<?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>1405-7425</journal-id>
<journal-title><![CDATA[Papeles de población]]></journal-title>
<abbrev-journal-title><![CDATA[Pap. poblac]]></abbrev-journal-title>
<issn>1405-7425</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma del Estado de México, Centro de Investigación y Estudios Avanzados de la Población]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-74252025000100020</article-id>
<article-id pub-id-type="doi">10.22185/24487147.2025.120.20</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo de predicción del crecimiento urbano en el estado de Nuevo León, México]]></article-title>
<article-title xml:lang="en"><![CDATA[Prediction model of urban growth in the state of Nuevo León, México]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chávez-Hernández]]></surname>
<given-names><![CDATA[Refugio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez-Flores]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Malcón-Cervera]]></surname>
<given-names><![CDATA[Claudia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cano-Olivos]]></surname>
<given-names><![CDATA[Patricia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Monterrey  ]]></institution>
<addr-line><![CDATA[ Nuevo León]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Popular Autónoma del Estado de Puebla Secretaría de Ciencia, Humanidades, Tecnología e Innovación ]]></institution>
<addr-line><![CDATA[ Estado de Puebla]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2025</year>
</pub-date>
<volume>31</volume>
<numero>120</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-74252025000100020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-74252025000100020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-74252025000100020&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Este estudio establece un marco de referencia para identificar un modelo predictivo del crecimiento urbano y su orientación para la proyección de reservas de territorio para una organización inmobiliaria en el estado de Nuevo León, México. El estudio está enfocado en un algoritmo de modelación de regresión lineal y polinómica utilizando los datos del Instituto Nacional de Estadística y Geografía (INEGI). La información de entrada son datos históricos de los años 1970 a 2010, los cuales se utilizaron para realizar proyecciones del año 2020, los resultados se comparan con el reporte de 2020 presentados por INEGI para identificar la precisión del modelo. La metodología incluye tres etapas: preparación de datos, modelación de datos en Python, y la interpretación de resultados. Se encontró una relación de coincidencia de 90.6 por ciento, la modelación final se completó hasta el año 2040, los resultados muestran una distribución de crecimiento con alto porcentaje de incremento en el área metropolitana; de 25.45 por ciento en 2030, y 75.72 por ciento en 2040. Por último, el estudio recomienda la orientación geográfica en la búsqueda de reservas para la planeación futura de obras.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study establishes a reference framework to identify a predictive model of urban growth and its orientation for the projection of land reserves for a real estate organization in the state of Nuevo León, Mexico. The study is focused on a linear and polynomial regression modeling algorithm using data from the National Institute of Statistics and Geography (INEGI). The input information is historical data from the years 1970 to 2010, which was used to make projections for the year 2020, the results are compared with the 2020 report presented by INEGI to identify the accuracy of the model. The methodology includes three stages: data preparation, data modeling in Python, and interpretation of results. A coincidence relationship of 90.6per cent was found, the final modeling was completed until the year 2040, and the results show a distribution of growth with a high percentage of increase in the metropolitan area; 25.45per cent in 2030, and 75.72 per cent in 2040. Finally, the study recommends geographic orientation in the search for reserves for future planning of works.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Crecimiento urbano]]></kwd>
<kwd lng="es"><![CDATA[modelo de regresión]]></kwd>
<kwd lng="es"><![CDATA[predicción]]></kwd>
<kwd lng="es"><![CDATA[uso de suelo]]></kwd>
<kwd lng="en"><![CDATA[Urban growth]]></kwd>
<kwd lng="en"><![CDATA[regression model]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[land use]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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