Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (2): 85-97.doi: 10.11947/j.JGGS.2022.0209

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Towards Exploring Patterns of Editing Behavior on OpenStreetMap

Zhiyao ZHAO1,3(),Hongchao FAN2()   

  1. 1. China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China
    2. Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway
    3. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611765, China
  • Received:2021-10-29 Accepted:2022-05-05 Online:2022-06-20 Published:2022-07-22
  • Contact: Hongchao FAN;
  • About author:Zhiyao ZHAO, E-mail:
  • Supported by:
    National Natural Science Foundation of China(41771484)


OpenStreetMap has a large number of volunteers. There is a hypothesis that volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM. It may be strongly related to data quality and data reliability on OSM. As for the heterogeneity and the reliability of OSM data, previous research usually focuses on the geometric accuracy, spatial location accuracy and semantic integrity of OSM data, while few researchers have analyzed these problems from the perspective of editing behavior. On the grounds of relationship between mapping motivation and editing behavior, the dispersion of editing trajectory and clockwise direction index are proposed in the paper to explore whether the volunteers are sufficiently motivated and knowledgeable. In the experiments, the historical OSM data of four countries suggested that developed countries have lower trajectory dispersion. The lower degree of trajectory dispersion reflects the higher concentration and professionalism of volunteers. A high degree of drawing direction consistency shows volunteers who mapped French data were natives with local knowledge. From this point of view, this paper verifies that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

Key words: editing behavior; historical OSM data; data quality heterogeneity; data reliability