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

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datacite.relatedItem.firstPage269
datacite.relatedItem.issue6
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2220-9964
datacite.relatedItem.relationTypeIsPublishedIn
datacite.relatedItem.titleIJGI
datacite.relatedItem.volume8
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.date.issued2019
dc.identifier.doihttps://doi.org/10.3390/ijgi8060269
dc.identifier.otherjz000156-0267
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28356
dc.publisherMDPI AG
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.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28357
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