The application of principal component cluster analysis in environment classification for Chinese cities
In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores,...
Gespeichert in:
| Veröffentlicht in: | IOP conference series. Earth and environmental science 2020-09, Vol.569 (1), p.12040 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 1 |
| container_start_page | 12040 |
| container_title | IOP conference series. Earth and environmental science |
| container_volume | 569 |
| creator | Wang, Jingcheng Zhang, Lunwu Zhang, Dingfei Zhao, Fangchao Yang, Xiaokui |
| description | In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores, dissimilarity between each city is measured during clustering. These cities are classified into seven types, and they are marked on the map of China. The result of this classification is consistent to our traditional cognition. Therefore, the principal component cluster analysis is suitable for analyzing numerous observations with variables on a large scale. This approach helps to enhance the environmental adaptability of equipments by recognizing the environment type of each city. |
| doi_str_mv | 10.1088/1755-1315/569/1/012040 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2556369418</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2556369418</sourcerecordid><originalsourceid>FETCH-LOGICAL-c455t-f76badcc587341c95f1ab9d45d144c85f73e721f0eec9f2b93a9fa2d64f478543</originalsourceid><addsrcrecordid>eNqFkMtKAzEUhoMoWKuvIAE3bsZJJslkspRSL1BwYV2HNJPQlGkSk6nQt3fKaEUQXJ0D_4VzPgCuMbrDqGlKzBkrMMGsZLUocYlwhSg6AZOjcHrcET8HFzlvEKo5JWIC1su1gSrGzmnVu-BhsDAm57WLqoM6bGPwxvdQd7vcmwSVV90-uwydh8Z_uBT8dtRVzs5-t9iQ4GztvMkGatc7ky_BmVVdNldfcwreHubL2VOxeHl8nt0vCk0Z6wvL65VqtWYNJxRrwSxWK9FS1mJKdcMsJ4ZX2CJjtLDVShAlrKramlrKG0bJFNyMvTGF953JvdyEXRquzrJirCa1oLgZXPXo0inknIyVw9NblfYSI3mgKg_A5AGeHKhKLEeqQ7Aagy7En-Z_Q7d_hObz1182GVtLPgHgwYhp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2556369418</pqid></control><display><type>article</type><title>The application of principal component cluster analysis in environment classification for Chinese cities</title><source>Institute of Physics</source><source>Institute of Physics Open Access Journal Titles</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wang, Jingcheng ; Zhang, Lunwu ; Zhang, Dingfei ; Zhao, Fangchao ; Yang, Xiaokui</creator><creatorcontrib>Wang, Jingcheng ; Zhang, Lunwu ; Zhang, Dingfei ; Zhao, Fangchao ; Yang, Xiaokui</creatorcontrib><description>In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores, dissimilarity between each city is measured during clustering. These cities are classified into seven types, and they are marked on the map of China. The result of this classification is consistent to our traditional cognition. Therefore, the principal component cluster analysis is suitable for analyzing numerous observations with variables on a large scale. This approach helps to enhance the environmental adaptability of equipments by recognizing the environment type of each city.</description><identifier>ISSN: 1755-1307</identifier><identifier>EISSN: 1755-1315</identifier><identifier>DOI: 10.1088/1755-1315/569/1/012040</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Adaptability ; Cities ; Classification ; Cluster analysis ; Clustering ; Cognition ; Principal components analysis</subject><ispartof>IOP conference series. Earth and environmental science, 2020-09, Vol.569 (1), p.12040</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c455t-f76badcc587341c95f1ab9d45d144c85f73e721f0eec9f2b93a9fa2d64f478543</citedby><cites>FETCH-LOGICAL-c455t-f76badcc587341c95f1ab9d45d144c85f73e721f0eec9f2b93a9fa2d64f478543</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1755-1315/569/1/012040/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,780,784,27924,27925,38868,38890,53840,53867</link.rule.ids></links><search><creatorcontrib>Wang, Jingcheng</creatorcontrib><creatorcontrib>Zhang, Lunwu</creatorcontrib><creatorcontrib>Zhang, Dingfei</creatorcontrib><creatorcontrib>Zhao, Fangchao</creatorcontrib><creatorcontrib>Yang, Xiaokui</creatorcontrib><title>The application of principal component cluster analysis in environment classification for Chinese cities</title><title>IOP conference series. Earth and environmental science</title><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><description>In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores, dissimilarity between each city is measured during clustering. These cities are classified into seven types, and they are marked on the map of China. The result of this classification is consistent to our traditional cognition. Therefore, the principal component cluster analysis is suitable for analyzing numerous observations with variables on a large scale. This approach helps to enhance the environmental adaptability of equipments by recognizing the environment type of each city.</description><subject>Adaptability</subject><subject>Cities</subject><subject>Classification</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Cognition</subject><subject>Principal components analysis</subject><issn>1755-1307</issn><issn>1755-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkMtKAzEUhoMoWKuvIAE3bsZJJslkspRSL1BwYV2HNJPQlGkSk6nQt3fKaEUQXJ0D_4VzPgCuMbrDqGlKzBkrMMGsZLUocYlwhSg6AZOjcHrcET8HFzlvEKo5JWIC1su1gSrGzmnVu-BhsDAm57WLqoM6bGPwxvdQd7vcmwSVV90-uwydh8Z_uBT8dtRVzs5-t9iQ4GztvMkGatc7ky_BmVVdNldfcwreHubL2VOxeHl8nt0vCk0Z6wvL65VqtWYNJxRrwSxWK9FS1mJKdcMsJ4ZX2CJjtLDVShAlrKramlrKG0bJFNyMvTGF953JvdyEXRquzrJirCa1oLgZXPXo0inknIyVw9NblfYSI3mgKg_A5AGeHKhKLEeqQ7Aagy7En-Z_Q7d_hObz1182GVtLPgHgwYhp</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Wang, Jingcheng</creator><creator>Zhang, Lunwu</creator><creator>Zhang, Dingfei</creator><creator>Zhao, Fangchao</creator><creator>Yang, Xiaokui</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope></search><sort><creationdate>20200901</creationdate><title>The application of principal component cluster analysis in environment classification for Chinese cities</title><author>Wang, Jingcheng ; Zhang, Lunwu ; Zhang, Dingfei ; Zhao, Fangchao ; Yang, Xiaokui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c455t-f76badcc587341c95f1ab9d45d144c85f73e721f0eec9f2b93a9fa2d64f478543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptability</topic><topic>Cities</topic><topic>Classification</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Cognition</topic><topic>Principal components analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jingcheng</creatorcontrib><creatorcontrib>Zhang, Lunwu</creatorcontrib><creatorcontrib>Zhang, Dingfei</creatorcontrib><creatorcontrib>Zhao, Fangchao</creatorcontrib><creatorcontrib>Yang, Xiaokui</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><jtitle>IOP conference series. Earth and environmental science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Jingcheng</au><au>Zhang, Lunwu</au><au>Zhang, Dingfei</au><au>Zhao, Fangchao</au><au>Yang, Xiaokui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The application of principal component cluster analysis in environment classification for Chinese cities</atitle><jtitle>IOP conference series. Earth and environmental science</jtitle><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>569</volume><issue>1</issue><spage>12040</spage><pages>12040-</pages><issn>1755-1307</issn><eissn>1755-1315</eissn><abstract>In order to investigate the dissimilarities of different cities in China, an approach combining principal component analysis and hierarchical clustering is proposed. Three rather than two principal components are reserved to conduct a more elaborate analysis. Based on corresponding component scores, dissimilarity between each city is measured during clustering. These cities are classified into seven types, and they are marked on the map of China. The result of this classification is consistent to our traditional cognition. Therefore, the principal component cluster analysis is suitable for analyzing numerous observations with variables on a large scale. This approach helps to enhance the environmental adaptability of equipments by recognizing the environment type of each city.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1755-1315/569/1/012040</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1755-1307 |
| ispartof | IOP conference series. Earth and environmental science, 2020-09, Vol.569 (1), p.12040 |
| issn | 1755-1307 1755-1315 |
| language | eng |
| recordid | cdi_proquest_journals_2556369418 |
| source | Institute of Physics; Institute of Physics Open Access Journal Titles; EZB-FREE-00999 freely available EZB journals |
| subjects | Adaptability Cities Classification Cluster analysis Clustering Cognition Principal components analysis |
| title | The application of principal component cluster analysis in environment classification for Chinese cities |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T23%3A28%3A32IST&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=The%20application%20of%20principal%20component%20cluster%20analysis%20in%20environment%20classification%20for%20Chinese%20cities&rft.jtitle=IOP%20conference%20series.%20Earth%20and%20environmental%20science&rft.au=Wang,%20Jingcheng&rft.date=2020-09-01&rft.volume=569&rft.issue=1&rft.spage=12040&rft.pages=12040-&rft.issn=1755-1307&rft.eissn=1755-1315&rft_id=info:doi/10.1088/1755-1315/569/1/012040&rft_dat=%3Cproquest_cross%3E2556369418%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=2556369418&rft_id=info:pmid/&rfr_iscdi=true |