https://doi.org/10.4081/gh.2024.1271
Mastering geographically weighted regression: key considerations for building a robust model
Published: 29 February 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]
Anselin L, Li X, Koschinsky J, 2022. GeoDa, from the desktop to an ecosystem for exploring spatial data. Geogr Anal 54:439-466.
DOI: https://doi.org/10.1111/gean.12311
Bivand R, Yu D, Nakaya T, Garcia-Lopez M.-A, Bivand M.R, 2017. Package ‘spgwr’. R software package.
Brunsdon C, Fotheringham S, Charlton M, 1998. Geographically Weighted Regression. J R Stat Soc: Series D (The Statistician) 47:431-43.
DOI: https://doi.org/10.1111/1467-9884.00145
Firouraghi N, Bergquist R, Fatima M, Mohammadi A, Hamer DH, Shirzadi MN, Kiani B, 2023. High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020. Infect Dis Poverty 12:49.
DOI: https://doi.org/10.1186/s40249-023-01103-1
Guo L, Ma Z, Zhang L, 2008. Comparison of bandwidth selection in application of geographically weighted regression: a case study. Can J For Res 38:2526-34.
DOI: https://doi.org/10.1139/X08-091
Kiani B, Fatima M, Amin N.H, Hesami A, 2023. Comparing geospatial clustering methods to study spatial patterns of lung cancer rates in urban areas: A case study in Mashhad, Iran. GeoJournal 88:1659-69.
DOI: https://doi.org/10.1007/s10708-022-10707-3
Kiani B, Mamiya H, Thierry B, Firth C, Fuller D, Winters M, Kestens Y. 2023. The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada. Habitat Int 139:102899.
DOI: https://doi.org/10.1016/j.habitatint.2023.102899
Kiani B, Thierry B, Fuller D, Firth C, Winters M, Kestens Y. 2023. Gentrification, neighborhood socioeconomic factors and urban vegetation inequities: A study of greenspace and tree canopy increases in Montreal, Canada. Landsc. Urban Plan. 240:104871.
DOI: https://doi.org/10.1016/j.landurbplan.2023.104871
Mohammadi A, Bergquist R, Fathi G, Pishgar E, de Melo SN, Sharifi A, Kiani B, 2022. Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada. BMC Public Health 22:1482.
DOI: https://doi.org/10.1186/s12889-022-13807-4
Mohammadi A, Pishgar E, Fatima M, Lotfata A, Fanni Z, Bergquist R, Kiani B, 2023. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Trop Med Infect 8:85.
DOI: https://doi.org/10.3390/tropicalmed8020085
Rey SJ, Anselin L, Amaral P, Arribas‐Bel D, Cortes R.X, Gaboardi J.D, Kang W, Knaap E, Li Z, Lumnitz S, Oshan T, Shao H, Wolf L.J. 2022. The PySAL ecosystem: Philosophy and implementation. Geogr Anal 54:467-87.
DOI: https://doi.org/10.1111/gean.12276
Scott LM, Janikas MV. 2009. Spatial statistics in ArcGIS. Handbook of applied spatial analysis: Software tools, methods and applications, Springer, pp. 27-41.
DOI: https://doi.org/10.1007/978-3-642-03647-7_2
Soroori E, Kiani B, Ghasemi S, Mohammadi A, Shabanikiya H, Bergquist R, Kiani F, Tabatabaei-Jafari H. 2023. Spatial Association between urban neighbourhood characteristics and child pedestrian–motor vehicle collisions. Appl Spat Anal Policy 16:1443-62.
DOI: https://doi.org/10.1007/s12061-023-09519-w
Wheeler D.C. 2021. Geographically weighted regression. Handbook of regional science, Springer: 1895-921.
DOI: https://doi.org/10.1007/978-3-662-60723-7_77
How to Cite
Kiani, B., Sartorius, B., Lau, C. L., & Bergquist, R. (2024). Mastering geographically weighted regression: key considerations for building a robust model. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1271
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