Data Acquisition / Formatting

Eight different datasets were used to create the suitability map. The table below lists them, as well as their data source.

The first step when working with the data was to format it so that all data would be presented in a similar format. In most cases this involved clipping spatial area to represent only the study area. (studyarea.gif)

Census data
In the case of census data all data was presented in Dissemination Area (da’s). Three types of census data were combined (transit users, cyclists, average total income) to make one Census map (image of map) representing combined values for all three datasets.
This was done by.
1) creating maps which display the distribution of #of transit users, # of cyclists and total average income. Classified in 5 categories.
2) Normalizing each of the maps by creating a new field (called norm_1) in the attribute table of each map. Then using the calculator function to multiply the entire set of data by whatever number the highest value needs to be divided by, to make 100. So for example, if the highest value is 400. You would multiply the set of data by 0.25. Then you would have values ranging from 0-100 within the new field Norm_1. A new map displaying these values (split into 5 categories) is made (should look same as previous map, only with different numbers.)
3) Join the attribute table of all three maps in an empty DA map. Then create a new field in the attribute table called Norm step 2. Use calculator to add all values of all three Norm_1 values.
4) Now a map displaying the combined values of all three areas of study (cycling, transit and income) are all combined into one field called Norm_2.

Landuse data,
Landuse data was available on the h:/ drive in GVRD 2001 data. The map produced classified landuse based on land use type.

Geology
Geology data was georeferenced from a geology map taken form the geological survey of Canada [–natural resources Canada.] A field was created in the attribute table attributing each polygon with existing geology data.

Elevation
Elevation data was taken from the Vancouver/lower mainland DEM in H:/ drive. The Spatial analyst/surface analyst tool was used to classify the slope. The map was then reclassified to identify areas with slopes greater than 3%. A map identifying 5 different classifications of slope was made.

Skytrain/roads/water
The skytrain, roads and water maps were all used in analysis, but not further modified (other then having the study area clipped out) at this stage.

Normalization

The normalization process is a step which modifies the data from existing maps to ensure that all maps have values between 0-100. This is necessary when combing values from different sources to ensure that some values don’t skew the overall results. (So when the highest value on all maps is 100 and all maps represent values between 0-100, the maps can be used together during analysis)

Census data was normalized by taking the final values found on Norm_2 field (representing he combined values of all three areas of study) and then multiplied to ensure the highest value was 100.

Landuse data was normalized by assigning a numerical value (between 0-100) to each land use. The higher the value, the less desirable. Below is a chart which identifies our numbeing values, and reasoning.

Values between 0-100 were assigned to different geology in the region.

The remaining data sets used (including skytrain, major roads, water and Schools/institutions) were assigned values of either 100 or 0, 0 being most desirable.

Weighting.

The next setp in the analysis involvfed weightingthe different datasets. The weighting process involved multiplying the normalized values of each dataset by a certain value (0-1), in order to determine how much that data set would be valued in the final MCE analysis.
This process involved thorough research to help identify the values for each data set.
The table below identifies the values assigned to each set of data.

Suitability Map

The final Suitability map is a map of the lower mainland with values ranging from 0 to 526. The lower the value on the range the more suitable the pixel is for a subway line. The pixel size of the final map is at a 20 meter resolution. There is a faint visibility of geology and the buffered schools are very visible. Furthermore, major roads are very visible as they were weighted very high*. All criteria that were weighted highly* are easily visible on the map.
*Although the value was weighted highly, the actual weighting of the criteria lowered the value.

Model

The analysis that followed the creation of the suitability map, was facilitated by the creation of a model. The model would be able to provide the cost distance analysis, and create the ideal route. Creating a model was done to save time later if we were to do further analysis of another route. The flowchart (link) helps describe the processes, which the model includes to create the cost distance path. Download it here (link) and apply it to other municipalities/regions given an appropriate, normalized and weighted suitability map is made. All that is needed to run the model is a suitability map, and specific star/end points.

Calculations

Two of our main raster calculations.

For Changing “NoData” values…
Con(IsNull(layer),100,(layer))

For Buffering the skytrain lines.
((layer / max of layer) * 100)