Felipe A. CarranzaMetzi Rutilia Aguilar Munguía2026-02-262026-02-262025-11-05Carranza, F. A., & Aguilar Munguía, M. R. (2025). Análisis clúster con datos satelitales y sociodemográficos para clasificar el territorio salvadoreño. Investigaciones Latinoamericanas En Ingeniería Y Arquitectura, (2), 45–52. https://doi.org/10.51378/ilia.vi2.9659https://doi.org/10.51378/ilia.vi2.9659https://micelio.uca.edu.sv/handle/20.500.14513/368This study explores whether the rural area of El Salvador can be subdivided into groups of municipalities where each group has its own characteristics in terms of variables GDP per capita, electricity consumption per capita, population density, poverty rate and night light. The study was performed horizontally considering the spatial distribution of light. The light was obtained by processing satellite images with Geographic Information Systems (GIS) software. Based on the nature of the data, it was decided to apply advanced statistical clustering techniques that, supported by the advantages of computing, would allow comparing 1000 cluster possibilities by changing the classification parameters such as the method and the distance used. The study concludes that at the exploratory level, subdivision with hierarchical cluster technique is possible only by incorporating night light and advanced techniques with t-SNE. It was found that the best model of subterritories is grouped into nine categories where two groups are mainly municipalities with urban predominance and the rest with rural predominance.45-52 p.PDFesDerechos de autor 2025 Felipe A. Carranza, Metzi Aguilar MunguíaSIGTeledetecciónAprendizaje de máquinaAgrupamientoIndicadores socioeconómicosRemote sensingMachine learningClusteringSocioeconomic indicators