# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 4 Case Study
### Data Collection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Score** & **Weighting of Residential Area (Factor \(A\))** & **Sub-Sum** \\ \hline \(B\) & 100.0 & 0.29\% & 288.5 \\ C & 60.0 & 0.06\% & 34.6 \\ \(D\) & 11.7 & 0.67\% & 79.0 \\ \(E\) & 100.0 & 0.08\% & 76.9 \\ \(F\) & 17.2 & 0.58\% & 99.0 \\ \(G\) & 80.0 & 0.38\% & 307.7 \\ \(H\) & 50.0 & 0.06\% & 28.8 \\  & & Sum & 914.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction of the number of visitors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Factors** & & & **Base** & **Score** \\ \hline \multicolumn{8}{c}{_B_: Competitiveness of other CVSs} \\ \hline \multirow{2}{*}{BQ1} & Competitive store & Major & Minor & Local chain CVS & Self-operated & None & \\  & category & competitive & CVS (0) & CVS (20) & (30) & CVS (40) & (100) & 1 & 100 \\ \hline \multirow{2}{*}{BQ2} & Relative distance & \textless{}50 m (0) & 50–100 m (5) & 100–200 m (10) & \textgreater{}200 m (20) & & NA \\ \hline \multirow{2}{*}{BQ3} & Competitive store & \multirow{2}{*}{good (0)} & \multirow{2}{*}{acceptable (10)} & \multirow{2}{*}{poor (20)} & & & NA \\  & proximity & & & & & & NA \\ \hline \multirow{2}{*}{BQ4} & Competition store & \multirow{2}{*}{24 h} & \multirow{2}{*}{not 24 h} & \multirow{2}{*}{} & & & NA \\  & opening hours & & & & & & NA \\ \hline \multicolumn{8}{c}{C: Competitiveness of Supermarkets:} \\ \hline \multirow{2}{*}{CQ1} & \multirow{2}{*}{Number of stores} & \(\geq\)4 (0) & 3 (5) & 2 (20) & 1 (40) & none & \\  & & & & & (100) & 20 \\ \hline \multirow{2}{*}{CQ2} & Relative distance & \textless{}50 m (0) & 50–100 m (10) & 100–200 m (15) & \textgreater{}200 m (35) & & 35 \\ \hline \multirow{2}{*}{CQ3} & Competition store & \multirow{2}{*}{after 22:00 (0)} & \multirow{2}{*}{21:00–22:00 (5)} & \multirow{2}{*}{20:00–21:00 (10)} & before 20:00 & \\  & closed hours & & & & (15) & & 5 \\ \hline \multicolumn{8}{c}{_D_: People flow} \\ \hline DQ1 & Survey time & 07:00–09:00 & 12:00–14:00 & 17:00–19:00 & 21:00–23:00 & \\ \hline DQ2 & Pedestrian & & & 264 & 40 & 264/40 = 6.6 \\ \hline \multirow{2}{*}{DQ3} & Motorcycle & \multicolumn{3}{c}{2568} & \multirow{2}{*}{500} & \multirow{2}{*}{2568/500 = 5.1} \\ \hline \multicolumn{8}{c}{_E_: Major customer type} \\ \hline \multirow{2}{*}{EQ1} & \multicolumn{3}{c}{Single source (0)} & Two sources & \multirow{2}{*}{\(>\)Two sources (100)} & & 100 \\  & & & (80) & & & 100 \\ \hline \multicolumn{8}{c}{_F_: Number of households} \\ \hline \multirow{2}{*}{FQ2} & Number of & \multirow{2}{*}{} & \multirow{2}{*}{429} & \multirow{2}{*}{25} & \multirow{2}{*}{429/25 = 17.2} \\  & households & & & & & \\ \hline \multicolumn{8}{c}{G: Store location} \\ \hline \multirow{2}{*}{GQ1} & \multirow{2}{*}{Store location} & Off & Between minor & Between main & & 30 \\  & intersection (0) & streets (30) & streets (50) & & & 30 \\ \hline \multirow{2}{*}{GQ2} & Width & \textless{}6 m (0) & 6–10 m (20) & \textgreater{}10 m (50) & & 50 \\ \hline \multicolumn{8}{c}{_H_: Visibility} \\ \hline HQ1 & Visibility & \textless{}50 m (0) & 50–100 m (50) & \textgreater{}100 m (100) & & 50 \\ \hline \multicolumn{8}{c}{_I_: Area} \\ \hline IQ1 & Usable area & \textless{}50 m\({}^{2}\) (0.9) & 50–100 m\({}^{2}\) (1.0) & \textgreater{}100 m\({}^{2}\) (1.1) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scores of the factors. As shown in Table 5, the average actual daily turnover per month over six months in Store 1 is 55,023, and the ratio of the predicted daily turnover/average actual daily turnover is 0.78. If this value is smaller than 0.9, then the new store is defined as a failure. As depicted, the average difference between the actual daily turnover and the predicted  
\begin{table}
\begin{tabular}{r r r r r r r r r r r} \hline \hline
**Store** & **Predict.