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Automated identification and characterization of parcels with OpenStreetMap and points of interest

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  • Xingjian Liu
  • Ying Long

Abstract

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

Suggested Citation

  • Xingjian Liu & Ying Long, 2016. "Automated identification and characterization of parcels with OpenStreetMap and points of interest," Environment and Planning B, , vol. 43(2), pages 341-360, March.
    • Handle: RePEc:sae:envirb:v:43:y:2016:i:2:p:341-360
    DOI: 10.1177/0265813515604767
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    References listed on IDEAS

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    1. Lingjun Tang & Yu Lin & Sijia Li & Sheng Li & Jingyi Li & Fu Ren & Chao Wu, 2018. "Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    2. Yan Ma & Feng Xue, 2019. "Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    3. Mingming Cai & Yaolin Liu & Minghai Luo & Lijun Xing & Yanfang Liu, 2019. "Job Accessibility from a Multiple Commuting Circles Perspective Using Baidu Location Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    4. 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.
    5. Jing Wu & Jingwen Li & Yue Ma, 2019. "Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    6. Ying Long & Kang Wu, 2017. "Simulating Block-Level Urban Expansion for National Wide Cities," Sustainability, MDPI, vol. 9(6), pages 1-19, May.

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