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Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data

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  • Xinxin Zhou

    (College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Yuan Ding

    (School of Earth Science and Engineering, Hohai University, Nanjing 211100, China)

  • Changbin Wu

    (College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems, Nanjing Normal University, Nanjing 210023, China)

  • Jing Huang

    (College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

  • Chendi Hu

    (College of Geographical Science, Nanjing Normal University, Nanjing 210023, China
    Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China)

Abstract

The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service (LBS) data, provides micro-scale knowledge about residential areas. The purpose is to characterize the spatial allocation rationality of the service facilities of residential areas from Internet map and LBS data. An Internet map provides exact geographical data (e.g., points of interests (POI)) and stronger route planning analysis capability through an application programming interface (API) (e.g., route planning API). Meanwhile, LBS data collected from mobile equipment afford detailed population distribution values. Firstly, we defined the category system of service facilities and calculated the available service facilities capacity of residential areas (ASFC-RA) through a scrappy algorithm integrated with the modified cumulative opportunity measure model. Secondly, we used Thiessen polygon spatial subdivision to gain the population distribution capacity of residential areas (PDC-RA) from Tencent LBS data at the representative moment. Thirdly, we measured the spatial allocation rationality of service facilities of residential areas (SARSF-RA) by combining ASFC-RA and PDC-RA. In this case, a trial strip census, consisting of serval urban residential areas from Wuxi City, Jiangsu Province, is selected as research area. Residential areas have been grouped within several ranges according to their SARSF-RA values. Different residential areas belong to different groups, even if they are spatially contiguous. Spatial locations and other investigation information coordinate with these differences. Those results show that the method that we proposed can express the micro-spatial allocation rationality of different residential areas dramatically, which provide a new data lens for various researchers and applications, such as urban residential areas planning and service facilities allocation.

Suggested Citation

  • Xinxin Zhou & Yuan Ding & Changbin Wu & Jing Huang & Chendi Hu, 2019. "Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1337-:d:210731
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    References listed on IDEAS

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    2. Yuan Yuan & Hongbo Li & Xiaolin Zhang & Xiaoliang Hu & Yahua Wang, 2019. "Emerging Location-Based Service Data on Perceiving and Measuring Multifunctionality of Rural Space: A Study of Suzhou, China," Sustainability, MDPI, vol. 11(20), pages 1-18, October.

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