Measuring Perceived Walkability at the City Scale Using Open Data

Abstract

The walkability of the built environment has been shown to be critical to the health of residents, and open data have been widely used to assess walkability. However, previous research has focused on the relationship between the built environment and walking behavior rather than perceived walkability, and there is a lack of systematic research on walkability at the urban scale using open data. This paper presents a methodological framework for systematically measuring and assessing perceived walkability at the urban scale, considering general and specific features. The walkability indices are obtained using variables from open data or calculated automatically through machine learning and algorithms to ensure they can be evaluated at a larger urban scale. The proposed method is applied to Harbin, China, to assess the perceived walkability of streets using hundreds of thousands of street view images and points of interest obtained from open data. The results are compared with a subjective evaluation of walkability to validate the proposed method. The results demonstrate that measures of the urban built environment can describe perceived walkability. Thus, the proposed framework shows promise for assessing the walkability of urban spaces, supporting policy proposals, and establishing design guidelines for optimising urban spaces.