Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China
文献类型：SCI/SSCI论文
| 作者 | Harris R. ; Dong G. P. ; Zhang W. Z. |
| 发表日期 | 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 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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