Integrating spatial accessibility estimates derived from crowdsourced, commercial, and authoritative geo-datasets: Case study of mapping accessibility to urban green space in the Tokyo-Yokohama area

Abstract. Parks and other public green spaces (hereafter “urban green spaces”) provide many benefits to urban dwellers, but some residents receive few benefits due to a lack of urban green spaces nearby their home/workplace. Understanding spatial variations in urban green space accessibility is thus important for urban planning. As a case study, here we mapped urban green space accessibility in Japan’s highly urbanized Tokyo and Kanagawa Prefectures using a Gravity Model (GM). As the inputs for the GM, we used georeferenced datasets of urban green spaces obtained from various sources, including national government (Ministry of Land, Transportation, Infrastructure, and Tourism; MLIT), a commercial map provider (ESRI Japan Corporation), and a crowdsourcing initiative (OpenStreetMap). These datasets all varied in terms of their spatial and thematic coverage, as could be seen in the urban green space accessibility maps generated using each individual dataset alone. To overcome the limitations of each individual dataset, we developed an integrated urban green space accessibility map using a maximum value operator. The proposed map integration approach is simple and can be applied for mapping spatial accessibility to other goods and services using heterogeneous geographic datasets.