mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale

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dc.contributor.authorTaylor Oshan
dc.contributor.authorZiqi Li
dc.contributor.authorWei Kang
dc.contributor.authorLevi Wolf
dc.contributor.authorA. Fotheringham
dc.date.accessioned2025-05-14T15:20:10Z
dc.date.available2025-05-14T15:20:10Z
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28357
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titlemgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale
dc.typepdf
dspace.entity.typeDistribution
relation.isDatasetOfDistribution06da02a2-6810-49ce-ad14-d2463342f125
relation.isDatasetOfDistribution.latestForDiscovery06da02a2-6810-49ce-ad14-d2463342f125

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