Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China
OpenStreetMap (OSM), an open, crowdsourced geographic information platform, holds significant potential in fields like urban planning and resource management. Currently, most research focuses primarily on data quality issues, without considering the evolution of OSM buildings. This paper employs the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2459109 |
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| _version_ | 1832087069270212608 |
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| author | Sidan Chen Lingjia Liu Kaixiang Li Xiaohui Ding Wei Jiang |
| author_facet | Sidan Chen Lingjia Liu Kaixiang Li Xiaohui Ding Wei Jiang |
| author_sort | Sidan Chen |
| collection | DOAJ |
| description | OpenStreetMap (OSM), an open, crowdsourced geographic information platform, holds significant potential in fields like urban planning and resource management. Currently, most research focuses primarily on data quality issues, without considering the evolution of OSM buildings. This paper employs the Markov-FLUS model to simulate and predict the expansion of OSM building data in Shenzhen. OSM building data in 2015 and 2019 were used to simulate the distribution of OSM buildings in 2023, and the distribution and completeness of OSM buildings in 2027 were subsequently simulated. The results indicate that by 2027, the growth rates of OSM buildings in Luohu and Longhua districts in Shenzhen will exceed 40%, with other areas growing by over 25%. The overall completeness of OSM buildings is projected to reach 39.99%. The simulation results can be used to identify future expansion of OSM building data in Shenzhen and support the sustainable development of OSM in the city. |
| format | Article |
| id | doaj-art-e6874f9844e7480da19451e704ea038f |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-e6874f9844e7480da19451e704ea038f2025-02-06T08:15:47ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2459109Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, ChinaSidan Chen0Lingjia Liu1Kaixiang Li2Xiaohui Ding3Wei Jiang4School of Geography and Environment, Jiangxi Normal University, Nanchang, ChinaSchool of Geography and Environment, Jiangxi Normal University, Nanchang, ChinaSchool of Geography and Environment, Jiangxi Normal University, Nanchang, ChinaSchool of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, ChinaChina Institute of Water Resources and Hydropower Research, Beijing, ChinaOpenStreetMap (OSM), an open, crowdsourced geographic information platform, holds significant potential in fields like urban planning and resource management. Currently, most research focuses primarily on data quality issues, without considering the evolution of OSM buildings. This paper employs the Markov-FLUS model to simulate and predict the expansion of OSM building data in Shenzhen. OSM building data in 2015 and 2019 were used to simulate the distribution of OSM buildings in 2023, and the distribution and completeness of OSM buildings in 2027 were subsequently simulated. The results indicate that by 2027, the growth rates of OSM buildings in Luohu and Longhua districts in Shenzhen will exceed 40%, with other areas growing by over 25%. The overall completeness of OSM buildings is projected to reach 39.99%. The simulation results can be used to identify future expansion of OSM building data in Shenzhen and support the sustainable development of OSM in the city.https://www.tandfonline.com/doi/10.1080/10106049.2025.2459109OpenStreetMapbuilding dataMarkov-FLUS modelspatiotemporal analysiscompleteness prediction |
| spellingShingle | Sidan Chen Lingjia Liu Kaixiang Li Xiaohui Ding Wei Jiang Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China Geocarto International OpenStreetMap building data Markov-FLUS model spatiotemporal analysis completeness prediction |
| title | Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China |
| title_full | Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China |
| title_fullStr | Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China |
| title_full_unstemmed | Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China |
| title_short | Simulation and prediction of the expansion of OpenStreetMap building data based on the Markov-FLUS model in Shenzhen, China |
| title_sort | simulation and prediction of the expansion of openstreetmap building data based on the markov flus model in shenzhen china |
| topic | OpenStreetMap building data Markov-FLUS model spatiotemporal analysis completeness prediction |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2459109 |
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