Using spatial data and cluster analysis to automatically detect non-trivial relationships between environmental transgressors

The Amazon Rainforest is the most significant biodiversi-ty reserve on the planet. It plays a central role in combating global warming and climate change on the Earth. De-spite its importance, in 2021, the illegal deforestation process in the Brazilian Amazon rainforest had the worst year in a decade. The data show that more than 10,000 kilometers of native forest were destroyed that year-an increase of 29% compared to 2020. To fight against the action of deforesters, Brazilian environmental inspection agencies imposed more than 14 billion dollars in environmental fines in recent decades. However, it has not effectively reduced deforestation as only 4% of this amount was effectively collected-not inhibiting lawbreakers from deforesting. This is due to the difficulty of identifying the real transgressors, who use scapegoats to hide their crimes. The main objective of this paper is to propose an approach to find the real environmental transgressors through the analysis of data related to the fines imposed by Brazilian governmental agencies in the last three decades. We propose a method that employ clustering techniques in geo-graphic and temporal data extracted from fines to identify non-trivial correlations between scapegoats and large landowners. The automatically identified links were load-ed into a graph analysis database for accuracy assessment. The observed results were positive and indicated that this strategy could effectively identify the real culprits.