Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 55, 2019
https://doi.org/10.5194/ica-proc-2-55-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 55, 2019
https://doi.org/10.5194/ica-proc-2-55-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

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

Brian Alan Johnson1, Rajarshi Dasgupta1, Shizuka Hashimoto2, Pankaj Kumar1, and Akio Onishi3 Brian Alan Johnson et al.
  • 1Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies, Japan
  • 2Integrated Research System for Sustainability Sciences (IR3S), The University of Tokyo, Japan
  • 3School of Data Science, Yokohama City University, Japan

Keywords: OpenStreetMap, Volunteered Geographic Information, urban parks, urban green space

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.

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