When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones
U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between usin...
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description | U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. We find CZs often reduce—but do not eliminate and can even increase—spatial autocorrelation for variables across categories. We then test the potential for regional variation in spatial autocorrelation with a series of maps and find variation based on the variable of interest. We conclude that the use of CZs does not eliminate the need to test for spatial autocorrection, but CZs may be useful for reducing spatial autocorrelation in many cases. |
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An empirical description of spatial autocorrelation in U.S. counties versus commuting zones</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Carpenter, Craig Wesley ; Lotspeich-Yadao, Michael C ; Tolbert, Charles M</creator><contributor>Kato, Hironori</contributor><creatorcontrib>Carpenter, Craig Wesley ; Lotspeich-Yadao, Michael C ; Tolbert, Charles M ; Kato, Hironori</creatorcontrib><description>U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. 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We conclude that the use of CZs does not eliminate the need to test for spatial autocorrection, but CZs may be useful for reducing spatial autocorrelation in many cases.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0270303</identifier><identifier>PMID: 35830393</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Analysis ; Autocorrelation ; Cluster analysis ; Commuting ; Computer and Information Sciences ; Earth Sciences ; Economic aspects ; Economic indicators ; Economic statistics ; entrepreneurship ; Error correction ; Evaluation ; Geography ; Geospatial data ; Labor force ; Labor market ; Land use ; Management ; Physical Sciences ; Regional development ; Research and Analysis Methods ; Researchers ; Social Sciences ; spatial variation ; United States ; Variables</subject><ispartof>PloS one, 2022-07, Vol.17 (7), p.e0270303-e0270303</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Carpenter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Carpenter et al 2022 Carpenter et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5473-8f92716984f42d6813b7857af29edc44bc70c25a9014b775f6dd153ee655a9103</citedby><cites>FETCH-LOGICAL-c5473-8f92716984f42d6813b7857af29edc44bc70c25a9014b775f6dd153ee655a9103</cites><orcidid>0000-0001-7511-1168 ; 0000-0001-5537-3654</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278745/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278745/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2919,23857,27915,27916,53782,53784,79361,79362</link.rule.ids></links><search><contributor>Kato, Hironori</contributor><creatorcontrib>Carpenter, Craig Wesley</creatorcontrib><creatorcontrib>Lotspeich-Yadao, Michael C</creatorcontrib><creatorcontrib>Tolbert, Charles M</creatorcontrib><title>When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones</title><title>PloS one</title><description>U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. We find CZs often reduce—but do not eliminate and can even increase—spatial autocorrelation for variables across categories. 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An empirical description of spatial autocorrelation in U.S. counties versus commuting zones</atitle><jtitle>PloS one</jtitle><date>2022-07-13</date><risdate>2022</risdate><volume>17</volume><issue>7</issue><spage>e0270303</spage><epage>e0270303</epage><pages>e0270303-e0270303</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>U.S. Commuting Zones (CZs) are an aggregation of county-level data that researchers commonly use to create less arbitrary spatial entities and to reduce spatial autocorrelation. However, by further aggregating data, researchers lose point data and the associated detail. Thus, the choice between using counties or CZs often remains subjective with insufficient empirical evidence guiding researchers in the choice. This article categorizes regional data as entrepreneurial, economic, social, demographic, or industrial and tests for the existence of local spatial autocorrelation in county and CZ data. 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subjects | Analysis Autocorrelation Cluster analysis Commuting Computer and Information Sciences Earth Sciences Economic aspects Economic indicators Economic statistics entrepreneurship Error correction Evaluation Geography Geospatial data Labor force Labor market Land use Management Physical Sciences Regional development Research and Analysis Methods Researchers Social Sciences spatial variation United States Variables |
title | When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones |
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