<?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>2215-2652</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[Ingeniería]]></abbrev-journal-title>
<issn>2215-2652</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-26522024000100043</article-id>
<article-id pub-id-type="doi">10.15517/ri.v34i1.56618</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Uncertainly in land value modeling of the San José Metropolitan Region, Costa Rica]]></article-title>
<article-title xml:lang=""><![CDATA[La incertidumbre en la modelación de valores del suelo de la Gran Área Metropolitana, Costa Rica]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Molina]]></surname>
<given-names><![CDATA[Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vargas Aguilar]]></surname>
<given-names><![CDATA[Darío]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Costa Ric  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<volume>34</volume>
<numero>1</numero>
<fpage>43</fpage>
<lpage>50</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-26522024000100043&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-26522024000100043&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-26522024000100043&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los patrones de valor del suelo muestran asociaciones espaciales claras con accesibilidad a centralidades urbanas y a factores físicos de un territorio. Sin embargo, las predicciones basadas en esta estructura pueden ser altamente inciertas, dado que los datos mismos también exhiben aglomeración (y, por tanto, permiten mejores predicciones en las zonas más densamente muestreadas). Se presenta una evaluación de esta incertidumbre para extrapolaciones de valor del suelo en la Gran Área Metropolitana de Costa Rica mediante simulaciones gaussianas condicionales y una exploración de los determinantes de esta incertidumbre, como forma de reconocer fortalezas y debilidades de esta predicción. La predicción E-Type simulada resultó marginalmente mejor que extrapolaciones mediante kriging ordinario y produjo una cuantificación espacialmente explícita de la incertidumbre. El patrón de incertidumbre resultó ser un espejo de los valores del suelo. Se encontró que la incertidumbre se reduce con características asociadas a mayor aptitud del suelo para usos urbanos y, por tanto, de mayor precio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Land value patterns show very distinct spatial associations with accessibility to urban centralities and physical factors in a territory. However, predictions based on models of this structure can be highly uncertain, as the underlying data also may show clustering (thus allowing for better predictions in more densely sampled areas). An assessment of this uncertainty for land value extrapolations in the San José Metropolitan Region of Costa Rica is presented, via conditional Gaussian simulation, and the determinants of this uncertainty were explored, to find spatial strengths and weaknesses in the modeling efforts. The E-Type prediction from the conditional Gaussian simulation was found to marginally improve on ordinary kriging methods and it also provided explicit uncertainty patterns, which are the inverse of the land value prediction. The estimated uncertainty was found to decrease with characteristics that identify suitability for urban land use (and thus higher land values).]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Extrapolación]]></kwd>
<kwd lng="es"><![CDATA[factores espaciales]]></kwd>
<kwd lng="es"><![CDATA[incertidumbre]]></kwd>
<kwd lng="es"><![CDATA[simulación gaussiana secuencial]]></kwd>
<kwd lng="es"><![CDATA[valor del suelo]]></kwd>
<kwd lng="en"><![CDATA[Extrapolation]]></kwd>
<kwd lng="en"><![CDATA[land values]]></kwd>
<kwd lng="en"><![CDATA[sequential Gaussian simulation]]></kwd>
<kwd lng="en"><![CDATA[spatial factors]]></kwd>
<kwd lng="en"><![CDATA[uncertainty]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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