Análisis geoespacial y mapeo formal de centros urbanos en el departamento de Nariño mediante operadores espaciales en R.

As of May 20, 2026

Informe en RMarkdown de la Unidad 2 sobre modelamiento espacial multicriterio para la evaluación de aptitud climática y similitud ambiental del cultivo de caña de azúcar usando datos WorldClim y R.

This project presents an advanced Business Intelligence analysis applied to Mexico's tourism sector, utilizing Geographically Weighted Regression (GWR) models. Unlike traditional global models (OLS), this approach identifies the spatial heterogeneity of tourism drivers, revealing critical regional dynamics that national averages often mask. Key technical aspects include the implementation of an Adaptive Bisquare Kernel with an optimized bandwidth of 30 nearest neighbors, selected via the Corrected Akaike Information Criterion (AICc) to ensure the highest possible model accuracy across different geographic scales. The analysis features a detailed spatial diagnostic through the cartographic visualization of Local R-squared, identifying variations in explanatory power that range from an 85% fit in the Southeast to unique border dynamics in the Northwest. Furthermore, it applies prescriptive analytics by translating local coefficients into state-specific investment strategies, addressing critical variables such as international tourist arrivals and the regional impact of crime rates. Ultimately, this work serves as a vital bridge between predictive analytics and strategic decision-making for sustainable regional development in Mexico.