Abstract
The Data Revolution provides an unprecedented opportunity for enhancing evidence-based decision making in the area of public policy. Machine learning techniques will play an increasingly important role in knowledge extraction in data bases associated with important social phenomena such as poverty, crime and environmental degradation. As much of the corresponding data is spatio-temporal it is important to develop spatial data mining methodologies to attack these problems. In this paper, we will use spatial data mining techniques to analyze the relation between poverty and crime and poverty and environmental integrity in two bespoke data sets. We will show that the role and relation of poverty is measurable but is highly complex and heterogeneous.
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Stephens, C.R., López-Corona, O., Ruíz, R.D., Santana, W.M. (2020). Poverty and Its Relation to Crime and the Environment: Applying Spatial Data Mining to Enhance Evidence-Based Policy. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_25
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DOI: https://doi.org/10.1007/978-3-030-14118-9_25
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