Create a GIS model: determine the type of model, devise a process to weigh factors, and design a model.
Through literature reviews, a Multi Criteria Evaluation (MCE) was determined to be the most fitting way to achieve the intended goal. A similar study conducted in Florida to determine placement of a railway station used Weighted Linear Combination (WLC) (Rosenberg and Esnard, 2008). Since transit is being assessed in this study, it is advisable to use the same method. A WLC is often incorporated into MCEs as an initial step, and part of a larger process, so it is believed that an MCE will produce a more complex and informative result.
To generate the criteria and constraint layers, the socioeconomic data is tabulated, extracted, and joined to the census tract shape file, linking their boundaries. They are then applied into a series of calculations that are crucial to an MCE, as expressed by the tables below. Table 1 explains the logistics of a pairwise comparison. A ninepoint scale is typically used in such scenarios to conceptualize the importance of one criteria over another (Bonnycastle, Yang, and Mersey, 2014). Table 2 displays the actual rankings given to each of the criteria in this study over one another. For example, the criteria 'Density' is given a 31 ranking over 'Median Income', meaning that the population density of a census tract is moderately more important to this study than the census tract's median income. The sum of the relative ranking is calculated vertically on the bottom of the table for each respective criterion. The relative rankings in each cell are then divided by the sum of their column, and then the mean is calculated horizontally in order to dermine the final weight each criterion, as demonstrated by Table 3. These weights are what determine the overall strength of each criterion in the analysis.
Table 1. Pairwise Table for Relative Comparisons
1/9 
1/7 
1/5 
1/3 
1 
3 
5 
7 
9 
Extremely 
Very Strongly 
Strongly 
Moderately 
Equally 
Moderately 
Strongly 
Very Strongly 
Extremely 
Table 2. Relative Pairwise Rankings

Median Income 
Immigration 
Minority 
Children 
Youth 
Adult 
Mature 
Senior 
Density 
PostSecondary 
Median Income 
1 
3 
3 
9 
9 
0.33 
7 
9 
0.33 
3 
Immigration 
0.33 
1 
1 
9 
7 
0.2 
5 
7 
0.14 
0.2 
Minority 
0.33 
1 
1 
9 
7 
0.5 
3 
7 
0.11 
0.14 
Children 
0.11 
0.11 
0.11 
1 
0.14 
0.11 
0.11 
0.33 
0.11 
0.11 
Youth 
0.11 
0.14 
0.14 
7 
1 
0.11 
0.33 
1 
0.11 
0.11 
Adult 
3 
5 
5 
9 
9 
1 
7 
9 
0.33 
0.33 
Mature 
0.14 
0.2 
0.33 
9 
3 
0.14 
1 
5 
0.14 
0.14 
Senior 
0.11 
0.14 
0.14 
3 
1 
0.11 
0.2 
1 
0.11 
0.11 
Density 
3 
7 
9 
9 
9 
3 
7 
9 
1 
3 
Post Secondary 
0.33 
5 
7 
9 
9 
3 
7 
9 
0.33 
1 
Sum 
8.49 
22.60 
26.73 
74 
55.14 
8.51 
37.64 
57.33 
2.73 
8.15 
Table 3. Individual Weights Calculation

Median Income 
Immigration 
Minority 
Children 
Youth 
Adult 
Mature 
Senior 
Density 
Post Secondary 
Weights 
Median Income 
0.118 
0.1328 
0.112 
0.121 
0.163 
0.039 
0.186 
0.157 
0.122 
0.368 
0.152 
Immigration 
0.039 
0.044 
0.037 
0.121 
0.127 
0.024 
0.133 
0.122 
0.052 
0.023 
0.072 
Minority 
0.039 
0.044 
0.037 
0.121 
0.127 
0.059 
0.079 
0.122 
0.041 
0.018 
0.069 
Children 
0.013 
0.005 
0.004 
0.013 
0.003 
0.013 
0.003 
0.006 
0.041 
0.014 
0.011 
Youth 
0.013 
0.006 
0.005 
0.094 
0.018 
0.013 
0.009 
0.017 
0.041 
0.014 
0.023 
Adult 
0.354 
0.221 
0.187 
0.121 
0.163 
0.118 
0.186 
0.157 
0.122 
0.041 
0.167 
Mature 
0.017 
0.009 
0.013 
0.121 
0.054 
0.017 
0.027 
0.087 
0.052 
0.018 
0.041 
Senior 
0.013 
0.006 
0.005 
0.040 
0.018 
0.013 
0.005 
0.017 
0.041 
0.014 
0.017 
Density 
0.354 
0.309 
0.337 
0.121 
0.163 
0.353 
0.186 
0.157 
0.366 
0.368 
0.271 
Post Secondary 
0.039 
0.221 
0.261 
0.121 
0.163 
0.353 
0.186 
0.157 
0.122 
0.123 
0.175 












Sum 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
Due to the amount of factors that were considered, it is helpful to use a consistency ratio to ensure the weights make sense logically (Bonnycastle, 2018). Table 4 shows the information used to calculate the consistency ratio. If the final number is less than 0.10, then the ranks given have a consistent ratio. The final calculated value is 0.06 so the ranks are weighted correctly relative to each other.
Table 4. Consistency Ratio

Weights*Sum 
Consistency Vector 
Median Income 
1.288 
10.115 
Immigration 
1.638 
7.957 
Minority 
1.839 
7.083 
Children 
0.846 
15.388 
Youth 
1.275 
10.222 
Adult 
1.422 
9.167 
Mature 
1.561 
8.349 
Senior 
0.995 
13.093 
Density 
0.741 
17.580 
Post Secondary 
1.425 
9.147 



Sum 
13.031 
108.101 
Average of Vectors 10.810
Consistency Index = (average  n) / (n  1) = 0.090008
Random Inconsistency Index (Constant) = 1.49
Consistency Ratio = CI / RI = 0.060408
The consistency ratio shows that the ranks are consistent if the output value is lower than 0.10, therefore the calculated ranks are consistent.