Geographical and temporal weighted regression (GTWR)

Fotheringham, A. S., Crespo, R. and Yao, J. (2015) Geographical and temporal weighted regression (GTWR). Geographical Analysis, 47(4), pp. 431-452. (doi: 10.1111/gean.12071)

104022.pdf - Accepted Version



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 (GIScience), 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 London 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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Yao, Dr Jing
Authors: Fotheringham, A. S., Crespo, R., and Yao, J.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Geographical Analysis
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN (Online):1538-4632
Copyright Holders:Copyright © 2015 The Ohio State University
First Published:First published in Geographical Analysis 47(4):431-452
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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