Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the conte...

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Autores principales:	Zhengyuan Chai, Yi Yang, Yangyang Zhao, Yonghu Fu, Ling Hao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	residential land prices spatial and temporal non-stationarity contextualized geographically and temporally weighted regression Shijiazhuang Agriculture S
Acceso en línea:	https://doaj.org/article/6c38a75bed0e409aa79f4e1d0cb68d0b
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spelling	oai:doaj.org-article:6c38a75bed0e409aa79f4e1d0cb68d0b2021-11-25T18:09:16ZExploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model10.3390/land101111482073-445Xhttps://doaj.org/article/6c38a75bed0e409aa79f4e1d0cb68d0b2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1148https://doaj.org/toc/2073-445XA spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R<sup>2</sup> value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China.Zhengyuan ChaiYi YangYangyang ZhaoYonghu FuLing HaoMDPI AGarticleresidential land pricesspatial and temporal non-stationaritycontextualized geographically and temporally weighted regressionShijiazhuangAgricultureSENLand, Vol 10, Iss 1148, p 1148 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	residential land prices spatial and temporal non-stationarity contextualized geographically and temporally weighted regression Shijiazhuang Agriculture S
spellingShingle	residential land prices spatial and temporal non-stationarity contextualized geographically and temporally weighted regression Shijiazhuang Agriculture S Zhengyuan Chai Yi Yang Yangyang Zhao Yonghu Fu Ling Hao Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
description	A spatial and temporal heterogeneity analysis of residential land prices, in general, is crucial for maintaining high-quality economic development. Previous studies have attempted to explain the geographical evolution rule by studying spatial-temporal heterogeneity, but they have neglected the contextual information, such as school district, industrial zone, population density, and job density, associated with residential land prices. Therefore, in this study, we consider contextual factors and propose a revised local regression algorithm called the contextualized geographically and temporally weighted regression (CGTWR), to effectively address spatiotemporal heterogeneity, and to creatively extend the feasibility of importing the contextualization into the GTWR model. The quantitative impact of contextual information on residential land prices was identified in Shijiazhuang (SJZ) city from 1974 to 2021. Empirical analyses demonstrated that school district and industrial zone factors played important roles in residential land prices. Notably, the distance from a residential area to an industrial zone was significantly positively correlated with residential land prices. In addition, a positive relationship between school districts and residential land prices was also observed. Finally, the R<sup>2</sup> value of the CGTWR model was 92%, which was superior to those of ordinary least squares (OLS, 76%), geographically weighted regression (GWR, 85%), contextualized geographically weighted regression (CGWR, 86%), and GTWR (90%) models. These evaluation results indicate that the CGTWR algorithm, which incorporates contextual information and spatiotemporal variation, could provide policy makers with evidence for understanding the nature of varying relationships within a land price dataset in China.
format	article
author	Zhengyuan Chai Yi Yang Yangyang Zhao Yonghu Fu Ling Hao
author_facet	Zhengyuan Chai Yi Yang Yangyang Zhao Yonghu Fu Ling Hao
author_sort	Zhengyuan Chai
title	Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
title_short	Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
title_full	Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
title_fullStr	Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
title_full_unstemmed	Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model
title_sort	exploring the effects of contextual factors on residential land prices using an extended geographically and temporally weighted regression model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6c38a75bed0e409aa79f4e1d0cb68d0b
work_keys_str_mv	AT zhengyuanchai exploringtheeffectsofcontextualfactorsonresidentiallandpricesusinganextendedgeographicallyandtemporallyweightedregressionmodel AT yiyang exploringtheeffectsofcontextualfactorsonresidentiallandpricesusinganextendedgeographicallyandtemporallyweightedregressionmodel AT yangyangzhao exploringtheeffectsofcontextualfactorsonresidentiallandpricesusinganextendedgeographicallyandtemporallyweightedregressionmodel AT yonghufu exploringtheeffectsofcontextualfactorsonresidentiallandpricesusinganextendedgeographicallyandtemporallyweightedregressionmodel AT linghao exploringtheeffectsofcontextualfactorsonresidentiallandpricesusinganextendedgeographicallyandtemporallyweightedregressionmodel
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Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

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