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

Xingjian Liu & Ying Long

Environment and Planning B: Urban Analytics and City Science2015https://doi.org/10.1177/0265813515604767article
ABDC A*
Weight
0.75

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.

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https://doi.org/https://doi.org/10.1177/0265813515604767

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@article{xingjian2015,
  title        = {{Automated identification and characterization of parcels with OpenStreetMap and points of interest}},
  author       = {Xingjian Liu & Ying Long},
  journal      = {Environment and Planning B: Urban Analytics and City Science},
  year         = {2015},
  doi          = {https://doi.org/https://doi.org/10.1177/0265813515604767},
}

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

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0.75

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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