A Semi-Automated Geographic Information System Approach for Pre-Census Mapping

International audience Accurate and efficient pre-census mapping is crucial for reliable population enumeration, particularly in regions with complex geographic and security constraints. This study presents a semi-automated Geographic Information System (GIS) approach for developing a detailed digital geodatabase to support Mali’s fifth pre-census, with a rigorous validation process ensuring methodological reliability. Using high-resolution satellite imagery and geospatial techniques, Enumeration Sections (ES) were delineated based on estimated population and visible boundaries such as roads and waterways. The method integrates Google Satellite imagery with geospatial data to generate ES polygons aligned with administrative limits and population thresholds. Validation in the cercles of Macina and Niono demonstrated strong accuracy, with correlation coefficients of 0.97 and 0.94, and Root Mean Square Errors (RMSE) of 37.46 and 42.79, respectively. The Mean Absolute Errors (MAE) of 26.63 and 29.58 indicate minimal deviation between estimated and field-measured populations, confirming high model consistency. While the approach enhances mapping accuracy and efficiency, limitations include dependence on satellite image quality and challenges in updating rapidly changing regions. Compared with similar GIS-based census mapping initiatives in Nigeria and Kenya, the proposed method shows comparable performance and adaptability. Beyond Mali, this scalable and cost-effective framework offers a practical solution for pre-census and population mapping in other low- and middle-income countries.