Inteligencia Artificial para la planificación urbana en Latinoamérica
DOI:
https://doi.org/10.70577/eag5rs05Keywords:
Cluster, Datos, Geoespacial, Planificación, Urbanismos.Abstract
La planificación urbana en América Latina enfrenta desafíos significativos debido al rápido crecimiento urbano, la desigualdad socioeconómica y la vulnerabilidad ambiental. Con más del 80% de su población viviendo en zonas urbanas y una proyección del 90% para 2050, es fundamental optimizar la distribución de recursos y mejorar los servicios públicos mediante enfoques basados en datos. Este artículo propone el uso de algoritmos de clustering como herramientas clave para identificar áreas homogéneas dentro de las ciudades, facilitando una planificación más equitativa y sostenible. Mediante técnicas de ciencia de datos, como K-means y DBSCAN, se analizan indicadores urbanos agrupados en tres dimensiones: infraestructura (acceso a agua, electricidad, transporte), socioeconómica (ingresos, educación, salud) y territorial (uso del suelo, espacios verdes). Estos métodos permiten segmentar áreas críticas, como asentamientos informales o zonas con déficit de infraestructura, mejorando la toma de decisiones en políticas públicas. El análisis se apoya en un conjunto de datos sintético de 5000 registros, generado con distribuciones estadísticas realistas basadas en estudios recientes. Se aplican técnicas avanzadas como PCA para reducir dimensionalidad, normalización de variables y métricas de validación como el índice de Calinski-Harabasz. Los resultados muestran una estructura urbana bipolar con dos clusters bien definidos por K-means, mientras que DBSCAN identifica múltiples zonas de transición y ruido espacial, típico de contextos urbanos dinámicos e informales, por lo que se conluye que la combinación de clustering, análisis geoespacial y estrategias basadas en datos ofrece una metodología robusta para guiar políticas urbanas en América Latina, promoviendo la equidad y la resiliencia frente al cambio climático.
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