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Poverty and Its Relation to Crime and the Environment: Applying Spatial Data Mining to Enhance Evidence-Based Policy

  • Christopher R. StephensEmail author
  • Oliver López-Corona
  • Ricardo David Ruíz
  • Walter Martínez Santana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

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.

Keywords

Spatial data mining Naive Bayes Poverty Complexity Crime Ecosystem integrity Niche 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Christopher R. Stephens
    • 1
    • 2
    Email author
  • Oliver López-Corona
    • 1
    • 3
    • 4
  • Ricardo David Ruíz
    • 1
    • 6
  • Walter Martínez Santana
    • 1
    • 5
  1. 1.C3 - Centro de Ciencias de la Complejidady Universidad Nacional Autónoma de MéxicoMexico CityMéxico
  2. 2.Instituto de Ciencias NuclearesUniversidad Nacional Autónoma de MéxicoMexico CityMéxico
  3. 3.Cátedras CONACyT, Comisionado en Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)Mexico CityMéxico
  4. 4.Red ambiente y Sostenibilidad, Instituto de Ecología A.C.XalapaMéxico
  5. 5.Instituto Tecnológico Autónomo de México (ITAM)Mexico CityMexico
  6. 6.TechMileageScottsdaleUSA

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