DATA &
METHODOLOGY

1. Cleaning and Transforming Employment Data

The EA Business Data is filtered to include only Core or Additional Amenities. Employment size is log-transformated to reduce the effect of extreme outliers.

2. Gathering and Transforming Station-Level Daily Visit Data

Daily visits are weighted based on home location to categorize residents vs. non-residents, while also being log transformed, reducing the effect of over-penalizing high-volume downtown and transfer stations.

3. Calculating Access using Two-Stage Floating Catchment Areas

Two-Stage Floating Catchment Area (2SFCA) is a spatial accessibility method that measures how easily people can reach services while accounting for both supply and demand within a travel threshold. Services are our core and additional amenities. Supply is the transformed counts of employees per amenity. Demand is the effective demand for daily resident and non-resident visits. For the travel threshold, we use an 800-metre buffer around each station, equivalent to a 10-minute walk.

4. Calculating a Regional Benchmark and Access Surplus

In order to compare the Access of an amenity type for a particular transit station area, regional benchmarks were created to compare the different levels of access conditions across the transit region, with Typical (median) and Above Average (75th percentile) Access benchmarks.

From there, Access Surplus for each Amenity Type and transit station area was calculated as the difference between Access and Regional Access to identify where more capacity should exist or additional visits can be handled.

5. Translating Access into Planning Action

In order to compare the Access of an amenity type for a particular transit station area, regional benchmarks were created to compare the different levels of access conditions across the transit region, with Typical (median) and Above Average (75th percentile) Access benchmarks.

With Access to every Amenity Type within a transit station area calculated and compared to the Regional Access, this can be translated into interpretable numbers for accomplishing the following objectives:

i. Identify Amenities within a transit station area where additional Capacity is needed to meet typical Access levels

ii. Identify how Access changes with a future increase in Residents to a particular transit station area

Calculating Typical Employment
For each amenity group within a transit region, the median employment size is calculated. This represents the employment of a typical amenity type within a transit station area.

Calculating Additional Employees Needed
For MTSAs where an Amenity has an Access Surplus less than 0, Additional Employees Needed is calculated as a ratio of Access Surplus and Regional Access, multiplied by Typical Employment. $$\mathrm{Employees\; Needed}=\left|\frac{\mathrm{Access\; Surplus}}{\mathrm{Regional\; Access}}\right|\mathrm{\times \; Typical\; Employment}$$

6. Calculating Future Demand Scenarios

Using Population and Visit Data, we can convert the number of additional residents into the additional number of daily trips by calculating the average number of daily visits made to a transit station area by residents.

Future demand scenarios are calculated as a ratio of daily visits and a sum of daily and additional daily visits, multiplied by access.

$$\mathrm{Future\; Access}=\mathrm{Access}\times \frac{\mathrm{Daily\; Visits}}{\mathrm{Daily\; Visits\; +\; Additional\; Daily\; Visits}}$$