<?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>2663-3981</journal-id>
<journal-title><![CDATA[Revista cartográfica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. cartogr.]]></abbrev-journal-title>
<issn>2663-3981</issn>
<publisher>
<publisher-name><![CDATA[Instituto Panamericano de Geografía e Historia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2663-39812025000200031</article-id>
<article-id pub-id-type="doi">10.35424/rcarto.i111.5932</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Cartografía epidemiológica: comparación de tres métodos de análisis espacial con SIG y software libre para la COVID-19 en Málaga, España]]></article-title>
<article-title xml:lang="en"><![CDATA[Epidemiological cartography: Comparison between three methods by using GIS and free software for COVID-19 in Malaga, Spain]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vargas Molina]]></surname>
<given-names><![CDATA[Sebastián Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Málaga Departamento de Geografía ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Spain</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>
<numero>111</numero>
<fpage>31</fpage>
<lpage>51</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2663-39812025000200031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2663-39812025000200031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2663-39812025000200031&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este trabajo presenta tres medidas epidemiológicas: tasa de prevalencia, exceso de riesgo y riesgo relativo, junto con su representación en unidades espaciales discretas (distritos censales y celdas de malla) y superficies continuas, superando diferencias entre delimitaciones administrativas. Estos métodos se comparan a escala intraurbana en Málaga, España, usando datos a nivel domiciliario de casos positivos de COVID-19 recolectados durante la fase aguda de la pandemia (2020-2022), los cuales son procesados y analizados con SIG y software libre y de código abierto. Los cálculos vectoriales estiman un área de mayor riesgo relativo de 7 km², con 63,655 personas, mientras que los ráster amplían el área a 16 km², con 206,900 personas, y con un riesgo 40% por encima de la prevalencia base. La comparación muestra que las superficies ráster son más sólidas al superar límites espaciales arbitrarios, aunque requieren datos puntuales, usualmente inaccesibles. Por el contrario, las estimaciones basadas en polígonos son menos precisas, y subestiman tanto la extensión del área como a la población en mayor riesgo por mitad, pero el tipo de datos son de mayor disponibilidad. Se concluye que en epidemiología, salud pública y geografía de la salud es crucial comparar indicadores y representaciones cartográficas tanto para la comunicación de resultados adecuados y transparentes, como para la toma decisiones precisas, y se sugieren pasos siguientes para el análisis.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper presents three epidemiological measures: prevalence rate, excess risk, and relative risk, alongside their representation through both discrete spatial units (such as census districts and grid cells) and continuous surfaces. These methods are compared at an intraurban scale in Málaga, Spain, using household-level positive COVID-19 cases data from the acute phase of the pandemic, spanning 2020 to 2022, which are processed and analyzed with free open-source software and GIS. Vector-based calculations estimate the area of highest relative risk in Málaga to be approximately 7 km², encompassing a population of 63,655 people. In contrast, raster-based calculations estimate an area of 16 km², containing a population of 206,900 people, and a 40% higher risk when compared to baseline prevalence. The comparison demonstrates that raster-based relative risk surfaces are more robust, as they overcome the limitations of arbitrary spatial boundaries. However, generating these surfaces require point-level case data, which is often publicly unavailable. Conversely, polygon-based estimations are less precise, and underestimate both the extent and the amount of population at-risk by half, but rely on more readily accessible aggregated data. The study concludes by emphasizing the importance of comparing between indicators and cartographic representation for both results communication and accurate decision-making in the fields of epidemiology, public health, and health geography, as well as by suggesting further steps in the analysis.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[epidemiología]]></kwd>
<kwd lng="es"><![CDATA[cartografía]]></kwd>
<kwd lng="es"><![CDATA[Sistema de Información Geográfica]]></kwd>
<kwd lng="es"><![CDATA[salud pública]]></kwd>
<kwd lng="es"><![CDATA[análisis espacial]]></kwd>
<kwd lng="en"><![CDATA[epidemiology]]></kwd>
<kwd lng="en"><![CDATA[cartography]]></kwd>
<kwd lng="en"><![CDATA[Geographic Information System]]></kwd>
<kwd lng="en"><![CDATA[public health]]></kwd>
<kwd lng="en"><![CDATA[spatial analysis]]></kwd>
</kwd-group>
</article-meta>
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