Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas
Accurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban space...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4201 |
| _version_ | 1797493398778675200 |
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| author | Chong Huang Chaoliang Xiao Lishan Rong |
| author_facet | Chong Huang Chaoliang Xiao Lishan Rong |
| author_sort | Chong Huang |
| collection | DOAJ |
| description | Accurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban spaces or the corresponding POI data has not been fully considered in previous studies. In this study, we proposed a new scheme for urban-functional-area identification by combining POI data, OpenStreetMap (OSM) datasets, and high-resolution remote-sensing imagery. A function-intensity index that integrates the quantitative-density index and average-nearest-neighbor index (ANNI) of POIs was built for representing the urban function. The results show that the proposed function-intensity index can balance the impact of the spatial heterogeneity of each type of POI on determining the functional characteristics of the urban units. In Futian District, Shenzhen, China, the method was effective in distinguishing functional areas with fewer POI amounts but high ANNIs from those functional areas with dense POIs. The overall accuracy of the proposed method is about 11% higher than that of the method using the POI density only. This paper argues for considering both the quantitative density and spatial heterogeneity of POIs to improve urban-functional-area identification. |
| first_indexed | 2024-03-10T01:19:29Z |
| format | Article |
| id | doaj.art-e3e79b42363f48f8a38b11602df3394f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:19:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e3e79b42363f48f8a38b11602df3394f2023-11-23T14:02:37ZengMDPI AGRemote Sensing2072-42922022-08-011417420110.3390/rs14174201Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional AreasChong Huang0Chaoliang Xiao1Lishan Rong2State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Civil Engineering, University of South China, Hengyang 421001, ChinaSchool of Civil Engineering, University of South China, Hengyang 421001, ChinaAccurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban spaces or the corresponding POI data has not been fully considered in previous studies. In this study, we proposed a new scheme for urban-functional-area identification by combining POI data, OpenStreetMap (OSM) datasets, and high-resolution remote-sensing imagery. A function-intensity index that integrates the quantitative-density index and average-nearest-neighbor index (ANNI) of POIs was built for representing the urban function. The results show that the proposed function-intensity index can balance the impact of the spatial heterogeneity of each type of POI on determining the functional characteristics of the urban units. In Futian District, Shenzhen, China, the method was effective in distinguishing functional areas with fewer POI amounts but high ANNIs from those functional areas with dense POIs. The overall accuracy of the proposed method is about 11% higher than that of the method using the POI density only. This paper argues for considering both the quantitative density and spatial heterogeneity of POIs to improve urban-functional-area identification.https://www.mdpi.com/2072-4292/14/17/4201points of interest (POIs)functional-zone identificationremote sensingaverage-nearest-neighbor indexurban functional area |
| spellingShingle | Chong Huang Chaoliang Xiao Lishan Rong Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas Remote Sensing points of interest (POIs) functional-zone identification remote sensing average-nearest-neighbor index urban functional area |
| title | Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas |
| title_full | Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas |
| title_fullStr | Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas |
| title_full_unstemmed | Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas |
| title_short | Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas |
| title_sort | integrating point of interest density and spatial heterogeneity to identify urban functional areas |
| topic | points of interest (POIs) functional-zone identification remote sensing average-nearest-neighbor index urban functional area |
| url | https://www.mdpi.com/2072-4292/14/17/4201 |
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