Table 8
Regression models for rates of selected theft offences, city of Toronto census tracts, 2006

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Regression models for rates of selected theft offences, city of Toronto census tracts, 2006
  Shoplifting Theft from a motor vehicle Motor vehicle theft Other thefts
Number of incidents 9,214 15,731 5,804 19,972
Explanatory power 1 20.1 27.5 33.8 56.6
  regression coefficients (b)
Socio-economic characteristics  
Access to socio-economic resources -0.187*** -0.098* -0.192***
Economic vulnerability
Ethno-cultural characteristics  
Recent immigrants -0.319***
Ethno-cultural diversity 0.083**
Demographic characteristics  
Children 0.348*** -0.113**
Elderly people -0.133*** -0.071*
Young men 0.135**
Urban characteristics  
Centrality 0.263***
Urbanization 0.097* 0.143**
Major repairs 0.085* 0.194***
Subway or train station 0.221**
Economic activity  
Commercial activity 0.632*** 0.116* 0.521***
Manufacturing jobs -0.268*** 0.092*
Office jobs -0.236***
Bars -0.131** 0.099*
Spatial lag 1 0.382*** 0.368*** 0.379***
not applicable (variables excluded from the model because they are not significant [p<0.05])
Significantly associated with dependent variable p<0.05
Significantly associated with dependent variable p<0.01
Significantly associated with dependent variable p<0.001
For the shoplifting rate model, data represent the coefficient of determination (R2). For the other models, which are autoregressive, they are the squared correlation coefficients between observed and predicted values. See "Spatial autocorrelation and regression" in the Methodology section for more information on autoregressive spatial models.
Based on 524 census tracts. Regression models include intercept. Population at risk includes residents and workers.
Statistics Canada, Incident-based Uniform Crime Reporting Survey, geocoded database, 2006 and 2006 Census.