POPMUSIC for the point feature label placement problem
Point feature label placement is the problem of placing text labels adjacent to point features on a map so as to maximize legibility. The goal is to choose positions for the labels that do not give rise to label overlaps and that minimize obscuration of features. A practical goal is to minimize the...
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| Veröffentlicht in: | European journal of operational research 2009-01, Vol.192 (2), p.396-413 |
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| container_title | European journal of operational research |
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| creator | Alvim, Adriana C.F. Taillard, Éric D. |
| description | Point feature label placement is the problem of placing text labels adjacent to point features on a map so as to maximize legibility. The goal is to choose positions for the labels that do not give rise to label overlaps and that minimize obscuration of features. A practical goal is to minimize the number of overlaps while considering cartographic preferences. This article proposes a new heuristic for solving the point feature label placement problem based on the application of the
POPMUSIC frame. Computational experiments show that the proposed heuristic outperformed other recent metaheuristics approaches in the literature. Experiments with problem instances involving up to 10 million points show that the computational time of the proposed heuristic increases almost linearly with the problem size. New problem instances based on real data with more than 13,000 labels are proposed. |
| doi_str_mv | 10.1016/j.ejor.2007.10.002 |
| format | Article |
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Computational geometry</subject><subject>POPMUSIC</subject><subject>Popular music</subject><subject>Preferences</subject><subject>Studies</subject><subject>Tabu search</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UE1LxDAQDaLguvoHPBXBY9fJR5sWvMjiF64oqOeQphNs6W5r0hX23ztlxaOBlwnDe28mj7FzDgsOPL9qF9j2YSEANDUWAOKAzXihRZoXORyyGUitUyG4PmYnMbYAwDOezVj--vL6_PH2uEx8H5LxE5OhbzZj4tGO24BJZyvskqGzDtdI_SH0VYfrU3bkbRfx7LfO2cfd7fvyIV293D8ub1apy1Q2prUoqgJ0XbmSS-HJxJUVFDlXoIFLWdUWVW21kqosa-c95iXXtpBZlWVaejlnF3tfmvu1xTiatt-GDY00AhRXhdBAJLEnudDHGNCbITRrG3aGg5niMa2Z4jFTPFOP4iHR014UcED3p0A6RMVovo20vBR07wgkLak005MwEGSZG8Wl-RzX5Hb5u6eNznY-2I1r4p-roO8qyBTxrvc8pNC-GwwmugY3DusmoBtN3Tf_Lf0D42SSjA</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Alvim, Adriana C.F.</creator><creator>Taillard, Éric D.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>200901</creationdate><title>POPMUSIC for the point feature label placement problem</title><author>Alvim, Adriana C.F. ; Taillard, Éric D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c545t-d28b807dbc9132facec9b08614070133bdae4da743499dcffe6917a835b5573f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Cartography</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Heuristic</topic><topic>Labeling</topic><topic>Large-scale optimization</topic><topic>Map labeling</topic><topic>Metaheuristics</topic><topic>Metaheuristics POPMUSIC Map labeling Large-scale optimization Tabu search</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>POPMUSIC</topic><topic>Popular music</topic><topic>Preferences</topic><topic>Studies</topic><topic>Tabu search</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alvim, Adriana C.F.</creatorcontrib><creatorcontrib>Taillard, Éric D.</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alvim, Adriana C.F.</au><au>Taillard, Éric D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>POPMUSIC for the point feature label placement problem</atitle><jtitle>European journal of operational research</jtitle><date>2009-01</date><risdate>2009</risdate><volume>192</volume><issue>2</issue><spage>396</spage><epage>413</epage><pages>396-413</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>Point feature label placement is the problem of placing text labels adjacent to point features on a map so as to maximize legibility. The goal is to choose positions for the labels that do not give rise to label overlaps and that minimize obscuration of features. A practical goal is to minimize the number of overlaps while considering cartographic preferences. This article proposes a new heuristic for solving the point feature label placement problem based on the application of the
POPMUSIC frame. Computational experiments show that the proposed heuristic outperformed other recent metaheuristics approaches in the literature. Experiments with problem instances involving up to 10 million points show that the computational time of the proposed heuristic increases almost linearly with the problem size. New problem instances based on real data with more than 13,000 labels are proposed.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2007.10.002</doi><tpages>18</tpages></addata></record> |
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| identifier | ISSN: 0377-2217 |
| ispartof | European journal of operational research, 2009-01, Vol.192 (2), p.396-413 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences Artificial intelligence Cartography Computer science control theory systems Exact sciences and technology Heuristic Labeling Large-scale optimization Map labeling Metaheuristics Metaheuristics POPMUSIC Map labeling Large-scale optimization Tabu search Pattern recognition. Digital image processing. Computational geometry POPMUSIC Popular music Preferences Studies Tabu search |
| title | POPMUSIC for the point feature label placement problem |
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