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

José Alberto Sousa Torres, Paulo Henrique dos Santos, Daniel Alves da Silva, C. E. L. Veiga, Márcio Bastos Medeiros, Guilherme Fay Verqara, Fábio L. L. Mendonça, Rafael Timóteo de Sousa Júnior
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Abstract

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.
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利用空间数据和聚类分析自动检测环境违规者之间的非琐碎关系
亚马逊雨林是地球上最重要的生物多样性保护区。它在应对全球变暖和地球气候变化方面发挥着核心作用。尽管它很重要,但在2021年,巴西亚马逊雨林的非法砍伐过程是十年来最严重的一年。数据显示,那一年有超过1万公里的原始森林被破坏,比2020年增加了29%。为了打击毁林者的行为,近几十年来,巴西环境检查机构征收了140多亿美元的环境罚款。然而,它并没有有效地减少森林砍伐,因为只有4%的森林被有效收集,这并没有阻止不法分子砍伐森林。这是因为很难识别真正的罪犯,他们用替罪羊来掩盖自己的罪行。本文的主要目的是提出一种方法,通过分析与巴西政府机构在过去三十年中施加的罚款有关的数据,找到真正的环境违规者。我们提出了一种方法,在从罚款中提取的地理和时间数据中使用聚类技术来识别替罪羊和大土地所有者之间的非琐碎相关性。自动识别的链接被加载到图形分析数据库中进行准确性评估。观察结果是积极的,表明该策略可以有效地识别真正的罪魁祸首。
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