4.5 Article

An Analysis of the Evolution, Completeness and Spatial Patterns of OpenStreetMap Building Data in China

出版社

MDPI
DOI: 10.3390/ijgi8010035

关键词

OpenStreetMap; China; building data; evolution; completeness; spatial pattern

资金

  1. National Natural Science Foundation of China [41771428, 71874165]
  2. China Institute of Geo-Environment Monitoring [0001212016CC60013]
  3. Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)
  4. Open Research Fund of Teaching Laboratory, China University of Geosciences (Wuhan)

向作者/读者索取更多资源

OpenStreetMap (OSM) is a free map that can be created, edited, and updated by volunteers globally. The quality of OSM datasets is therefore of great concern. Extensive studies have focused on assessing the completeness (a quality measure) of OSM datasets in various countries, but very few have been paid attention to investigating the OSM building dataset in China. This study aims to present an analysis of the evolution, completeness and spatial patterns of OSM building data in China across the years 2012 to 2017. This is done using two quality indicators, OSM building count and OSM building density, although a corresponding reference dataset for the whole country is not freely available. Development of OSM building counts from 2012 to 2017 is analyzed in terms of provincial- and prefecture-level divisions. Factors that may affect the development of OSM building data in China are also analyzed. A 1 x 1 km(2) regular grid is overlapped onto urban areas of each prefecture-level division, and the OSM building density of each grid cell is calculated. Spatial distributions of high-density grid cells for prefecture-level divisions are analyzed. Results show that: (1) the OSM building count increases by almost 20 times from 2012 to 2017, and in most cases, economic (gross domestic product) and OSM road length are two factors that may influence the development of OSM building data in China; (2) most grid cells in urban areas do not have any building data, but two typical patterns (dispersion and aggregation) of high-density grid cells are found among prefecture-level divisions.

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