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Applied Spatial Analysis and Policy

, Volume 12, Issue 4, pp 941–963 | Cite as

Hidden in Plain Sight: A Machine Learning Approach for Detecting Prostitution Activity in Phoenix, Arizona

  • Edward HelderopEmail author
  • Jessica Huff
  • Fred Morstatter
  • Anthony Grubesic
  • Danielle Wallace
Article
  • 124 Downloads

Abstract

Prostitution has been a topic of study for decades, yet many questions remain about where prostitution occurs. Difficulty in identifying prostitution activity is often attributed to the hidden and seemingly victimless nature of the crime. Despite numerous challenges associated with policing street prostitution, these encounters become more difficult to identify when they take place indoors, especially in locations away from public view, such as hotels. The purpose of this paper is to develop a strategy for identifying hotel facilities and surrounding areas that may be experiencing elevated levels of prostitution activity using high-volume, user-generated data, namely hotel reviews written by guests and posted to Travelocity.com. A unique synthesis of methods including data mining, natural language processing, machine learning, and basic spatial analysis are combined to identify facilities that may require additional law enforcement resources and/or social/health service outreach. Prostitution hotspots are identified within the city of Phoenix, Arizona and policy implications are discussed.

Keywords

Natural language processing Machine learning Prostitution Spatial analysis Phoenix 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Center for Spatial Reasoning & Policy Analytics, College of Public Service & Community SolutionsArizona State UniversityPhoenixUSA
  2. 2.School of Criminology and Criminal JusticeArizona State UniversityPhoenixUSA
  3. 3.Information Sciences Institute, Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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