Typically, when using spatial analysis to analyze crime patterns two general methods exist for identifying spatial concentrations of crime: area-based methods and point-based methods (Williamson et al. 2001). In area-based methods, crime data are aggregated into geographical areas such as blocks, precincts, or census tracts (Williamson et al. 2001). The block aggregation technique was used for this study because the advantages outweigh the shortcomings. Block aggregation is a useful tool for studying crime patterns and provides an informative “first cut” at the data before additional analysis using more sophisticated techniques (Williamson et al. 2001). In our case, after adding the crime data using the XY coordinates, every crime was aggregated into one of the 22 local areas to perform a Hot Spot Analysis on crime rates. Crimes rates were calculated based on total crime in an area divided by population density which was calculated from the census data. The results from the hotspot analysis were mapped and presented as a visualization to better understand the data (Figure 1). Next, to properly analyze the change in crime patterns in relation to socioeconomic variables, the percent change for crime rate and all socioeconomic variables were calculated for each area. Based on these calculations, a map representing the crime rate changes as percentages was able to be produced to highlight how crime has evolved in each area and Vancouver as a whole, from 2006 to 2016 (Figure 2). The third step in our analysis was to identify any potential relationships between all variables using a Generalized Linear Regression (GLR). Our GLR helped produce Figure 3 which shows the relationship between all our variables along with their R² to represent the strength of the relationship. Finally, the last step of our analysis was to run an Explanatory Regression to identify which combination of variables produces the strongest relationship with the crime rate changes. The Explanatory Regression produced 5 tables showing the highest adjusted R² relationship between the dependent variables and change in crime rate with each table adding a variable (ie. Table 1: any 1 dependent variable, Table 2: any 2 dependent variables, Table 3: any 3 dependent variables, etc.) (Table 1,2,3,4,5).