Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting...

Full description

Saved in:
Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2012-06, Vol.26 (6), p.963-982
Main Authors: Hagenauer, Julian, Helbich, Marco
Format: Article
Language:English
Subjects:
Genetic algorithms
Geographic information science
Land use
machine learning
Mapping
Neural networks
OpenStreetMap UK
spatial data quality
Urban areas
volunteered geographic information
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3
cites cdi_FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3
container_end_page 982
container_issue 6
container_start_page 963
container_title International journal of geographical information science : IJGIS
container_volume 26
creator Hagenauer, Julian
Helbich, Marco
description In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2 ) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.
doi_str_mv 10.1080/13658816.2011.619501
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1268655465</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2678309991</sourcerecordid><originalsourceid>FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3</originalsourceid><addsrcrecordid>eNp9kU9v1DAQxSMEElXpN-BgqRcuWWwndpwTQhW0SFtx6d2a-M_WNLGXsUO1dz443m659NDTjMa_9-SZ1zQfGd0wquhn1kmhFJMbThnbSDYKyt40Z3XM246q4e1TL9oj8765yDlMlHdqVGoQZ83f2xBD3JEVJ4hkhmjbNTuyh1Icxkw8poX8SfMai3PoLNm5tEPY3wdDQvQJFyghRTIdyOKgCpKvSHSlvsO8SxjK_ZJJ9SWAJfhgAswkuhWfSnlM-JA_NO88zNldPNfz5u77t7urm3b78_rH1ddtazqpSsuYnDio0Y-97SlYq0AY3klJ-dhPvWfSem4m44eeO1t3tsZ5BXYSqh-U7c6bTyfbPabfq8tFLyEbN9etXVqzZlwqKUQvRUUvX6C_0oqxfk4zypTo5DCMlepPlMGUMzqv9xgWwEOF9DEc_T8cfQxHn8Kpsi8n2fMB6w1mqwsc5oQeIZqQdfeqwz9mo5jD</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1018536779</pqid></control><display><type>article</type><title>Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks</title><source>Taylor and Francis Science and Technology Collection</source><creator>Hagenauer, Julian ; Helbich, Marco</creator><creatorcontrib>Hagenauer, Julian ; Helbich, Marco</creatorcontrib><description>In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2 ) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.</description><identifier>ISSN: 1365-8816</identifier><identifier>EISSN: 1362-3087</identifier><identifier>EISSN: 1365-8824</identifier><identifier>DOI: 10.1080/13658816.2011.619501</identifier><language>eng</language><publisher>Abingdon: Taylor &amp; Francis</publisher><subject>Genetic algorithms ; Geographic information science ; Land use ; machine learning ; Mapping ; Neural networks ; OpenStreetMap UK ; spatial data quality ; Urban areas ; volunteered geographic information</subject><ispartof>International journal of geographical information science : IJGIS, 2012-06, Vol.26 (6), p.963-982</ispartof><rights>Copyright Taylor &amp; Francis Group, LLC 2012</rights><rights>Copyright Taylor &amp; Francis Group 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3</citedby><cites>FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Hagenauer, Julian</creatorcontrib><creatorcontrib>Helbich, Marco</creatorcontrib><title>Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks</title><title>International journal of geographical information science : IJGIS</title><description>In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2 ) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.</description><subject>Genetic algorithms</subject><subject>Geographic information science</subject><subject>Land use</subject><subject>machine learning</subject><subject>Mapping</subject><subject>Neural networks</subject><subject>OpenStreetMap UK</subject><subject>spatial data quality</subject><subject>Urban areas</subject><subject>volunteered geographic information</subject><issn>1365-8816</issn><issn>1362-3087</issn><issn>1365-8824</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNp9kU9v1DAQxSMEElXpN-BgqRcuWWwndpwTQhW0SFtx6d2a-M_WNLGXsUO1dz443m659NDTjMa_9-SZ1zQfGd0wquhn1kmhFJMbThnbSDYKyt40Z3XM246q4e1TL9oj8765yDlMlHdqVGoQZ83f2xBD3JEVJ4hkhmjbNTuyh1Icxkw8poX8SfMai3PoLNm5tEPY3wdDQvQJFyghRTIdyOKgCpKvSHSlvsO8SxjK_ZJJ9SWAJfhgAswkuhWfSnlM-JA_NO88zNldPNfz5u77t7urm3b78_rH1ddtazqpSsuYnDio0Y-97SlYq0AY3klJ-dhPvWfSem4m44eeO1t3tsZ5BXYSqh-U7c6bTyfbPabfq8tFLyEbN9etXVqzZlwqKUQvRUUvX6C_0oqxfk4zypTo5DCMlepPlMGUMzqv9xgWwEOF9DEc_T8cfQxHn8Kpsi8n2fMB6w1mqwsc5oQeIZqQdfeqwz9mo5jD</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Hagenauer, Julian</creator><creator>Helbich, Marco</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis LLC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope></search><sort><creationdate>20120601</creationdate><title>Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks</title><author>Hagenauer, Julian ; Helbich, Marco</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Genetic algorithms</topic><topic>Geographic information science</topic><topic>Land use</topic><topic>machine learning</topic><topic>Mapping</topic><topic>Neural networks</topic><topic>OpenStreetMap UK</topic><topic>spatial data quality</topic><topic>Urban areas</topic><topic>volunteered geographic information</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hagenauer, Julian</creatorcontrib><creatorcontrib>Helbich, Marco</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>International journal of geographical information science : IJGIS</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hagenauer, Julian</au><au>Helbich, Marco</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks</atitle><jtitle>International journal of geographical information science : IJGIS</jtitle><date>2012-06-01</date><risdate>2012</risdate><volume>26</volume><issue>6</issue><spage>963</spage><epage>982</epage><pages>963-982</pages><issn>1365-8816</issn><eissn>1362-3087</eissn><eissn>1365-8824</eissn><abstract>In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2 ) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.</abstract><cop>Abingdon</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/13658816.2011.619501</doi><tpages>20</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1365-8816
ispartof International journal of geographical information science : IJGIS, 2012-06, Vol.26 (6), p.963-982
issn 1365-8816
1362-3087
1365-8824
language eng
recordid cdi_proquest_miscellaneous_1268655465
source Taylor and Francis Science and Technology Collection
subjects Genetic algorithms
Geographic information science
Land use
machine learning
Mapping
Neural networks
OpenStreetMap UK
spatial data quality
Urban areas
volunteered geographic information
title Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T03%3A21%3A27IST&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=Mining%20urban%20land-use%20patterns%20from%20volunteered%20geographic%20information%20by%20means%20of%20genetic%20algorithms%20and%20artificial%20neural%20networks&rft.jtitle=International%20journal%20of%20geographical%20information%20science%20:%20IJGIS&rft.au=Hagenauer,%20Julian&rft.date=2012-06-01&rft.volume=26&rft.issue=6&rft.spage=963&rft.epage=982&rft.pages=963-982&rft.issn=1365-8816&rft.eissn=1362-3087&rft_id=info:doi/10.1080/13658816.2011.619501&rft_dat=%3Cproquest_cross%3E2678309991%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c368t-116b2a89f94d40add8a5c23660294b4f16df2cbcf742ed365dcef8adb58478d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1018536779&rft_id=info:pmid/&rfr_iscdi=true