## 6 Experiments
### Datasets and Experimental Setup  
For the evaluation, we made use of NYC conveninece store checkin data available from FourSquare [45]. To obtain convenience stores' location data and their visitor counts from FourSquare, the developers' places API was used. API calls to APIs can be broken down into two categories: regular and premium. Regular API calls only return basic information including the venue location, category, and a venue ID. Premium API calls return rich content including the number of visitors. In order to obtain convenience store data, premium API calls were used as we were interested in convenience store's visitor count in addition to its location information.  
For the sake of evaluation, we ranked the NYC convenience stores in descending order with respect to their visitor count and used it as _ground truth_. Although the visitor count is reliable, the duration of the visitor count is not known. We then used our proposed AHP/TOPSIS approach to rank the NYC convenience stores. The obtained ranking is compared against the ground truth, and the TPR is computed.  
For the convenience stores' ideal location, we identified the following five criteria based on the intuition that most of the convenience stores' customers come from these locations:  
1. TStations: Transportation stations (train, metro, bus, stations, etc.);  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Candidates** & \(S_{i}^{+}\) & \(S_{i}^{-}\) & \(P_{i}\) & **Rank** \\ \hline
1 & 40.8839, \(-\)73.8561 & 0.297131401 & 0.144593761 & 0.327338747 & 8 \\
2 & 40.8095, \(-\)73.8807 & 0.254017926 & 0.299058138 & 0.540717918 & 3 \\
3 & 40.8347, \(-\)73.9177 & 0.348682791 & 0.05856651 & 0.143809971 & 9 \\
4 & 40.7931, \(-\)73.9675 & 0.204902022 & 0.303211003 & 0.596739284 & 2 \\
5 & 40.7586, \(-\)73.9731 & 0.011381735 & 0.392231042 & 0.97180036 & 1 \\
6 & 40.7699, \(-\)73.9271 & 0.250201973 & 0.241413717 & 0.491061864 & 5 \\
7 & 40.7496, \(-\)73.8692 & 0.266998664 & 0.212344026 & 0.442990016 & 6 \\
8 & 40.7231, \(-\)73.914 & 0.254352423 & 0.6269993794 & 0.514915118 & 4 \\
9 & 40.6686, \(-\)73.9321 & 0.391639917 & 0.004541242 & 0.011462539 & 10 \\
10 & 40.6599, \(-\)73.8701 & 0.283262679 & 0.173548531 & 0.37991303 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Candidate \(S_{i}^{-}\), \(S_{i}^{+}\), and \(P_{i}\) values and their ranking.  
Buildings: Buildings including residential, commercial, etc.;
* EVenues: Entertainment venues (museums, movie theaters, stadiums, etc.);
* Shops: Other shops in the vicinity; and
* PPlaces: Professional places (convention centers, medical centers, factories, etc.)  
The data (venue information) related to the criteria _TStations_, _EVenues_, _Shops_, and _PPlaces_ were obtained from the FourSquare developers' places API [46]. Using the API, up to date check-in information was obtained for over 62 million global venues (As of 31 August 2020). The data related to criteria _Buildings_ was obtained from the NYC buildings footprint data [47]. Building footprints represent the full perimeter outline of each building as viewed from directly above. Besides the perimeter, other useful attributes of this dataset included ground elevation at building base, roof height above ground elevation, construction year, and feature type. The Buildings dataset consists of more than 1 million NYC building information including residential, commercial, and government buildings.