical technique called factor analysis[1]. The objective of this procedure was to derive a smaller number of “components” or “factors” which can represent the original 25 variables. Each of the components will represent a group of variables that are highly correlated. In other words, variables that are highly correlated will be expected to be represented in the same component (such as those pairs of highly correlated variables described above).

In this study, the optimal number of components or factors was found to be four after examining the results of the factor analysis. These 4 components (factors) were then used to represent the original 25 categories of offences. In other words, while the 25 crime rates could be used to represent the detailed pattern of crime, the 4 components could satisfactorily do a similar job, though with less details. Each component combined the rates of a few offences that were closely associated. For example, the first component in the analysis was designated “Minor Crimes” and could be used to represent 7 minor crimes while the second component was designated “Violent Crimes” and could be used to represent 5 violent crimes and 5 other crimes (details below in the Section 3). Therefore, for each city, instead of using 25 crime rates to represent its pattern of crime, we could use 4 factor scores.

The next step in the analysis was to determine whether the pattern of crime varies according to geographical region or city size. The statistical technique used was discriminant analysis. The objective of this procedure was to find out whether cities in different regions have their own typical crime patterns or regional crime profiles. The same analysis would also be done for city size classes. The results would show how the crime profiles regions (or city size classes) differ from each other. Furthermore, based on their factor scores, individual cities would be assigned to one of the regions and one of the city size classes which may or may not be the same as their original region or original city sizes. The results might show that a certain city had a crime pattern that resembled cities in a different region than its own (or a different size class than its own).

In this way, the analysis would provide information on crime patterns for all of Canada, for different regions, and for different city size classes, as well as information on crime patterns of individual cities.

[1] The technical term of the statistical procedure used is “principal component analysis”. It is the most commonly used type of factor analysis which includes a variety of different statistical procedures.

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