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

Simple item page
dc.contributor.authorTaylor Oshan
dc.contributor.authorZiqi Li
dc.contributor.authorWei Kang
dc.contributor.authorLevi Wolf
dc.contributor.authorA. Fotheringham
dc.date.accessioned2025-05-14T15:20:11Z
dc.date.available2025-05-14T15:20:11Z
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/28358
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.typesupplierxml
dspace.entity.typeDistribution
relation.isDatasetOfDistribution06da02a2-6810-49ce-ad14-d2463342f125
relation.isDatasetOfDistribution.latestForDiscovery06da02a2-6810-49ce-ad14-d2463342f125

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ijgi-08-06-00269.xml
Size:
170.99 KB
Format:
Extensible Markup Language
Download

Collections

2019