<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1870-3925</journal-id>
<journal-title><![CDATA[Región y sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Región y sociedad]]></abbrev-journal-title>
<issn>1870-3925</issn>
<publisher>
<publisher-name><![CDATA[El Colegio de Sonora]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1870-39252020000100108</article-id>
<article-id pub-id-type="doi">10.22198/rys2020/32/1268</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[La reconfiguración de los subcentros de empleo en Ciudad Juárez, Chihuahua, 2004-2014]]></article-title>
<article-title xml:lang="en"><![CDATA[Employment Sub-centers Reconfiguration in Ciudad Juárez, Chihuahua, 2004-2014]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chaparro Hernández]]></surname>
<given-names><![CDATA[Isaac]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Hernández]]></surname>
<given-names><![CDATA[Vladimir]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez Instituto de Arquitectura, Diseño y Arte ]]></institution>
<addr-line><![CDATA[Ciudad Juárez Chihuahua]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez Instituto de Arquitectura, Diseño y Arte ]]></institution>
<addr-line><![CDATA[Ciudad Juárez Chihuahua]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<volume>32</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1870-39252020000100108&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1870-39252020000100108&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1870-39252020000100108&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo es identificar la estructura urbana de Ciudad Juárez y demostrar la reconfiguración de los subcentros de empleo mediante la implementación de tres indicadores de autocorrelación espacial. El método considera los principios de la dependencia espacial mediante la detección de la autocorrelación global y local. Para ello se hace uso de tres indicadores: I de Moran, G de Getis-Ord y c de Geary. Las variables usadas fueron la densidad bruta de empleo total, en manufactura y terciario de 2004 a 2014 a escala de áreas geoestadísticas básicas. La falta de censos económicos más recientes se considera una limitante, además de que no se encontraron investigaciones previas que hayan utilizado estrictamente la metodología aquí propuesta para comparar y discutir resultados. A través de los indicadores de autocorrelación global fue posible identificar el grado de agrupamiento de las densidades de empleo, mientras que los locales permitieron caracterizar los subcentros de trabajo. Los resultados muestran clústeres de alta densidad que conforman cuatro islas dispersas y dos núcleos de empleo. Además, se comprueba la utilidad de combinar los tres tipos de autocorrelaciones espaciales.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This article aims to identify Ciudad Juárez urban structure and to demonstrate employment sub-centers reconfiguration through the implementation of three spatial correlation indicators. This method, objectified by local and global autocorrelation detection, takes into account the principles of spatial dependency. For the purpose, there were used Moran&#8217;s I, Getis-Ord&#8217;s G, and Geary&#8217;s c indicators. Manufacturing and tertiary sectors total employment gross densities from 2004 to 2014 at basic geostatistical areas scale were the variables used. The limitation of this work is the lack of data from recent census and the absence of previous research that strictly applied the methodology used in this work, therefore it is not possible to compare and discuss results. Due to the indicators, it was possible to identify the employment densities grouping level, while the local indicators characterized employment sub-centers. Results show that high-density clusters shape four dispersed islands and two employment centers and it has been possible to prove the utility to combine the three spatial correlation types to demonstrate employment sub-centers reconfiguration.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[subcentros de empleo]]></kwd>
<kwd lng="es"><![CDATA[estructura urbana]]></kwd>
<kwd lng="es"><![CDATA[dependencia espacial]]></kwd>
<kwd lng="es"><![CDATA[autocorrelación espacial]]></kwd>
<kwd lng="es"><![CDATA[Ciudad Juárez]]></kwd>
<kwd lng="en"><![CDATA[employment sub-centers]]></kwd>
<kwd lng="en"><![CDATA[urban structure]]></kwd>
<kwd lng="en"><![CDATA[spatial dependency]]></kwd>
<kwd lng="en"><![CDATA[spatial autocorrelation]]></kwd>
<kwd lng="en"><![CDATA[Ciudad Juárez]]></kwd>
</kwd-group>
</article-meta>
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