Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis
Smart cities leverage technology, data, and digital infrastructure to enhance residents' quality of life, fostering sustainability and operational efficiency. To this end, the price data of rental housing in Hefei in 2022 were hereby collected to build a geographically weighted regression (GWR)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917424001028 |
| _version_ | 1797253889604452352 |
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| author | Wen Zhao Jie Zhong Jiale Lv |
| author_facet | Wen Zhao Jie Zhong Jiale Lv |
| author_sort | Wen Zhao |
| collection | DOAJ |
| description | Smart cities leverage technology, data, and digital infrastructure to enhance residents' quality of life, fostering sustainability and operational efficiency. To this end, the price data of rental housing in Hefei in 2022 were hereby collected to build a geographically weighted regression (GWR) model, and the mechanism of various factors on the price of rental housing in Hefei was discussed. Spatial correlation analysis revealed a notable autocorrelation of rental housing prices in Hefei, exhibiting a distribution pattern of decreasing values from the city center outward. Besides, the impact of 13 factors on house rent was examined. Overall, factors such as building area, proximity to wet markets, and distance from the top three hospitals were demonstrated to exert the most significant influence on rental housing prices in Hefei. The decision-making of smart cities needs to be scientifically analyzed to find out the factors affecting the price of rental housing in the region, and the research conclusions can provide certain references for urban decision-makers in the overall management of urban resources and targeted solutions to regional housing rental and other issues. |
| first_indexed | 2024-04-24T21:41:14Z |
| format | Article |
| id | doaj.art-213943b44d5941a9b22bdd86f3ecf5b9 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-24T21:41:14Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-213943b44d5941a9b22bdd86f3ecf5b92024-03-21T05:37:39ZengElsevierMeasurement: Sensors2665-91742024-06-0133101126Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysisWen Zhao0Jie Zhong1Jiale Lv2School of Urban Construction, Anhui Xinhua University, Hefei, 230088, China; Corresponding author.Anhui Fangdi Architectural Landscape Planning & Design Co., LTD., Hefei, 230088, ChinaSchool of Urban Construction, Anhui Xinhua University, Hefei, 230088, ChinaSmart cities leverage technology, data, and digital infrastructure to enhance residents' quality of life, fostering sustainability and operational efficiency. To this end, the price data of rental housing in Hefei in 2022 were hereby collected to build a geographically weighted regression (GWR) model, and the mechanism of various factors on the price of rental housing in Hefei was discussed. Spatial correlation analysis revealed a notable autocorrelation of rental housing prices in Hefei, exhibiting a distribution pattern of decreasing values from the city center outward. Besides, the impact of 13 factors on house rent was examined. Overall, factors such as building area, proximity to wet markets, and distance from the top three hospitals were demonstrated to exert the most significant influence on rental housing prices in Hefei. The decision-making of smart cities needs to be scientifically analyzed to find out the factors affecting the price of rental housing in the region, and the research conclusions can provide certain references for urban decision-makers in the overall management of urban resources and targeted solutions to regional housing rental and other issues.http://www.sciencedirect.com/science/article/pii/S2665917424001028Smart citiesSpatial differentiation characteristicsRental housing pricesGeographically weighted regression (GWR) modelInfluencing factors |
| spellingShingle | Wen Zhao Jie Zhong Jiale Lv Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis Measurement: Sensors Smart cities Spatial differentiation characteristics Rental housing prices Geographically weighted regression (GWR) model Influencing factors |
| title | Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis |
| title_full | Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis |
| title_fullStr | Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis |
| title_full_unstemmed | Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis |
| title_short | Influencing factors and spatial differentiation of rental housing in a smart city: A GWR model analysis |
| title_sort | influencing factors and spatial differentiation of rental housing in a smart city a gwr model analysis |
| topic | Smart cities Spatial differentiation characteristics Rental housing prices Geographically weighted regression (GWR) model Influencing factors |
| url | http://www.sciencedirect.com/science/article/pii/S2665917424001028 |
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