## 2 Related Work
### Multi-Criteria Decision-Making  
In [18], the authors employed Fuzzy AHP for the selection of a new site for a hospital using the factors travel time and population density surrounding the new site of a hospital. The authors of [19] used AHP and spacial data in an attempt to determine candidate landfill sites. As a result, they were able to find the best, good, and unsuitable landfill areas. [PERSON] et al. in [20] presented a TOPSIS-based approach for location planning under uncertainty. The uncertainty in their work was used to handle the lack of real data in location planning. In contrast, this work makes extensive use of real spatial data to compute alternative locations' factors/criteria computation. We believe that the use of real data can give us more accurate results. Furthermore, we made use of real spatial data to evaluate our approach. Besides their individual use, the AHP/TOPSIS approach is frequently used together for different multi-criteria decision-making problems. For instance, the authors in [21] made use of the AHP/TOPSIS hybrid approach to identify and rank the solutions of RL (reverse logistics) adoption to overcome its barriers. Fuzzy AHP is applied to obtain the weights of the barriers as criteria by pairwise comparison, and the final ranking of the solutions of RL adoption is obtained using fuzzy TOPSIS. The authors in [22] utilized the AHP/TOPSIS approach to select the best alternative, with an aim to improve the electronic supply chain management (e-SCM) performance of an Indian automobile industry. [PERSON] et al. [23] utilized AHP/TOPSIS to solve the problem of selection of a branch of students for the \"All Round Excellence Award\" from an engineering college. A CSPCM/TOPSIS approach for the quantification of accessibility to market facilities in rural areas was studied by [PERSON] et al. [24]. The authors made use of the Constant-Sum Paired-Comparison Method (CSPCM) to weight the factors and TOPSIS to rank the accessibility to market facilities. The study evaluated the accessibility of different urban and rural markets by using four factors, i.e., distance, time, cost, and road condition. Their study was mainly based on survey, i.e., a total of 335 questionnaire surveys were conducted from the whole study area (ten sub-districts) or, on average, 33.5 surveys per district, which is quite a small number for a district and is highly prone to bias. In contrast, we propose a real spatial data-based approach in this work, i.e., several million real dataset records and a spatial distance function are used to compute the criteria/factors weight. Furthermore, the accuracy of the obtained results are evaluated against real customer footprints. [PERSON] et al. [25] proposed a supplier selection analysis model considering both the AHP and TOPSIS method. Subjective and objective opinions of purchase managers/experts are quantified using AHP. The TOPSIS technique is used for calculating the supplier's ratings. The aim of their research is to determine the appropriate supplier providing the most customer satisfaction for the criteria identified in the supply chain. The proposed work presented in this paper provides a hybrid AHP/TOPSIS approach for pointing out an optimal site to open a commercial store. The reasons for selecting the AHP and TOPSIS methods are multi-fold. First and foremost is that the AHP and TOPSIS are among the most widely adopted MCDM techniques due to their simplicity and accuracy [26]. Secondly, when combined, they can reduce the computational efforts required to rank the alternatives. Other MCDM methods such as ANP, BWM, ELECTRE, or PROMETHEE would have been considered if one of our goals was not lowering the computational complexity and time required to find the ranking. Furthermore, TOPSIS works well with AHP quite nicely; that is, AHP is good for computing the criteria weights, while TOPSIS is good for ranking based on given weights.