Dual-environment feature fusion-based method for estimating building-scale population distributions
Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues. However, the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source g...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-11-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2023.2281571 |
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| author | Guangyu Liu Rui Li Jing Xia Zhaohui Liu Jing Cai Huayi Wu Mingjun Peng |
| author_facet | Guangyu Liu Rui Li Jing Xia Zhaohui Liu Jing Cai Huayi Wu Mingjun Peng |
| author_sort | Guangyu Liu |
| collection | DOAJ |
| description | Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues. However, the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data, which causes significant errors in population estimation. To address this problem, this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion (DEFF). The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings. Meanwhile, Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) method was proposed to assign appropriate weights to the features of the external environment for better feature fusion. Finally, a regression model was established using dual-environment features for building-scale population estimation. The experimental areas chosen for this study were Jianghan and Wuchang Districts, both located in Wuhan City, China. The estimated results of the DEFF were compared with those of the ablation experiments, as well as three publicly accessible population datasets, specifically LandScan, WorldPop, and GHS-POP, at the community scale. The evaluation results showed that DEFF had an [Formula: see text] of approximately 0.8, Mean Absolute Error (MAE) of approximately 1200, Root Mean Square Error (RMSE) of approximately 1700, and both Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) of approximately 26%, indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation. |
| format | Article |
| id | doaj-art-8d61ce933e4d4ccbab8d40a2853d1a5d |
| institution | DOAJ |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-8d61ce933e4d4ccbab8d40a2853d1a5d2025-08-20T02:50:00ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532024-11-012761943195810.1080/10095020.2023.2281571Dual-environment feature fusion-based method for estimating building-scale population distributionsGuangyu Liu0Rui Li1Jing Xia2Zhaohui Liu3Jing Cai4Huayi Wu5Mingjun Peng6State Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaWuhan Geomatics Institute, Wuhan, ChinaInformation on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues. However, the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data, which causes significant errors in population estimation. To address this problem, this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion (DEFF). The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings. Meanwhile, Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) method was proposed to assign appropriate weights to the features of the external environment for better feature fusion. Finally, a regression model was established using dual-environment features for building-scale population estimation. The experimental areas chosen for this study were Jianghan and Wuchang Districts, both located in Wuhan City, China. The estimated results of the DEFF were compared with those of the ablation experiments, as well as three publicly accessible population datasets, specifically LandScan, WorldPop, and GHS-POP, at the community scale. The evaluation results showed that DEFF had an [Formula: see text] of approximately 0.8, Mean Absolute Error (MAE) of approximately 1200, Root Mean Square Error (RMSE) of approximately 1700, and both Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) of approximately 26%, indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.https://www.tandfonline.com/doi/10.1080/10095020.2023.2281571Building scalemulti-source data fusionestimation of population distributiondual environmentData Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) |
| spellingShingle | Guangyu Liu Rui Li Jing Xia Zhaohui Liu Jing Cai Huayi Wu Mingjun Peng Dual-environment feature fusion-based method for estimating building-scale population distributions Geo-spatial Information Science Building scale multi-source data fusion estimation of population distribution dual environment Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) |
| title | Dual-environment feature fusion-based method for estimating building-scale population distributions |
| title_full | Dual-environment feature fusion-based method for estimating building-scale population distributions |
| title_fullStr | Dual-environment feature fusion-based method for estimating building-scale population distributions |
| title_full_unstemmed | Dual-environment feature fusion-based method for estimating building-scale population distributions |
| title_short | Dual-environment feature fusion-based method for estimating building-scale population distributions |
| title_sort | dual environment feature fusion based method for estimating building scale population distributions |
| topic | Building scale multi-source data fusion estimation of population distribution dual environment Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution (DQW-TOPSIS) |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2023.2281571 |
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