Modeling walking accessibility to urban parks using Google Maps crowdsourcing database in the high-density urban environments of Hong Kong

Abstract

Abstract Accessing urban parks is important for promoting physical activities and improving public health. In this study, we propose the use of Google Maps crowdsourcing data and the incorporation of park attractiveness to model urban park accessibility in the complex urban environments of Hong Kong. The difference between using geometric and route distance, the effect of park attractiveness in measuring accessibility, and the benefits gained from using walk time compared to distance are investigated. Our result shows that (1) route and geometric distances have a strong correlation with a conversion factor of about 1.5; (2) the common assumption that park size can be a proxy for describing attractiveness may not be correct. Instead, park attractiveness should be explicitly considered for a more effective accessibility modeling; and (3) estimation by walking time shows that there are non-negligible impacts from street conditions and traffic on urban park accessibility. Moreover, district hotspots short of park accessibility or attractiveness can be explicitly detected. Overall, this developed approach provides a flexible and informative approach to model the accessibility to urban parks. The outputs will help city planners, health professionals, and policymakers to evaluate and improve urban park planning and equity in accessibility in high-density cities.