A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution
Buildings, as key factors influencing population distribution, have various functional attributes. Existing research mainly focuses on the relationship between land functions and population distribution at the macro scale, while neglecting the finer-grained, micro-scale impact of building functional...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4492 |
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| author | Dongfeng Ren Xin Qiu Zehua An |
| author_facet | Dongfeng Ren Xin Qiu Zehua An |
| author_sort | Dongfeng Ren |
| collection | DOAJ |
| description | Buildings, as key factors influencing population distribution, have various functional attributes. Existing research mainly focuses on the relationship between land functions and population distribution at the macro scale, while neglecting the finer-grained, micro-scale impact of building functionality on population distribution. To address this issue, this study integrates multi-source geospatial and spatio-temporal big data and employs the XGBoost algorithm to classify buildings into five functional categories: residential, commercial, industrial, public service, and landscape. The proposed model innovatively incorporates texture, geometric, and temporal features of building images, as well as socio-economic characteristics extracted using the distance decay algorithm. The results yield the following conclusions: (1) The proposed method achieves an overall classification accuracy of 0.77, which is 0.12 higher than that of the random forest-based approach. (2) The introduction of time features and the distance decay method further improved the model performance, increasing the accuracy by 0.04 and 0.03, respectively. (3) The correlation between the building functions and population distribution varies significantly across different scales. At the district and county levels, residential, commercial, and industrial buildings show a strong correlation with population distribution, whereas this correlation is relatively weak at the street scale. This study advances the understanding of building functions and their role in shaping population distribution, providing a robust framework for urban planning and population modeling. |
| format | Article |
| id | doaj-art-461ae8fec1c54e59964c689a0dac3674 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-461ae8fec1c54e59964c689a0dac36742024-12-13T16:31:02ZengMDPI AGRemote Sensing2072-42922024-11-011623449210.3390/rs16234492A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population DistributionDongfeng Ren0Xin Qiu1Zehua An2School of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaNari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, ChinaBuildings, as key factors influencing population distribution, have various functional attributes. Existing research mainly focuses on the relationship between land functions and population distribution at the macro scale, while neglecting the finer-grained, micro-scale impact of building functionality on population distribution. To address this issue, this study integrates multi-source geospatial and spatio-temporal big data and employs the XGBoost algorithm to classify buildings into five functional categories: residential, commercial, industrial, public service, and landscape. The proposed model innovatively incorporates texture, geometric, and temporal features of building images, as well as socio-economic characteristics extracted using the distance decay algorithm. The results yield the following conclusions: (1) The proposed method achieves an overall classification accuracy of 0.77, which is 0.12 higher than that of the random forest-based approach. (2) The introduction of time features and the distance decay method further improved the model performance, increasing the accuracy by 0.04 and 0.03, respectively. (3) The correlation between the building functions and population distribution varies significantly across different scales. At the district and county levels, residential, commercial, and industrial buildings show a strong correlation with population distribution, whereas this correlation is relatively weak at the street scale. This study advances the understanding of building functions and their role in shaping population distribution, providing a robust framework for urban planning and population modeling.https://www.mdpi.com/2072-4292/16/23/4492functional classification of buildingsXGBoost modelmulti-source geospatial and spatio-temporal big dataPearson coefficientdistance decay function |
| spellingShingle | Dongfeng Ren Xin Qiu Zehua An A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution Remote Sensing functional classification of buildings XGBoost model multi-source geospatial and spatio-temporal big data Pearson coefficient distance decay function |
| title | A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution |
| title_full | A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution |
| title_fullStr | A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution |
| title_full_unstemmed | A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution |
| title_short | A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution |
| title_sort | multi source data driven analysis of building functional classification and its relationship with population distribution |
| topic | functional classification of buildings XGBoost model multi-source geospatial and spatio-temporal big data Pearson coefficient distance decay function |
| url | https://www.mdpi.com/2072-4292/16/23/4492 |
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