Methodology

Stage 2: Ideal Habitat Locations

Goal 2: Where in Vancouver will birds likely be drawn to? In this urban environment, what conditions will favour bird populations the most?

For the following stage of the project, a majority of the procedures were produced using ArcMap. Here, I developed parameters to create a habitat suitability surface to identify where in the city birds are generally best suited to thrive in.

I assumed the following criteria when generalizing bird traits:

Birds will have a better chance of survival where buildings are shorter in height. Open spaces and low buildings would likely mean that a lower building collision rate. High-rise buildings ( >10 stories tall) were designated as being detrimental to bird health.
Regions located near existing parks and greenways would be more suitable. These areas would provide similar habitats, food sources and space to interact in as their natural environments.
Neighbourhood blocks that have a high proportion of trees would also increase survivability of birds. Like the parks and greenways, trees would provide refuge from predators and a natural corridor path to travel along.
Birds will prefer areas that are further away from roads and skytrain lines. This would lower the amount of fatal accidental vehicle mortalities
Neighbourhoods with a lower population density will likely mean there are fewer disturbances and fewer domesticated cats in the area
Zones that are designated for industrial uses are bad for birds and will have higher pollution rates

With these core guidelines in place I followed these steps to conduct my analysis:

Import Data into ArcMap and then begin processing the data
Data Preparation

I first merged Lanes, One way Streets, Non-city Roads and Rapid Transit Lines to form one major road network later
Then categorized building footprints by average height (ie. into mid-rise, high-rise developments, etc)
Manually edited the neighbourhood block attribute table to include area and population > and then calculated population density
Joined city trees point layer into neighbourhood blocks and then edited the attributes table to calculate tree density

3. Euclidean Distance Tool

I then performed a Euclidean Distance tool to create the following raster layers which were then reclassified with appropriate weights:

Major Roads Network
Parks
Greenways

4. Next I converted the remaining layers into raster files and then reclassified their values to match what the conditions outlined for the habitat suitability analysis.

Building Heights were reclassified as 0 - 4 (4 being anything under 5m tall)
Zoning Regions were classified as 0 (High or Low level Industrial) or 1 (other)
Neighbourhood Population Densities
Neighbourhood Tree Densities

With all the information in place I performed a weighted sum tool with the following weighting schemes:

Sensitivity Analysis (using relatively even weights)

Variables: 0.15 - Building heights

0.15 - Neighbourhood population density

0.1 - Euclidean distance to the major roads network

0.15 - Neighbourhood tree density

0.2 - Euclidean distance to parks

0.2 - Euclidean distance to greenways

0.05 - Zoning

2. Relative Weighting System (which favoured parks, greenways and building heights)

Variables: 0.2 - Building heights

0.1 - Neighbourhood population density

0.1 - Euclidean distance to the major roads network

0.1 - Neighbourhood tree density

0.2 - Euclidean distance to parks

0.2 - Euclidean distance to greenways

0.1 - Zoning

Once this analysis on the general bird population in Vancouver was complete I then performed a modified weighting system that was modelled specifically to the Predator Birds category. In this case I reclassified the building heights raster later and flipped the weighting scheme to reflect their preference of taller buildings which could be used to survey prey closer to the ground.