Getting the data is what took up a major portion of the time on this project, since I had only limited data to begin with. Knowing that there was no available digitized data on the locations of McDonald's restaurants, or Subway, Wendy's and Burger King for that matter, I set forth on the monotonous task of digitzing each location myself. I first searched the web for all addresses of the four restaurants in the lower mainland. Then, using the road network map from the GIS lab and my road map atlas of the lower mainland, I identified each address on the road network map and digitized it's location. I also correlated each point with it's address in an excel file, to later link the two for identification purposes.

    So when I was finally finished digitizing, I had a complete map of all locations of the four restaurants in the lower mainland. At this point I was already able to determine spatial correlation of the different restaurants, simply by visual inspection.

    Since no data was available on population density in McDonald's catchment areas, I used road network density as an indicator of population density. If you analyze the data more closely you can see how the individual restaurants are located amoung areas of high road network density.

    The already digitized files which I used were from the 'gvrd/gen' folder in the Geog 355 data sets, and included 'city', 'mjroads', and 'allroads'.