Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder
With rapid urbanization, urban functional zones have become important for rational government and resource allocation. Points of interest (POIs), as informative and open-access data, have been widely used in studies of urban functions. However, most existing studies have failed to address unevenly o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/8/343 |
| _version_ | 1797584479746785280 |
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| author | Runpeng Xu Zhenjie Chen Feixue Li Chen Zhou |
| author_facet | Runpeng Xu Zhenjie Chen Feixue Li Chen Zhou |
| author_sort | Runpeng Xu |
| collection | DOAJ |
| description | With rapid urbanization, urban functional zones have become important for rational government and resource allocation. Points of interest (POIs), as informative and open-access data, have been widely used in studies of urban functions. However, most existing studies have failed to address unevenly or sparsely distributed POIs. In addition, the spatial adjacency of analysis units has been ignored. Therefore, we propose a new method for identifying urban functional zones based on POI density and marginalized graph autoencoder (MGAE). First, kernel density analysis was utilized to obtain the POI density and spread the effects of POIs to the surroundings, which enhanced the data from unevenly or sparsely distributed POIs considering the barrier effects of main roads and rivers. Second, MGAE performed feature extraction in view of the spatial adjacency to integrate features from the POIs of the surrounding units. Finally, the k-means algorithm was used to cluster units into zones, and semantic recognition was applied to identify the function category of each zone. A case study of Changzhou indicates that this method achieved an overall accuracy of 90.33% with a kappa coefficient of 0.88, which constitutes considerable improvement over that of conventional methods and can improve the performance of urban function identification. |
| first_indexed | 2024-03-10T23:53:15Z |
| format | Article |
| id | doaj.art-4e63867dfb1048839845b64d226f639b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T23:53:15Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-4e63867dfb1048839845b64d226f639b2023-11-19T01:24:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-08-0112834310.3390/ijgi12080343Identification of Urban Functional Zones Based on POI Density and Marginalized Graph AutoencoderRunpeng Xu0Zhenjie Chen1Feixue Li2Chen Zhou3School of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaWith rapid urbanization, urban functional zones have become important for rational government and resource allocation. Points of interest (POIs), as informative and open-access data, have been widely used in studies of urban functions. However, most existing studies have failed to address unevenly or sparsely distributed POIs. In addition, the spatial adjacency of analysis units has been ignored. Therefore, we propose a new method for identifying urban functional zones based on POI density and marginalized graph autoencoder (MGAE). First, kernel density analysis was utilized to obtain the POI density and spread the effects of POIs to the surroundings, which enhanced the data from unevenly or sparsely distributed POIs considering the barrier effects of main roads and rivers. Second, MGAE performed feature extraction in view of the spatial adjacency to integrate features from the POIs of the surrounding units. Finally, the k-means algorithm was used to cluster units into zones, and semantic recognition was applied to identify the function category of each zone. A case study of Changzhou indicates that this method achieved an overall accuracy of 90.33% with a kappa coefficient of 0.88, which constitutes considerable improvement over that of conventional methods and can improve the performance of urban function identification.https://www.mdpi.com/2220-9964/12/8/343urban functional zonePOIkernel density analysisgraph autoencoderk-meansChangzhou |
| spellingShingle | Runpeng Xu Zhenjie Chen Feixue Li Chen Zhou Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder ISPRS International Journal of Geo-Information urban functional zone POI kernel density analysis graph autoencoder k-means Changzhou |
| title | Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder |
| title_full | Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder |
| title_fullStr | Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder |
| title_full_unstemmed | Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder |
| title_short | Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder |
| title_sort | identification of urban functional zones based on poi density and marginalized graph autoencoder |
| topic | urban functional zone POI kernel density analysis graph autoencoder k-means Changzhou |
| url | https://www.mdpi.com/2220-9964/12/8/343 |
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