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Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees

Assuncao, Renato; Neves, Marcos; Camara, Gilberto; Freitas, Corina

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      <creatorName>Assuncao, Renato</creatorName>
      <affiliation>UFMG (Federal University of Minas Gerais)</affiliation>
      <creatorName>Neves, Marcos</creatorName>
      <affiliation>EMBRAPA (Brazilian Agricutural Research Agency)</affiliation>
      <creatorName>Camara, Gilberto</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-3681-487X</nameIdentifier>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
      <creatorName>Freitas, Corina</creatorName>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
    <title>Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees</title>
    <subject>Regionalization, Constrained clustering, Graph partitioning, Optimization, Zone design, Census data analysis</subject>
    <date dateType="Issued">2007-02-20</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1080/13658810600665111</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Regionalization is a classification procedure applied to spatial objects with an areal representation, which groups them into homogeneous contiguous regions. This paper presents an efficient method for regionalization. The first step creates a connectivity graph that captures the neighbourhood relationship between the spatial objects. The cost of each edge in the graph is inversely proportional to the similarity between the regions it joins. We summarize the neighbourhood structure by a minimum spanning tree (MST), which is a connected tree with no circuits. We partition the MST by successive removal of edges that link dissimilar regions. The result is the division of the spatial objects into connected regions that have maximum internal homogeneity. Since the MST partitioning problem is NP-hard, we propose a heuristic to speed up the tree partitioning significantly. Our results show that our proposed method combines performance and quality, and it is a good alternative to other regionalization methods found in the literature.&lt;/p&gt;</description>
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