Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation
The refined identification of Urban Functional Zones (UFZs) is crucial for effective urban planning, resource allocation, and environmental monitoring. However, achieving high precision and comprehensive identification of UFZs has been challenging. The rapid proliferation and open accessibility of m...
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2025-01-01
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| author | Xuan Du Yisha Pan Xiaoyan Yang Longgao Chen Liangchen Liu Ying Lin |
| author_facet | Xuan Du Yisha Pan Xiaoyan Yang Longgao Chen Liangchen Liu Ying Lin |
| author_sort | Xuan Du |
| collection | DOAJ |
| description | The refined identification of Urban Functional Zones (UFZs) is crucial for effective urban planning, resource allocation, and environmental monitoring. However, achieving high precision and comprehensive identification of UFZs has been challenging. The rapid proliferation and open accessibility of multi-source data, such as remote sensing imagery and socio-economic datasets, have introduced new opportunities for the dynamic identification of UFZs. This paper proposes a Point of Interest (POI)-weighted scoring method based on Empirical Bayesian Kriging (EBK), which is empirically demonstrated in Xuzhou, China. We integrate multi-source data, including remote sensing-based land use/cover information, road networks, POIs, and building geometry data, to classify UFZs based on their dependence on buildings. Results indicate that: 1) The EBK interpolation method not only accounts for the spatial and directional distance relationships between known and unknown sample points, but also produces more accurate raster images and provides more precise estimates in unmeasured ranges. 2) The method successfully identifies UFZs for 99.5% of the buildings in the central urban area of Xuzhou. Additionally, the incorporation of multi-source data enables the extraction of traffic and ecological zones, facilitating the identification of non-building-reliant zones within the city, thereby enhancing the completeness and comprehensiveness of urban spatial recognition. 3) The overall accuracy achieved is 84.4%, with a Kappa coefficient of 0.804 for the classification results, which represents a significant improvement in UFZs identification accuracy compared to traditional methods and offers a robust scientific basis for urban planning and resource optimization. |
| format | Article |
| id | doaj-art-a57a8b719acc46ae8cb9365bbcc4ac58 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-a57a8b719acc46ae8cb9365bbcc4ac582025-02-07T00:01:01ZengIEEEIEEE Access2169-35362025-01-0113227842279910.1109/ACCESS.2025.353600010857297Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring InnovationXuan Du0https://orcid.org/0009-0006-9269-6819Yisha Pan1Xiaoyan Yang2Longgao Chen3Liangchen Liu4Ying Lin5College of Public Policy and Management (College of Emergency Management), China University of Mining and Technology, Xuzhou, ChinaCollege of Public Policy and Management (College of Emergency Management), China University of Mining and Technology, Xuzhou, ChinaCollege of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, ChinaCollege of Public Policy and Management (College of Emergency Management), China University of Mining and Technology, Xuzhou, ChinaCollege of Public Policy and Management (College of Emergency Management), China University of Mining and Technology, Xuzhou, ChinaCollege of Public Policy and Management (College of Emergency Management), China University of Mining and Technology, Xuzhou, ChinaThe refined identification of Urban Functional Zones (UFZs) is crucial for effective urban planning, resource allocation, and environmental monitoring. However, achieving high precision and comprehensive identification of UFZs has been challenging. The rapid proliferation and open accessibility of multi-source data, such as remote sensing imagery and socio-economic datasets, have introduced new opportunities for the dynamic identification of UFZs. This paper proposes a Point of Interest (POI)-weighted scoring method based on Empirical Bayesian Kriging (EBK), which is empirically demonstrated in Xuzhou, China. We integrate multi-source data, including remote sensing-based land use/cover information, road networks, POIs, and building geometry data, to classify UFZs based on their dependence on buildings. Results indicate that: 1) The EBK interpolation method not only accounts for the spatial and directional distance relationships between known and unknown sample points, but also produces more accurate raster images and provides more precise estimates in unmeasured ranges. 2) The method successfully identifies UFZs for 99.5% of the buildings in the central urban area of Xuzhou. Additionally, the incorporation of multi-source data enables the extraction of traffic and ecological zones, facilitating the identification of non-building-reliant zones within the city, thereby enhancing the completeness and comprehensiveness of urban spatial recognition. 3) The overall accuracy achieved is 84.4%, with a Kappa coefficient of 0.804 for the classification results, which represents a significant improvement in UFZs identification accuracy compared to traditional methods and offers a robust scientific basis for urban planning and resource optimization.https://ieeexplore.ieee.org/document/10857297/Urban functional zonesrefined identificationmulti-source dataempirical Bayesian krigingconfusion matrixurban planning |
| spellingShingle | Xuan Du Yisha Pan Xiaoyan Yang Longgao Chen Liangchen Liu Ying Lin Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation IEEE Access Urban functional zones refined identification multi-source data empirical Bayesian kriging confusion matrix urban planning |
| title | Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation |
| title_full | Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation |
| title_fullStr | Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation |
| title_full_unstemmed | Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation |
| title_short | Refined Urban Functional Zones Identification via Empirical Bayesian Kriging: A POI-Weighted Scoring Innovation |
| title_sort | refined urban functional zones identification via empirical bayesian kriging a poi weighted scoring innovation |
| topic | Urban functional zones refined identification multi-source data empirical Bayesian kriging confusion matrix urban planning |
| url | https://ieeexplore.ieee.org/document/10857297/ |
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