# Spatial Image-Based Walkability Evaluation Using Regression Model
## 3 Experimental Results  
### Training the Regression Models  
The five models are trained on image scores obtained by the TrueSkill algorithm and semantic segmentation results containing 150 feature distribution. Table 4 shows the performances of the regression models. The multiple linear regression exhibited accuracy of 71.15%, and its dependent variable (image score) for the independent variable (150 classes) was unstable; this model was considered unsuitable for image scores prediction. Ridge, LASSO regression and SVR exhibited accuracies of 71.19, 68.64 and 72.91%, respectively. XGBoost exhibited accuracy of 76.60%, which was higher than the accuracy levels of linear and nonlinear regressions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RSME** & **R2** & **Accuracy** \\ \hline Multiple & 1,893,901.1099 & 1.1760 & 108,443,675.1976 & \(-\)5.8665 & 71.15\% \\ \hline Ridge & 0.0804 & 0.0104 & 0.1024 & 0.4768 & 71.19\% \\ \hline LASSO & 0.0900 & 0.0127 & 0.1128 & 0.3650 & 68.64\% \\ \hline SVR & 0.0931 & 0.0087 & 0.0953 & 0.5635 & 72.91\% \\ \hline XGBoost & 0.0743 & 0.0089 & 0.0947 & 0.5522 & 76.60\% \\ \hline [PERSON] et al. [17] & & & & & 73.50\% \\ \hline [PERSON] et al. [25] & & & & & 75.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model results for walkability evaluations. [PERSON] et al. [17] conducted crowdsourcing evaluation to select urban landscape suitable for six perceptions (Safe, Lively, Beautiful, Wealthy, Depressing, Boring), whereas this study evaluated only one perception (walkability). Despite the differences in evaluation indicators, it was judged feasible to compare as both studies conducted qualitative human evaluation of the walking environment. Ranking StreetScore (RSS)-CNN [17], which predicts scores for pairwise comparisons, exhibited an accuracy of 73.5%. The deep learning model based on the RSS-CNN architecture was built by [PERSON] et al. [25], achieving an accuracy of 75.01% when trained on the same data as in this study. This was higher than those of ridge, LASSO regression, and SVR built in this study but lower than that of XGBoost. According to reference [37] about XGBoost, a tree boosting system, generates individual trees and provides an algorithm for weighted quantile sketch for appropriate tree learning. It gives a weight to the weakly trained samples which are then reflected in the next learning model to reinforce the error. That is, in this study, it can be seen that XGBoost, a gradient boosting system, exhibited higher performance than other linear and nonlinear regression models. Table 5 compares the walkability scores predicted by the regression models with those obtained using the TrueSkill algorithm. For a first image with score of 88.32 points calculated by TrueSkill algorithm, ridge regression most closely predicted with 64.53 points. For the second image with a score of 49.47 points by TrueSkill algorithm, XGBoost predicted the closest score of 49.84 with a difference of 0.37. Therefore, it was determined that XGBoost, which demonstrated the best performance among the five regression models and provided score predictions similar to those generated by the TrueSkill algorithm, is suitable for predicting image scores in the walkability evaluation. Various regression models have been trained using semantic segmentation results to evaluate walkability; however, their accuracies did not reach 76%. It was determined that the crowdsourcing results were unreliable because walkability evaluation was not conducted properly by [PERSON] et al. [31]. Therefore, in this study, a web application was built to verify the data structure of the evaluation results. The web application visually provided the image information dataset and the pairwise image score dataset constructed in Section 2.1.2. Each image was compared 16 times with other images in the walkability evaluation, and was classified in the web application based on the number of times it was selected by the evaluator.