An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data
The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give i...
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Veröffentlicht in: | Geographical analysis 2001-01, Vol.33 (1), p.19-28 |
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description | The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations. |
doi_str_mv | 10.1111/j.1538-4632.2001.tb00434.x |
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It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations.</description><identifier>ISSN: 0016-7363</identifier><identifier>EISSN: 1538-4632</identifier><identifier>DOI: 10.1111/j.1538-4632.2001.tb00434.x</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Cancer ; Geography ; Italy ; Mortality ; Regions ; Simulation ; Spatial analysis ; Spatial distribution ; Statistical models</subject><ispartof>Geographical analysis, 2001-01, Vol.33 (1), p.19-28</ispartof><rights>2001 The Ohio State University</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3484-85e44f337ecb1ee60fc5065ca45fed597d7679a86427b8e2976aa2605cd5da583</citedby><cites>FETCH-LOGICAL-c3484-85e44f337ecb1ee60fc5065ca45fed597d7679a86427b8e2976aa2605cd5da583</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>Ekwaru, John Paul</creatorcontrib><creatorcontrib>Walter, Stephen D.</creatorcontrib><title>An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data</title><title>Geographical analysis</title><description>The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations.</description><subject>Cancer</subject><subject>Geography</subject><subject>Italy</subject><subject>Mortality</subject><subject>Regions</subject><subject>Simulation</subject><subject>Spatial analysis</subject><subject>Spatial distribution</subject><subject>Statistical models</subject><issn>0016-7363</issn><issn>1538-4632</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqVkM1OwzAQhC0EEqXwDhYHbgmO_5JyQVEpBakCicKBk-U6G5qQJsV21fbtcSjiji_2emZWmg-hy4TESTjXdZwIlkVcMhpTQpLYLwjhjMe7IzT4k47RIGgySplkp-jMuZoQQtOEDdB73uJ8vbbdrlppX3UtLjuL_RLwi24_cV7U2kBr9njug-x8ZX4M83WYdIPHzcZ5sFX7gbeVX_b_1gG-016fo5NSNw4ufu8herufvI4fotnz9HGczyLDeMajTADnJWMpmEUCIElpBJHCaC5KKMQoLVKZjnQmOU0XGdBRKrWmkghTiEKLjA3R1WFvKPG1AefVqnIGmka30G2cYhnlVDAajDcHo7GdcxZKtbahtN2rhKiepqpVj0z1yFRPU_3SVLsQvj2Et1UD-38k1XSSP4UX-wblFH0B</recordid><startdate>200101</startdate><enddate>200101</enddate><creator>Ekwaru, John Paul</creator><creator>Walter, Stephen D.</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>200101</creationdate><title>An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data</title><author>Ekwaru, John Paul ; Walter, Stephen D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3484-85e44f337ecb1ee60fc5065ca45fed597d7679a86427b8e2976aa2605cd5da583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Cancer</topic><topic>Geography</topic><topic>Italy</topic><topic>Mortality</topic><topic>Regions</topic><topic>Simulation</topic><topic>Spatial analysis</topic><topic>Spatial distribution</topic><topic>Statistical models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ekwaru, John Paul</creatorcontrib><creatorcontrib>Walter, Stephen D.</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Geographical analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ekwaru, John Paul</au><au>Walter, Stephen D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data</atitle><jtitle>Geographical analysis</jtitle><date>2001-01</date><risdate>2001</risdate><volume>33</volume><issue>1</issue><spage>19</spage><epage>28</epage><pages>19-28</pages><issn>0016-7363</issn><eissn>1538-4632</eissn><abstract>The rank adjacency statistic D provides a simple method to assess regional clustering. It is defined as the weighted average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper the usual normal approximation to the D statistic is found to give inaccurate results if the data are sparse and some regions have tied ranks. Adjusted formulae for the moments of D that allow for the existence of ties are derived. An example of analyses of sparse mortality data (with many regions having no deaths, and hence tied ranks) showed satisfactory agreement between the adjusted formulae and the empirical distribution of the D statistic. We conclude that the D statistic, when used with adjusted moments, provides a valid approximate method to evaluate spatial clustering, even in sparse data situations.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1538-4632.2001.tb00434.x</doi><tpages>10</tpages></addata></record> |
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subjects | Cancer Geography Italy Mortality Regions Simulation Spatial analysis Spatial distribution Statistical models |
title | An Approximation for the Rank Adjacency Statistic for Spatial Clustering with Sparse Data |
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