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Geographical and Temporal Weighted Regression ( GTWR )
Author(s) -
Fotheringham A. Stewart,
Crespo Ricardo,
Yao Jing
Publication year - 2015
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12071
Subject(s) - geographically weighted regression , dimension (graph theory) , regression analysis , computer science , regression , econometrics , set (abstract data type) , spatial analysis , geography , statistics , mathematics , machine learning , pure mathematics , programming language
Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science ( GIS cience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression ( GWR ), geographical and temporal weighted regression ( GTWR ), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19‐year set of house price data in L ondon from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.