DATA ACGUISITION AND MANIPULATION

The GIS coverage I used for this analysis was a shape file of Census Sub Divisions for the GVRD provided by Census Canada. To create a raster image to perform my analyses in Idrisi, I used the spatial analyst extension in Arcview to make a grid.apr project file. I then created an export file: xport.asc (ASCII data). In Idisi, under import, I used ARCRASTER to bring the xport.asc file into Idrisi and convert the ASCII raster data into Idrisi format. 

The attribute data I used for this project was all from Census Canada, downloaded into microsoft access and directly readable in Arcview. The Census variables I used were:

1.    Total employed labour force 15 years and over by place of work status (20% sample data) Usual place of work:

·    In CSD of residence
·    In different CSD

2.    Total employed labour force 15 years and over by mode of transportation (20% sample data)
·    Car, truck, van as driver
·    Car, truck, van as passenger
·    Public transit
·    Walked to work
·    Bicycle
3.    Total number of occupied private dwellings by structural type of dwelling (20% sample data)
·    Single-detached house
·    Semi-detached house
·    Row house
·    Apartment, detached duplex
·    Apartment building, five or more storeys
·    Apartment building, less than five storeys
4.    Avrerage Income.

The above attributes were all contained within the same attribute table in Arcview. I then started editing and manipulating the data to form fields in the attribute table that would be useful for my analysis: First, I created new fields for mode of transportation and number of occupied private dwellings by structural type from the existing fields:

·    Green modes = Car, truck, van as passenger + Public transit + Walked to work + Bicycle
·    Very green modes = Walked to work + Bicycle
·    High dense dwl. = Apartment building, five or more storeys + Apartment building, less than five storeys
·    Medium dense dwl. = Semi-detached house + Row house + Apartment, detached duplex


I then created the eight new fields to be used later as individual raster layers or factor images in Idrisi for my analyses. These fields are:

1.    %_green = green modes / Total employed labour force 15 years and over by mode of transportation
2.    %V_green = very green modes / Total employed labour force 15 years and over by mode of transportation)
3.    %Drive = Car, truck, van as driver / Total employed labour force 15 years and over by mode of transportation
4.    %same_csd  = In CSD of residence / Total employed labour force 15 years and over by place of work status.
5.    %diff_csd = In different CSD / Total employed labour force 15 years and over by place of work status.
6.    %H_dense = High dense dwl. / Total number of occupied private dwellings by structural type of dwelling.
7.    %M_dense = Medium dense dwl. / Total number of occupied private dwellings by structural type of dwelling.
8.    %L_dense = Single-detached house / Total number of occupied private dwellings by structural type of dwelling.

After the editing of the attribute table csd.shp was completed in Arcview, I went into Idrisi and used the data base manager to import the csd.shp attribute table which, upon saving it, became csd.dbf. I was now able to start creating my individual raster images with which to perform the analysis. To do this, I first created attribute values files for each of the factors to be considered in the analysis. I then used assign to join the raster image csd_raster with each .avl file to create a raster layer for each factor. Because these raster images themselves showed how the individual factors being analyzed were spatially distributed throughout the GVRD, I reclassed them into 5 categories to produce images   showing the spatial distribution of these factors, by CSD, throughout the GVRD.

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