Spatial analysis of the environmental impact of lack of basic sanitation in sullana, using Geographic Information Systems (GIS) 2025

Abstract

This study analyzes the spatial distribution of the environmental impact associated with the lack of basic sanitation in Sullana, Peru, using Geographic Information Systems (GIS) and statistical modeling. Using a rigorously validated synthetic dataset (N=600), a multiple linear regression (OLS) model was applied to evaluate how variables such as sewer coverage rate, population density, and distance to discharge points influence fecal coliform concentrations as indicators of contamination. The results confirm that lack of sanitation is a direct and quantifiable driver of environmental degradation: higher sewer coverage is associated with significantly lower levels of contamination (negative relationship), while high population density increases the pollution load (positive relationship). Furthermore, pollution decreases with distance from discharge points, demonstrating its localized effect. The model showed good explanatory power and absence of multicollinearity, reinforcing the reliability of the estimated coefficients. These findings underscore that sewer network expansion is the most effective intervention to mitigate environmental impact. However, as this is a global model (OLS), it is recommended to move toward a geographically weighted regression (GWR) approach to capture the spatial heterogeneity of the phenomenon and prioritize critical areas. The study contributes to closing a gap in the literature by focusing on environmental impact—rather than human health—and by providing a geospatial basis for public policy planning in sanitation