Geographically weighted elastic net logistic regression

A - Papers appearing in refereed journals

Comber, A. and Harris, P. 2018. Geographically weighted elastic net logistic regression. Journal of Geographical Systems. 20 (4), pp. 317-341.

AuthorsComber, A. and Harris, P.
Abstract

This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters.

KeywordsGWR; GW-ELNR; Elastic nets; Collinearity; Model selection
Year of Publication2018
JournalJournal of Geographical Systems
Journal citation20 (4), pp. 317-341
Digital Object Identifier (DOI)doi:10.1007/s10109-018-0280-7
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
The North Wyke Farm Platform- National Capability [2017-22]
Publisher's version
Accepted author manuscript
Output statusPublished
Publication dates
Online26 Sep 2018
Publication process dates
Accepted12 Sep 2018
PublisherSpringer Nature
Copyright licenseCC BY
ISSN1435-5930

Permalink - https://repository.rothamsted.ac.uk/item/8489y/geographically-weighted-elastic-net-logistic-regression

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