AGDP: A spatial disaggregation of real GDP through the integration of land use data.

Abstract / DescriptionThis dataset comprises spatially disaggregated Gross Domestic Product (GDP) estimates for the Republic of Benin, derived through an entropy-based raster allocation methodology. National and sub-national GDP aggregates were redistributed across a uniform grid using entropy-weighted spatial interpolation. The entropy weighting layer was constructed using 10 m resolution national land cover data for Benin (2025), which served as the primary proxy covariate informing the spatial disaggregation and helping minimize information loss while maximizing spatial specificity. The resulting product is a continuous economic surface at ~1 km × 1 km spatial resolution, delivered in ESRI Shapefile (.shp) format, referencing the 2025 fiscal/calendar year. Methodological BasisThe entropy maximization approach is grounded in principles of information theory, wherein the disaggregation process seeks the least-biased spatial redistribution of aggregate economic data consistent with known constraints (e.g., total national GDP, sectoral shares) and auxiliary spatial covariates. In this version, 10 m national land cover data (2025) was used as the primary proxy input for entropy weighting, given the absence of direct agricultural GDP data at this resolution. This method reduces systematic bias relative to naive areal-weighting approaches by accounting for land cover heterogeneity across administrative units. ApplicationsAgricultural Potential Assessment: supports identification of geographic areas exhibiting favorable socioeconomic conditions for agricultural expansion, using localized economic output as a proxy indicator of productive capacity.Agricultural Land-Use Planning: enables evidence-based delineation of suitability zones for crop-specific or multi-crop agricultural planning, integrating economic output data with biophysical suitability layers (soil, climate, terrain).Targeted Resource Allocation: facilitates spatial prioritization for agricultural input distribution (e.g., fertilizer, subsidized seed, extension services) by identifying zones of economic underperformance or high marginal returns to investment.Poverty and Livelihood Mapping: provides a continuous economic covariate for small-area estimation (SAE) of poverty indices and livelihood vulnerability assessments, particularly in data-sparse rural contexts where household survey coverage is limited.