Spatial Identification and Interactive Analysis of Urban Production—Living—Ecological Spaces Using Point of Interest Data and a Two-Level Scoring Evaluation Model

• Published in: Land (Basel) Vol. 11; no. 10; p. 1814
• Main Authors: Yang, Ying, Liu, Yawen, Zhu, Congmou, Chen, Xinming, Rong, Yi, Zhang, Jing, Huang, Bingbing, Bai, Longlong, Chen, Qi, Su, Yue, Yuan, Shaofeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2022
• Subjects: Bivariate analysis; Cities; Density; Distribution patterns; Ecological effects; GDP; Geospatial data; Gross Domestic Product; Hangzhou city; Heterogeneity; interactive relationship; Lakes; Land use; POI data; Population; production–living–ecological spaces; Remote sensing; Spatial analysis; Spatial heterogeneity; Statistical analysis; Transfer matrices; two-level scoring evaluation model; Urban areas; Urbanization; China; Yangtze River Delta
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11101814

Identifying urban production–living–ecological spaces and their interactive relationships is conducive to better understanding and optimizing urban space development. This paper took the main urban area of Hangzhou city as an example, and a two-level scoring evaluation model was constructed to accurately identify urban production–living–ecological spaces using point of interest (POI) data. Then, kernel density analysis, a spatial transfer matrix, and a bivariate spatial autocorrelation model were used to reveal the spatial patterns of urban production–living–ecological spaces and their interactive relationships during 2010 and 2019. The results showed that the proposed two-level scoring evaluation model combining both the physical area and density of POIs was effective in accurately identifying urban production–living–ecological spaces using POI data, with an identification accuracy of 88.9%. Urban production space was concentrated on the south bank of the Qiantang River and around the north of Hangzhou. Urban living space had the highest proportion, mainly distributed within the ring highway of Hangzhou in a contiguous distribution pattern, and urban ecological space was concentrated around West Lake and Xiang Lake. During 2010 and 2019, the expansion of urban production–living–ecological spaces had obvious spatial differences. Additionally, the mutual transformation between production and living spaces was more frequent during the study period and was mainly distributed within the ring highway of Hangzhou. There were significant positive spatial correlations between production and living and between living and ecological spaces, while a significant negative spatial correlation occurred between production and ecological spaces. The spatial correlations of urban production–living–ecological spaces revealed obvious spatial heterogeneity. This study proposed a two-level scoring evaluation model to accurately identify the spatial patterns of urban production–living–ecological spaces and their interactive relationships using POI data, which can provide detailed information and scientific references for urban spatial planning and management in rapidly urbanizing cities.