<?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>2448-718X</journal-id>
<journal-title><![CDATA[El trimestre económico]]></journal-title>
<abbrev-journal-title><![CDATA[El trimestre econ]]></abbrev-journal-title>
<issn>2448-718X</issn>
<publisher>
<publisher-name><![CDATA[Fondo de Cultura Económica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-718X2016000400679</article-id>
<article-id pub-id-type="doi">10.20430/ete.v83i332.236</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Mapa y jerarquía espacial de la pobreza en México. Un nuevo procedimiento para identificar el patrón espacial de los problemas sociales]]></article-title>
<article-title xml:lang="en"><![CDATA[Mapping Poverty and its Spatial Hierarchy in Mexico. A New Method for Identifying the Spatial Pattern of Social Problems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Treviño C.]]></surname>
<given-names><![CDATA[Jesús A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Nuevo León Facultad de Arquitectura ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2016</year>
</pub-date>
<volume>83</volume>
<numero>332</numero>
<fpage>679</fpage>
<lpage>723</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-718X2016000400679&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-718X2016000400679&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-718X2016000400679&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Esta investigación presenta y aplica un nuevo procedimiento para jerarquizar espacialmente la pobreza. El método propuesto combina simultáneamente variables de magnitud e intensidad utilizando técnicas estadísticas recientes (remuestreo y autocorrelación espacial) y tradicionales, así como rutinas de superposición espacial de los Sistemas de Información Geográfica. Mientras magnitud e intensidad se refieren a datos absolutos y relativos, respectivamente, cada variable puede estar concentrada o aglomerada en el espacio. En este estudio, concentración es la presencia de valores globales altos, independientemente de su localización, y aglomeración es la concentración de valores locales altos espacialmente contiguos. Ambas, aglomeración y concentración, son fusionadas mediante un procedimiento de superposición geográfica para crear conglomerados de magnitud o intensidad de pobreza. Los casos al interior de estos conglomerados son clasificados por procedimientos gaussianos (discontinuidades naturales) o paretianos (puntas y colas) para crear una jerarquía espacial. Por primera vez en el estudio de la pobreza, la jerarquía espacial resultante es producto de la combinación simultánea de los procesos de concentración y aglomeración medidos en términos absolutos y relativos. Los beneficios del procedimiento en una política pública espacialmente orientada son ilustrados abordando la focalización espacial de la pobreza en los 2 456 municipios mexicanos en el 2010. La metodología propuesta en esta investigación puede adaptarse fácilmente para identificar los patrones espaciales de otros problemas sociales, tales como crimen, industria, enfermedades, contaminación o justicia ambiental en diferentes áreas o países.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This research presents and applies a new procedure to elaborate a spatial hierarchy of poverty. The proposed method simultaneously combines variables of magnitude and intensity based on recent (bootstrapping &amp; spatial autocorrelation) and traditional statistical techniques, and overlay routines of Geographical Information Systems. While magnitude and intensity refer to absolute and relative data, respectively, each variable may be concentrated or agglomerated in space. In this study, concentration is the presence of high global values, regardless of their location, and agglomeration is the concentration of spatially contiguous high local values. Both agglomeration and concentration are merged through a geographical overlay procedure to create conglomerates of magnitude or intensity of poverty. Cases inside these conglomerates are classified by gaussian (natural breaks) or paretian (heads and tails) procedures to set up a spatial hierarchy. For the first time in the study of the spatial pattern of poverty, the resulting spatial hierarchy is based on the simultaneous combination of the concentration and agglomeration processes measured in relative and absolute terms. The benefits of the procedure for an area-based public policy are illustrated by assessing the spatial targeting of poverty in the 2,456 Mexican municipios in 2010. The suggested methodology in this research may be easily extended to identify other spatial patterns, such as crime, industry, diseases, pollution or environmental justice in different areas or countries.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis espacial]]></kwd>
<kwd lng="es"><![CDATA[aglomeración]]></kwd>
<kwd lng="es"><![CDATA[concentración]]></kwd>
<kwd lng="es"><![CDATA[conglomerados]]></kwd>
<kwd lng="es"><![CDATA[pobreza]]></kwd>
<kwd lng="en"><![CDATA[spatial analysis]]></kwd>
<kwd lng="en"><![CDATA[agglomeration]]></kwd>
<kwd lng="en"><![CDATA[concentration]]></kwd>
<kwd lng="en"><![CDATA[conglomerates]]></kwd>
<kwd lng="en"><![CDATA[poverty]]></kwd>
</kwd-group>
</article-meta>
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