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This article describes the design of a multidimensional environmental datacube to support the creation of deforestation risk prediction models. By integrating a comprehensive set of variables from diverse data sources, such as land tenure, accessibility, connectivity, and biophysical conditions, the datacube enables researchers and policymakers to develop more accurate and informed predictive models. This supports conservation strategies and sustainable forest management in the Amazon. The strategic aggregation of these variables enhances the understanding of deforestation dynamics and empowers decision-makers with the insights needed to implement effective interventions. By facilitating a nuanced analysis of deforestation's socio-economic and environmental drivers, the datacube aids in identifying high-risk areas and formulating targeted policies that balance economic development with environmental preservation
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