To achieve the goal of this project, we underwent specific mandates to finalize our normative spatial analysis.

1. Gather appropriate data. Using the Chass website from the University of Toronto (http://www.chass.utoronto.ca/), open data catalogue for the City of Toronto, and UBC abacus website, we were able to gather the appropriate data. These include police facilities, ambulance facilities.
2. We were required to organize the data into the same projection format (GCS North American 1983) to avoid scale inaccuracies.
3. Acquired quantitative data in excel format from the Chass website for average income, crime data, and demographic data.
4. Data organization: certain census tracts did not have the proper CTUID expression, so we were required to convert all census tracts into number formats, and create a new value field delineating proper CTUID numbers. The last step for data organization was to convert these values to text format to use in ArcGIS.
5. Join excel tables to ArcGIS from our geodatabase as a single table for each of the three variables. After each table was joined individually, we then joined each table into a single layer including the overlaying census tract shapefile.
6. In order to express the data onto the layer for each variable, we identified categories and class breaks in order to give us a choropleth map showing violent crime rates per census tract. We did the same for each variable.
7. Next, we used our previous shapefiles for ambulance and police facilities to create buffer zones of 1km overlaid onto violent crime rates to determine problem areas.
8. Finally, we conducted a series of linear regression analyses using the ordinary least squares function for a combination of two out of three variables, and a one final map comparing all three variables. Additionally, we conducted a spatial autocorrelation (Moran’s I test) to determine statistical significance.

The map below shows our area of interest being the City of Toronto.