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A spatial data model for urban spatial–temporal accessibility analysis

Author

Listed:
  • Zhangcai Yin

    (Wuhan University of Technology)

  • Zhanghaonan Jin

    (Wuhan University of Technology)

  • Shen Ying

    (Wuhan University)

  • Sanjuan Li

    (Wuhan University of Technology)

  • Qingquan Liu

    (Wuhan University)

Abstract

Time geography represents the uncertainty of the space–time position of moving objects through two basic structures, the space–time path and space–time prism, which are subject to the speed allowed in the travel environment. Thus, any attempt at a quantitative time-geographic analysis must consider the actual velocity with respect to space. In a trip, individuals tend to pass through structurally varying spaces, such as linear traffic networks and planar walking surfaces, which are not suitable for use in a single GIS spatial data model (i.e., network, raster) that is only applicable to a single spatial structure (i.e., point, line, polygon). In this study, a velocity model is developed for a traffic network and walking surface-constrained travel environment through the divide-and-conquer principle. The construction of this model can be divided into three basic steps: the spatial layering of the dual-constrained travel environment; independent modelling of each layer using different spatial data models; and generation of layer-based time-geographic framework by merging models of each layer. We demonstrate the usefulness of the model for studying the space–time accessibility of a moving object over a study area with varying spatial structures. Finally, an example is given to analyse the effectiveness of the proposed model.

Suggested Citation

  • Zhangcai Yin & Zhanghaonan Jin & Shen Ying & Sanjuan Li & Qingquan Liu, 2020. "A spatial data model for urban spatial–temporal accessibility analysis," Journal of Geographical Systems, Springer, vol. 22(4), pages 447-468, October.
  • Handle: RePEc:kap:jgeosy:v:22:y:2020:i:4:d:10.1007_s10109-020-00330-6
    DOI: 10.1007/s10109-020-00330-6
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    References listed on IDEAS

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    1. Tetsuo Kobayashi & Harvey Miller & Walied Othman, 2011. "Analytical methods for error propagation in planar space–time prisms," Journal of Geographical Systems, Springer, vol. 13(4), pages 327-354, December.
    2. Fayyaz, S. Kiavash & Liu, Xiaoyue Cathy & Porter, Richard J., 2017. "Dynamic transit accessibility and transit gap causality analysis," Journal of Transport Geography, Elsevier, vol. 59(C), pages 27-39.
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    4. Dykes, J. A. & Mountain, D. M., 2003. "Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 581-603, August.
    5. Shaw, Shih-Lung & Yu, Hongbo, 2009. "A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical–virtual space," Journal of Transport Geography, Elsevier, vol. 17(2), pages 141-149.
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    Cited by:

    1. Sławomir Goliszek, 2021. "GIS tools and programming languages for creating models of public and private transport potential accessibility in Szczecin, Poland," Journal of Geographical Systems, Springer, vol. 23(1), pages 115-137, January.
    2. Fu, Xiao & Zuo, Yufan & Zhang, Shanqi & Liu, Zhiyuan, 2022. "Measuring joint space-time accessibility in transit network under travel time uncertainty," Transport Policy, Elsevier, vol. 116(C), pages 355-368.

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