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Measuring Spatial Variations in Relationships with Geographically Weighted Regression

In: Recent Developments in Spatial Analysis

Author

Listed:
  • A. Stewart Fotheringham

    (University of Newcastle)

  • Martin Charlton

    (University of Newcastle)

  • Chris Brunsdon

    (University of Newcastle)

Abstract

A frequent aim of data analysis is to identify relationships between pairs of variables, often after negating the effects of other variables. By far the most common type of analysis used to achieve this aim is that of regression in which relationships between one or more independent variables and a single dependent variable are estimated. In spatial analysis, the data are drawn from geographical units and a single regression equation is estimated. This has the effect of producing ‘average’ or ‘global’ parameter estimates which are assumed to apply equally over the whole region. That is, the relationships being measured are assumed to be stationary over space. Relationships which are not stationary, and which are said to exhibit spatial nonstationarity, create problems for the interpretation of parameter estimates from a regression model. It is the intention of this paper to describe a statistical technique, which we refer to as Geographically Weighted Regression (GWR), which can be used both to account for and to examine the presence of spatial non-stationarity in relationships.

Suggested Citation

  • A. Stewart Fotheringham & Martin Charlton & Chris Brunsdon, 1997. "Measuring Spatial Variations in Relationships with Geographically Weighted Regression," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 4, pages 60-82, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-03499-6_4
    DOI: 10.1007/978-3-662-03499-6_4
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    Citations

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    Cited by:

    1. Coro Chasco Yrigoyen, 2004. "Modelos De Heterogeneidad Espacial," Econometrics 0411004, University Library of Munich, Germany.
    2. Oğuz Işik & M. Melih Pinarcioğlu, 2006. "Geographies of a silent transition: a geographically weighted regression approach to regional fertility differences in Turkey [Géographie d’une transition silencieuse: une approche des différences ," European Journal of Population, Springer;European Association for Population Studies, vol. 22(4), pages 399-421, December.
    3. Chasco, Coro & García, Isabel & Vicéns, José, 2007. "Modeling spatial variations in household disposable income with Geographically Weighted Regression," MPRA Paper 1682, University Library of Munich, Germany.
    4. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Figueiredo, Adriano Marcos Rodrigues & Bonjour, Sandra Cristina de Moura & Teixeira, Erly Cardoso & Helfand, Steven M., 2011. "Spatial analysis of agricultural supply response in the Brazilian Center-West," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 15.
    6. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    7. Ning Wang & Chang-Lin Mei & Xiao-Dong Yan, 2008. "Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique," Environment and Planning A, , vol. 40(4), pages 986-1005, April.
    8. Maria del Carmen Pérez González & Lidia Valiente Palma, 2020. "The “business–territory” relationship of cooperative societies as compared to the conventional business sector in the region of Andalusia," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 91(4), pages 565-583, December.
    9. Muhan Lv & Ningcheng Wang & Shenjun Yao & Jianping Wu & Lei Fang, 2021. "Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    10. Arezoo Mokhtari & Behnam Tashayo & Kaveh Deilami, 2021. "Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM 2.5 Estimation," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    11. LE GALLO, Julie, 2000. "Econométrie spatiale 2 -Hétérogénéité spatiale," LATEC - Document de travail - Economie (1991-2003) 2001-01, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    12. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.
    13. Figueiredo, Adriano Marcos Rodrigues & Bonjour, Sandra Cristina de Moura & Teixeira, Erly Cardoso & Helfand, Steven M., 2011. "Spatial analysis of agricultural supply response in the Brazilian Center-West," Economi­a Agraria (Revista Economia Agraria), Agrarian Economist Association (AEA), Chile, vol. 15, pages 1-13.
    14. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    15. Yanchuan Mou & Qingsong He & Bo Zhou, 2017. "Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    16. Miryam S. Merk & Philipp Otto, 2022. "Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    17. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    18. Xianyu Yu & Yi Wang & Ruiqing Niu & Youjian Hu, 2016. "A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, Chin," IJERPH, MDPI, vol. 13(5), pages 1-35, May.
    19. Kyung Hee Lee, 2020. "Mental Health and Recreation Opportunities," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    20. Arturo Bujanda & Thomas M. Fullerton, 2017. "Impacts of transportation infrastructure on single-family property values," Applied Economics, Taylor & Francis Journals, vol. 49(51), pages 5183-5199, November.
    21. Punzo, Gennaro & Castellano, Rosalia & Bruno, Emma, 2022. "Using geographically weighted regressions to explore spatial heterogeneity of land use influencing factors in Campania (Southern Italy)," Land Use Policy, Elsevier, vol. 112(C).
    22. Julie Le Gallo, 2000. "Spatial econometrics (2, Spatial heterogeneity) [Econométrie spatiale (2, Hétérogénéité spatiale)]," Working Papers hal-01526969, HAL.

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