<?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>0121-215X</journal-id>
<journal-title><![CDATA[Cuadernos de Geografía: Revista Colombiana de Geografía]]></journal-title>
<abbrev-journal-title><![CDATA[Cuad. Geogr. Rev. Colomb. Geogr.]]></abbrev-journal-title>
<issn>0121-215X</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ciencias Humanas de la Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-215X2023000100226</article-id>
<article-id pub-id-type="doi">10.15446/rcdg.v32n1.98039</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Integración de información estadística y observaciones de la Tierra para el cálculo de indicadores ODS 11.3.1 y 11.7.1 en Colombia, aplicando técnicas de clasificación Random Forest]]></article-title>
<article-title xml:lang="en"><![CDATA[Integration of statistical information and Earth observations for the calculation of SDG 11.3.1 and 11.7.1 indicators in Colombia, applying Random Forest classification techniques]]></article-title>
<article-title xml:lang="pt"><![CDATA[Integração de informações estatísticas e observações da Terra para o cálculo dos indicadores SDG 11.3.1 e 11.7.1 na Colômbia, aplicando técnicas de classificação de Florestas Aleatórias]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez Gutiérrez]]></surname>
<given-names><![CDATA[Miguel Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lasso Rodríguez]]></surname>
<given-names><![CDATA[Juan Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Durán Gil]]></surname>
<given-names><![CDATA[Carlos Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogota D.C]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Departamento Administrativo Nacional de Estadistica - DANE  ]]></institution>
<addr-line><![CDATA[Bogota D.C]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Departamento Administrativo Nacional de Estadistica - DANE  ]]></institution>
<addr-line><![CDATA[Bogota D.C]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>32</volume>
<numero>1</numero>
<fpage>226</fpage>
<lpage>257</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-215X2023000100226&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-215X2023000100226&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-215X2023000100226&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este artículo presenta el cálculo de los indicadores ODS 11.3.1 y 11.7.1 en Colombia, integrando información estadística y geoespacial, como fuentes esenciales para lograr una medición robusta y desagregada territorialmente. Con base en los procesos definidos por ONU-Hábitat, se desarrolla una metodología con énfasis geoespacial, soportada en el procesamiento de imágenes satelitales a través del algoritmo de clasificación supervisada Random Forest, para obtener las métricas requeridas en el cálculo de los dos indicadores, como son las áreas construidas, el consumo del suelo urbano, y los espacios abiertos, en conjunto con análisis integrados de información estadística. El indicador ODS 11.3.1 durante el periodo 2015-2020 se calculó para 63 ciudades definidas, cuyo valor nacional de 0,43 destaca que en el país se hace uso eficiente del suelo, mientras que el ODS 11.7.1 para 2018, se calculó en una muestra representativa de nueve ciudades, derivando que a nivel nacional un 33,2 % de las áreas construidas se destinan a espacios abiertos de uso público. Estos resultados hacen del país un referente regional en el monitoreo de los ODS, resaltando la posibilidad de actualizar los resultados a futuro, gracias a los procesamientos automatizados en la nube empleando desarrollos por medio de scripts.  Ideas destacadas: artículo de investigación que presenta las potencialidades de las técnicas analíticas enmarcadas en la clasificación supervisada de imágenes satelitales, cuyas aplicaciones para el cálculo de indicadores ODS, se consolidan como campo de acción relevante en un contexto de integración de la información estadística y geoespacial.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This article presents the calculation of the SDG 11.3.1 and 11.7.1 indicators in Colombia, integrating statistical and geospatial information, as essential sources to achieve a robust and territorially disaggregated measurement. Based on the processes defined by UN-Habitat, it develops a methodology with a geospatial emphasis, supported by the processing of satellite images through the Random Forest supervised classification algorithm, to obtain the metrics required in the calculation of the two indicators, such as built-up areas, urban land consumption, and open spaces, together with integrated analysis of statistical information. The SDG 11.3.1 indicator for the 2015-2020 period was calculated for 63 cities, whose national value of 0.43 highlights that efficient land use is made in the country, while SDG 11.7.1 for the year 2018 was calculated in a representative sample of nine cities, which indicates that at the national level 33.2 % of built areas are allocated to open spaces for public use. These results make the country a regional benchmark in the monitoring of the SDGs, highlighting the possibility of updating the results in the future, thanks to automated processing in the cloud using scripts developments.  Highlights: research article that presents the potential of analytical techniques framed in the supervised classification of satellite images. Its applications for the calculation of SDGs indicators are consolidated as a relevant field of action in a context of integration of statistical and geospatial information.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Este artigo apresenta o cálculo dos indicadores ODS 11.3.1 e 11.7.1 na Colômbia, integrando informações estatísticas e geoespaciais, como fontes essenciais para alcançar uma medição robusta e desagregada territorialmente. Com base nos processos definidos pela oNU-Habitat, é desenvolvida uma metodologia com ênfase geoespacial, apoiada no processamento de imagens de satélite por meio do algoritmo de classificação supervisionada Random Forest, para obter as métricas necessárias no cálculo dos dois indicadores, como são áreas construídas, consumo de solo urbano e espaços abertos, juntamente com análises integradas de informações estatísticas. O indicador ODS 11.3.1 para o período 2015-2020 foi calculado para 63 cidades definidas, cujo valor nacional de 0,43 destaca que o uso eficiente do solo é feito no país, enquanto o ODS 11.7.1 para o ano de 2018 foi calculado em uma amostra representativa de nove cidades, sendo que ao nível nacional 33,2 % das áreas construídas são destinadas a espaços livres de uso público. Estes resultados fazem do país uma referência regional no monitoramento dos ODS, destacando a possibilidade de atualização dos resultados no futuro, graças ao processamento automatizado na nuvem utilizando desenvolvimentos por meio de scripts.  Ideias destacadas: artigo de pesquisa que apresenta o potencial de técnicas analíticas enquadradas na classificação supervisionada de imagens de satélite, cujas aplicações para o cálculo de indicadores ODS se consolidam como um campo de atuação relevante em um contexto de integração de informações estatísticas e geoespaciais.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[algoritmo]]></kwd>
<kwd lng="es"><![CDATA[bosques aleatorios]]></kwd>
<kwd lng="es"><![CDATA[datos geoespaciales]]></kwd>
<kwd lng="es"><![CDATA[desarrollo sostenible]]></kwd>
<kwd lng="es"><![CDATA[estadística]]></kwd>
<kwd lng="es"><![CDATA[imágenes de satélite]]></kwd>
<kwd lng="en"><![CDATA[algorithm]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[geospatial data]]></kwd>
<kwd lng="en"><![CDATA[sustainable development]]></kwd>
<kwd lng="en"><![CDATA[statistics]]></kwd>
<kwd lng="en"><![CDATA[satellite imagery]]></kwd>
<kwd lng="pt"><![CDATA[algoritmo]]></kwd>
<kwd lng="pt"><![CDATA[florestas aleatórias]]></kwd>
<kwd lng="pt"><![CDATA[dados geográficos]]></kwd>
<kwd lng="pt"><![CDATA[desenvolvimento sustentável]]></kwd>
<kwd lng="pt"><![CDATA[estatística]]></kwd>
<kwd lng="pt"><![CDATA[imagens de satélite]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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