Implement the Multi-Criteria Evaluation model using GIS to create areas of optimal suitability for these stations. 

A Multi-Criteria Evaluation (MCE) is used to incorporate the factors determined in Objective 1 and weights assigned in Objective 2. The entire MCE process is visually represented by the model in Figure 2. An MCE works by incorporating multiple factors into one final variable. In this study, the weights calculated from the pairwise comparison are multiplied by standardizations calculated for each criterion in ArcMap respectively.

      Figure 2. Simplified model used to determine the suitability of census tracts for Regional Express Rail stations (MCE of socioeconomic and physical criteria/constraints)

The standardization equations can be broken down into two general expressions:  

Equation 1. 100 *((X_cctv - V_min) /(V_max - V_min))            

Equation 2.  100 *(1-((X_cctv - V_min) /(V_max - V_min)))

where:

·       X_cctv is the value of the criterion for each census tract;

·       V_min is the minimum value for the criterion across all the census tracts; and

·       V_max is the maximum value for the criterion across all the census tracts.

The first equation is used for criteria where increasing values result in increasing suitability, while the second is used for criteria where decreasing values result in increasing suitability. All the criteria from Objective 2 were standardized with the first equation, with the exception of income, which was standardized with the second equation. This is because demand for transit is higher in areas with low income. 

The individual products of each criterion are added in a new field. The entire algorithm is expressed as the following equation (using the weights from Table 3):

Suitability = ([StIncome] *0.152) + ([StImmigration]*0.072) + ([StVisibleMinority] *0.069) + ([StChild] *0.011) + ([StYouth] *0.023) + ([StAdult] *0.167) + ([StMature] *0.041) + ([StSenior] *0.017) + ([StDensity] *0.271) + ([StPostSecondary] *0.175)

where ‘St’ represents the standardization of the individual criterion.

This results in the suitability layer demonstrated in Figure 3, which expresses the appropriateness of all the census tracts in the study for an RER station.

     Figure 3. Suitability of census tracts for increased transit infrastructure using only socioeconomic criteria

The suitability is further narrowed down by incorporating physical constraints and criteria. The first major constraint is that an RER station must be built in a census tract which currently has a GO rail line passing through it. In addition, the physical criterion is distance from existing stations, thus higher scores are given to sections of each rail corridor that are further away from current GO stations. This is done by converting the rail lines into a raster file so that the cost-distance tool can be implemented. Lastly, by using zonal statistics, scores can be given to census tracts that have rail lines passing through them based on the sum of each rail grid cell contained within them. However, to account for census tracts that are near rail lines but not intersecting them, a buffer of 1,500m was created around the scored census tracts, and then intersected onto the census tract shapefile to calculate the mean socioeconomic suitability of all the applicable census tracts.