Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Harris, Paul, Brunsdon, Chris (2010) Exploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics. Computers & Geosciences, 36 (1) 54-70 doi:10.1016/j.cageo.2009.04.012

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleExploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics
JournalComputers & Geosciences
AuthorsHarris, PaulAuthor
Brunsdon, ChrisAuthor
Year2010 (January)Volume36
Issue1
PublisherElsevier BV
DOIdoi:10.1016/j.cageo.2009.04.012Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID451618Long-form Identifiermindat:1:5:451618:1
GUID0
Full ReferenceHarris, Paul, Brunsdon, Chris (2010) Exploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics. Computers & Geosciences, 36 (1) 54-70 doi:10.1016/j.cageo.2009.04.012
Plain TextHarris, Paul, Brunsdon, Chris (2010) Exploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics. Computers & Geosciences, 36 (1) 54-70 doi:10.1016/j.cageo.2009.04.012
In(2010, January) Computers & Geosciences Vol. 36 (1) Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Lu, Binbin, Harris, Paul, Charlton, Martin, Brunsdon, Chris (2014) The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science, 17. 85-101 doi:10.1080/10095020.2014.917453
Harris, Paul, Brunsdon, Chris, Charlton, Martin, Juggins, Steve, Clarke, Annemarie (2014) Multivariate Spatial Outlier Detection Using Robust Geographically Weighted Methods. Mathematical Geosciences, 46 (1). 1-31 doi:10.1007/s11004-013-9491-0
Harris, Paul, Juggins, Steve (2011) Estimating Freshwater Acidification Critical Load Exceedance Data for Great Britain Using Space-Varying Relationship Models. Mathematical Geosciences, 43 (3). 265-292 doi:10.1007/s11004-011-9331-z
Harris, Paul, Fotheringham, A. Stewart, Juggins, Steve (2010) Robust Geographically Weighted Regression: A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes. Annals of the Association of American Geographers, 100 (2). 286-306 doi:10.1080/00045600903550378
ErdoÄŸan, Saffet (2010) Modelling the spatial distribution of DEM error with geographically weighted regression: An experimental study. Computers & Geosciences, 36 (1) 34-43 doi:10.1016/j.cageo.2009.06.005
Brunsdon, Chris, Fotheringham, A. Stewart, Charlton, Martin E. (2010) Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28 (4). 281-298 doi:10.1111/j.1538-4632.1996.tb00936.x
Lu, Binbin, Hu, Yigong, Murakami, Daisuke, Brunsdon, Chris, Comber, Alexis, Charlton, Martin, Harris, Paul (2022) High-performance solutions of geographically weighted regression in R. Geo-spatial Information Science, 25 (4) 536-549 doi:10.1080/10095020.2022.2064244
Brunsdon, C., McClatchey, J., Unwin, D.J. (2001) Spatial variations in the average rainfall-altitude relationship in Great Britain: an approach using geographically weighted regression. International Journal of Climatology, 21 (4). 455-466 doi:10.1002/joc.614
Lu, Binbin; Hu, Yigong; Yang, Dongyang; Liu, Yong; Ou, Guangyu; Harris, Paul; Brunsdon, Chris; Comber, Alexis; Dong, Guanpeng (2025) GWmodelS: a standalone software to train geographically weighted models. Geo-spatial Information Science, 28 (2). doi:10.1080/10095020.2024.2343011
Harris, Paul, Brunsdon, Chris, Fotheringham, A. Stewart (2011) Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment, 25 (2). 123-138 doi:10.1007/s00477-010-0444-6
Harris, Paul, Clarke, Annemarie, Juggins, Steve, Brunsdon, Chris, Charlton, Martin (2015) Enhancements to a Geographically Weighted Principal Component Analysis in the Context of an Application to an Environmental Data Set. Geographical Analysis, 47 (2). 146-172 doi:10.1111/gean.12048
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 31, 2025 02:46:21
Go to top of page