Outline and Introduction                                                    Methodology                                                         Errors and Problems
Data Collection                                                       Spatial Analysis
 
 

Data Collection

Finding data that would apply to my project area was not difficult.  I ended up using Census data from 1996, covering the GVRD.  One of my earlier ideas required data that covered both the Vancouver and Toronto tracks, and the proposed tracks in Surrey in great detail.  I could not successfully find data such as this at a suitable scale.  All available data was at a scale too small.  I had hoped to be able to look at specific areas within each course layout.

I started with the ct96 raster image as a base for all of my maps.  The tables accompanying the image were thorough, but did not suit all of my individual needs.  I produced new fields using the Database Workshop.  Here are the determining factors and constraints that are used in my analysis:
 

            People per square kilometer;  this number is calculated for each census tract of the GVRD.  Table 1 provides the
            database used for this operation.  Locating the track in a sparsely populated area is obviously more suitable, for
            safety and transportation issues alone.

            Table 1, Categories 3 and 5:  PopDens = [V003] / [V005]
                                                                        = [Population, 1996] / [Land area in square kilometres, 1996]
 

            A distance of 5,000 m would be used.  I digized the present locations of stations on the ct96 raster image.  Locating the track near transportation hubs
            is of key influence.
              Table 1, Category 102:  Total number of persons 65 years and over.  I felt that using the actual number (keep in mind it's a 20% sample) rather than
            as a density would be more effective.  It would keep the course away from all large clusters of elderly people.  This is done simply to be considerate to the
            general population of the city.  Previous studies in other cities have proven this to be an effective means of operation.
              The age of the buidlings surrounding the track is of importance in its selection.  A track located near older neighborhoods does not improve on the land
            value of its surroundings.  The course in downtown Houston is an example of this phenomena.  Before its conception, it was thought that locating it in older
            neighborhoods would improve on their value.  After intensive study, the track was finally located in a newer neighborhood.  A racecourse better integrates
            itself with its surroundings when development is of recent memory.

            Table 15, Categories 31-34:  Old Buildings = [V031] + [V032] + [V033] + [V034] + [V035]
                                                                              = [All Buildings built before 1981]
 

            The income of the residents of a city allegedly has influence on the location of a racetrack.  This factor came somewhat as a surprise.  I believe that factor
            has little to do with track policy, but more with CART's image.  Can you imagine a global television audience viewing a race through the slums of a
            major city?  While not exactly the best moral stance, I do understand the motive.

            Table 14, Categories 5-9:  Low Income = [V005] + [V006] + [V007] + [V008] + [V009]
                                                                         = [Population aged 15 and older with an annual income less than $10,000]