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	Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China
	Harris R. ; Dong G. P. ; Zhang W. Z.
	2013
关键词	varying coefficient models house prices values water autocorrelation nonstationarity associations markets health space
英文摘要	Geographically Weighted Regression (GWR) is a method of spatial statistical analysis allowing the modeled relationship between a response variable and a set of covariates to vary geographically across a study region. Its use of geographical weighting arises from the expectation that observations close together by distance are likely to share similar characteristics. In practice, however, two points can be geographically close but socially distant because the contexts (or neighborhoods) within which they are situated are not alike. Drawing on a previous study of geographically and temporally weighted regression, in this article we develop what we describe as contextualized Geographically Weighted Regression (CGWR), applying it to the field of hedonic house price modeling to examine spatial heterogeneity in the land parcel prices of Beijing, China. Contextual variables are incorporated into the analysis by adjusting the geographical weights matrix to measure proximity not only by distance but also with respect to an attribute space defined by measures of each observation's neighborhood. Comparing CGWR with GWR suggests that adding the contextual information improves the model fit.
出处	Transactions in Gis
卷	17
期	6
页	901-919
收录类别	SCI
语种	英语
ISSN号	1361-1682
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/30528]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Harris R.,Dong G. P.,Zhang W. Z.. Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China. 2013.

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