Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China
The traditional methods of estimating housing vacancies rarely use daytime housing exterior images to estimate housing vacancy rates (HVR). In view of this, this study proposed the idea and method of estimating urban housing vacancies based on daytime housing exterior images, taking Guangzhou, China...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/6/349 |
| _version_ | 1797486715839971328 |
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| author | Xiaoli Yue Yang Wang Yabo Zhao Hongou Zhang |
| author_facet | Xiaoli Yue Yang Wang Yabo Zhao Hongou Zhang |
| author_sort | Xiaoli Yue |
| collection | DOAJ |
| description | The traditional methods of estimating housing vacancies rarely use daytime housing exterior images to estimate housing vacancy rates (HVR). In view of this, this study proposed the idea and method of estimating urban housing vacancies based on daytime housing exterior images, taking Guangzhou, China as a case study. Considering residential quarters as the basic evaluation unit, the spatial pattern and its influencing factors were studied by using average nearest neighbor analysis, kernel density estimation, spatial autocorrelation analysis, and geodetector. The results show that: (1) The urban housing vacancy rate can be estimated by the method of daytime housing exterior images, which has the advantage of smaller research scale, simple and easy operation, short time consumption, and less difficulty in data acquisition. (2) Overall, the housing vacancy rate in Guangzhou is low in the core area and urban district, followed by suburban and higher in the outer suburb, showing a spatial pattern of increasing core area–urban district–suburban–outer suburb. Additionally, it has obvious spatial agglomeration characteristics, with low–low value clustered in the inner circle and high–high value clustered in the outer suburb. (3) The residential quarters with low vacancy rates (<5%) are distributed in the core area, showing a “dual-core” pattern, while residential quarters with high vacancy rates (>50%) are distributed in the outer suburb in a multi-core point pattern, both of which have clustering characteristics. (4) The results of the factor detector show that all seven influencing factors have an impact on the housing vacancy rate, but the degree of impact is different; the distance from CBD (Central Business District) has the strongest influence, while subway accessibility has the weakest influence. This study provides new ideas and methods for current research on urban housing vacancies, which can not only provide a reference for residents to purchase houses rationally, but also provide a decision-making basis for housing planning and policy formulation in megacities. |
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| format | Article |
| id | doaj.art-a72ae050fbde41089fd00169193de114 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T23:37:15Z |
| publishDate | 2022-06-01 |
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| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-a72ae050fbde41089fd00169193de1142023-11-23T16:59:12ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-06-0111634910.3390/ijgi11060349Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in ChinaXiaoli Yue0Yang Wang1Yabo Zhao2Hongou Zhang3School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, ChinaFaculty of Geography, Yunnan Normal University, Kunming 650500, ChinaSchool of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, ChinaGuangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaThe traditional methods of estimating housing vacancies rarely use daytime housing exterior images to estimate housing vacancy rates (HVR). In view of this, this study proposed the idea and method of estimating urban housing vacancies based on daytime housing exterior images, taking Guangzhou, China as a case study. Considering residential quarters as the basic evaluation unit, the spatial pattern and its influencing factors were studied by using average nearest neighbor analysis, kernel density estimation, spatial autocorrelation analysis, and geodetector. The results show that: (1) The urban housing vacancy rate can be estimated by the method of daytime housing exterior images, which has the advantage of smaller research scale, simple and easy operation, short time consumption, and less difficulty in data acquisition. (2) Overall, the housing vacancy rate in Guangzhou is low in the core area and urban district, followed by suburban and higher in the outer suburb, showing a spatial pattern of increasing core area–urban district–suburban–outer suburb. Additionally, it has obvious spatial agglomeration characteristics, with low–low value clustered in the inner circle and high–high value clustered in the outer suburb. (3) The residential quarters with low vacancy rates (<5%) are distributed in the core area, showing a “dual-core” pattern, while residential quarters with high vacancy rates (>50%) are distributed in the outer suburb in a multi-core point pattern, both of which have clustering characteristics. (4) The results of the factor detector show that all seven influencing factors have an impact on the housing vacancy rate, but the degree of impact is different; the distance from CBD (Central Business District) has the strongest influence, while subway accessibility has the weakest influence. This study provides new ideas and methods for current research on urban housing vacancies, which can not only provide a reference for residents to purchase houses rationally, but also provide a decision-making basis for housing planning and policy formulation in megacities.https://www.mdpi.com/2220-9964/11/6/349daytime housing exterior imagesurban housing vacanciesspatial patternsinfluencing factorsGuangzhou |
| spellingShingle | Xiaoli Yue Yang Wang Yabo Zhao Hongou Zhang Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China ISPRS International Journal of Geo-Information daytime housing exterior images urban housing vacancies spatial patterns influencing factors Guangzhou |
| title | Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China |
| title_full | Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China |
| title_fullStr | Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China |
| title_full_unstemmed | Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China |
| title_short | Estimation of Urban Housing Vacancy Based on Daytime Housing Exterior Images—A Case Study of Guangzhou in China |
| title_sort | estimation of urban housing vacancy based on daytime housing exterior images a case study of guangzhou in china |
| topic | daytime housing exterior images urban housing vacancies spatial patterns influencing factors Guangzhou |
| url | https://www.mdpi.com/2220-9964/11/6/349 |
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