# Spatial Image-Based Walkability Evaluation Using Regression Model
## 1 Introduction
(2019) constructed a multi-level regression model to investigate the relationship between the residential environments and walking activities in Seoul, and supported [PERSON]'s urban vitality theory that \"Urban vitality is exerted by people's walking activities, and these activities are related to the built environment\". [PERSON] et al. (2019) proposed a method for identifying the characteristics of public spaces using the SegNet model and quantifying the perceptions of landscapes through discrete choice models. [PERSON] et al. (2018) used a transformer network to extract semantic features and proposed a human perception model that enhances feature representations to integrate visual elements. The correlation between environmental perceptions and resident activities were explored using the SlowFast network. Areas perceived as \"wealthy\", which showed a high active index, comprised high-rise buildings and considerable traffic flow, whereas those perceived as \"beautiful\" comprised greenery and landscapes. [PERSON] et al. (2019, 2020) extracted the street environment from the perspective of pedestrians using Google Street View images and semantic segmentation techniques, trained a model to predict pedestrian satisfaction with street environments, and compared its performance with that of other models. By analyzing the relationship between street environmental factors and walking satisfaction, they determined that walking satisfaction improved as visual complexity increased. [PERSON] et al. (2019) analyzed the relationship between street environmental factors and pedestrian satisfaction through semantic segmentation using Deeplabv3+, object detection using YOLOv3, and Multilevel ordered logistic regression analysis. They found that pedestrian satisfaction was low in street environments with many cars or signs, whereas more green spaces increased their satisfaction. Additionally, they emphasized that the visual factors considered important in street environments vary because the subjective criteria differ from person to person based on their social status or personal experiences. According to these studies, the walking environment and human walking activities are closely related, and the components of the walking environment are related to human emotions. This study proposes a method for predicting walkability scores of evaluations by using regression models and synthesizing the walking environments preferred by individuals. The main contributions of this study are as follows:  
1. It proposes regression models that predict image scores based on the walking environment, trained on the results of a walkability evaluation conducted through crowdsourcing. 2. The regression models, trained on the results of the walkability evaluation, analyze the features influencing the image score and extract feature importance accordingly. It verifies and compares the accuracy of the proposed models after retraining it using the extracted feature importance. The remainder of this paper is organized as follows: Section 2 presents the methods for conducting the walkability evaluation and analyzing environmental features. Section 3 presents the results of the experiment conducted using the proposed methods. Finally, Section 4 concludes the study.