Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA
We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response varia...
Gespeichert in:
| Veröffentlicht in: | Environmetrics (London, Ont.) Ont.), 2013-03, Vol.24 (2), p.98-108 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 108 |
|---|---|
| container_issue | 2 |
| container_start_page | 98 |
| container_title | Environmetrics (London, Ont.) |
| container_volume | 24 |
| creator | Jin, Chongyang Zhu, Jun Steen-Adams, Michelle M. Sain, Stephan R. Gangnon, Ronald E. |
| description | We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/env.2189 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671505035</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1671505035</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3699-1f21161d5cfa0818ca190a8595c55efe7fb8872150e54c9acc48707000b6d9d93</originalsourceid><addsrcrecordid>eNqFkU9LAzEQxRdRUKvgR8jRg1uTbrObeBOtVSlVbP1zCzE7q9HdpCZptd_eLBXFg3iaB_Ob4c28JNkjuEsw7h2CWXR7hPG1ZItgzlPM6cN61IThtI8x30y2vX_BUeW02ErCZCaDljVq5nXQxjatdvDkwHttDWpsCbVHlXWobZrYVTLAk3VaRV3KII-QRD7MyyWyFaqlKZGyC3BIGzS2LjyDM-hee2WN1-YA3U6Od5KNStYedr9qJ7k9G0xPztPR1fDi5HiUqiyP1knVIyQnJVWVxIwwJQnHklFOFaVQQVE9Mlb0CMVA-4pLpfqswEW87TEvecmzTrK_2jtz9m0OPogm-oA6mgQ794LkRRymOKP_ozTH8Z-Ekx9UOeu9g0rMnG6kWwqCRZuBiBmINoOIpiv0Xdew_JMTg_Hdb177AB_fvHSvIi-ygor78VBM2enNJb2eiLvsEzGYl5E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1560109191</pqid></control><display><type>article</type><title>Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Jin, Chongyang ; Zhu, Jun ; Steen-Adams, Michelle M. ; Sain, Stephan R. ; Gangnon, Ronald E.</creator><creatorcontrib>Jin, Chongyang ; Zhu, Jun ; Steen-Adams, Michelle M. ; Sain, Stephan R. ; Gangnon, Ronald E.</creatorcontrib><description>We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd.</description><identifier>ISSN: 1180-4009</identifier><identifier>EISSN: 1099-095X</identifier><identifier>DOI: 10.1002/env.2189</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Algorithms ; Bayesian hierarchical model ; environmental history ; Geographic information systems ; Inference ; Land cover ; landscape ecology ; Landscapes ; Mathematical models ; Multivariate CAR model ; Regression ; spatial statistics ; Statistical methods</subject><ispartof>Environmetrics (London, Ont.), 2013-03, Vol.24 (2), p.98-108</ispartof><rights>Copyright © 2013 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3699-1f21161d5cfa0818ca190a8595c55efe7fb8872150e54c9acc48707000b6d9d93</citedby><cites>FETCH-LOGICAL-c3699-1f21161d5cfa0818ca190a8595c55efe7fb8872150e54c9acc48707000b6d9d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fenv.2189$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fenv.2189$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,777,781,1412,27905,27906,45555,45556</link.rule.ids></links><search><creatorcontrib>Jin, Chongyang</creatorcontrib><creatorcontrib>Zhu, Jun</creatorcontrib><creatorcontrib>Steen-Adams, Michelle M.</creatorcontrib><creatorcontrib>Sain, Stephan R.</creatorcontrib><creatorcontrib>Gangnon, Ronald E.</creatorcontrib><title>Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA</title><title>Environmetrics (London, Ont.)</title><addtitle>Environmetrics</addtitle><description>We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd.</description><subject>Algorithms</subject><subject>Bayesian hierarchical model</subject><subject>environmental history</subject><subject>Geographic information systems</subject><subject>Inference</subject><subject>Land cover</subject><subject>landscape ecology</subject><subject>Landscapes</subject><subject>Mathematical models</subject><subject>Multivariate CAR model</subject><subject>Regression</subject><subject>spatial statistics</subject><subject>Statistical methods</subject><issn>1180-4009</issn><issn>1099-095X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkU9LAzEQxRdRUKvgR8jRg1uTbrObeBOtVSlVbP1zCzE7q9HdpCZptd_eLBXFg3iaB_Ob4c28JNkjuEsw7h2CWXR7hPG1ZItgzlPM6cN61IThtI8x30y2vX_BUeW02ErCZCaDljVq5nXQxjatdvDkwHttDWpsCbVHlXWobZrYVTLAk3VaRV3KII-QRD7MyyWyFaqlKZGyC3BIGzS2LjyDM-hee2WN1-YA3U6Od5KNStYedr9qJ7k9G0xPztPR1fDi5HiUqiyP1knVIyQnJVWVxIwwJQnHklFOFaVQQVE9Mlb0CMVA-4pLpfqswEW87TEvecmzTrK_2jtz9m0OPogm-oA6mgQ794LkRRymOKP_ozTH8Z-Ekx9UOeu9g0rMnG6kWwqCRZuBiBmINoOIpiv0Xdew_JMTg_Hdb177AB_fvHSvIi-ygor78VBM2enNJb2eiLvsEzGYl5E</recordid><startdate>201303</startdate><enddate>201303</enddate><creator>Jin, Chongyang</creator><creator>Zhu, Jun</creator><creator>Steen-Adams, Michelle M.</creator><creator>Sain, Stephan R.</creator><creator>Gangnon, Ronald E.</creator><general>John Wiley & Sons, Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7SU</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>201303</creationdate><title>Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA</title><author>Jin, Chongyang ; Zhu, Jun ; Steen-Adams, Michelle M. ; Sain, Stephan R. ; Gangnon, Ronald E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3699-1f21161d5cfa0818ca190a8595c55efe7fb8872150e54c9acc48707000b6d9d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Bayesian hierarchical model</topic><topic>environmental history</topic><topic>Geographic information systems</topic><topic>Inference</topic><topic>Land cover</topic><topic>landscape ecology</topic><topic>Landscapes</topic><topic>Mathematical models</topic><topic>Multivariate CAR model</topic><topic>Regression</topic><topic>spatial statistics</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Chongyang</creatorcontrib><creatorcontrib>Zhu, Jun</creatorcontrib><creatorcontrib>Steen-Adams, Michelle M.</creatorcontrib><creatorcontrib>Sain, Stephan R.</creatorcontrib><creatorcontrib>Gangnon, Ronald E.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Environmetrics (London, Ont.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Chongyang</au><au>Zhu, Jun</au><au>Steen-Adams, Michelle M.</au><au>Sain, Stephan R.</au><au>Gangnon, Ronald E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA</atitle><jtitle>Environmetrics (London, Ont.)</jtitle><addtitle>Environmetrics</addtitle><date>2013-03</date><risdate>2013</risdate><volume>24</volume><issue>2</issue><spage>98</spage><epage>108</epage><pages>98-108</pages><issn>1180-4009</issn><eissn>1099-095X</eissn><abstract>We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/env.2189</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1180-4009 |
| ispartof | Environmetrics (London, Ont.), 2013-03, Vol.24 (2), p.98-108 |
| issn | 1180-4009 1099-095X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1671505035 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Bayesian hierarchical model environmental history Geographic information systems Inference Land cover landscape ecology Landscapes Mathematical models Multivariate CAR model Regression spatial statistics Statistical methods |
| title | Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T10%3A47%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spatial%20multinomial%20regression%20models%20for%20nominal%20categorical%20data:%20a%20study%20of%20land%20cover%20in%20Northern%20Wisconsin,%20USA&rft.jtitle=Environmetrics%20(London,%20Ont.)&rft.au=Jin,%20Chongyang&rft.date=2013-03&rft.volume=24&rft.issue=2&rft.spage=98&rft.epage=108&rft.pages=98-108&rft.issn=1180-4009&rft.eissn=1099-095X&rft_id=info:doi/10.1002/env.2189&rft_dat=%3Cproquest_cross%3E1671505035%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1560109191&rft_id=info:pmid/&rfr_iscdi=true |