Evaluating neighborhood environment and utilitarian walking behavior with big data: A case study in tokyo metropolitan area

Abstract

Neighborhood environment for daily walking behavior has attracted highly academic attention in recent years because evidences have shown that ideal neighborhood environment can encourage walking behavior which is related to individual health. However, because of the difficulty in data collection, most of these studies concentrated on a small scale such as a street or a community. There is a need to do the study in a larger area for analyzing the results from a spatial view with an understanding of urban structure. The purpose of this study is to evaluate the neighborhood environment and utilitarian walking behavior in Tokyo Metropolitan Area and compare the results to check the relationships. Multi-criteria Evaluation (MCE) method was adopted for the evaluation of the neighborhood environment. Residential density, street connectivity, land use diversity, bus stop density and railway station accessibility, were the five criteria selected to run the MCE approach. The People Flow Data was utilized for the evaluation of people’s utilitarian walking behavior. The results showed a consistence of the two evaluation results in both spatial and statistical views. The findings supported that the approach of processing objective spatial data with GIS software was worth to be applied to other studies with a metropolitan-level.