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

Edward Helderop, Jessica Huff, Fred Morstatter, Anthony Grubesic, Danielle Wallace

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)941-963
Number of pages23
JournalApplied Spatial Analysis and Policy
Issue number4
StatePublished - Dec 1 2019


  • Machine learning
  • Natural language processing
  • Phoenix
  • Prostitution
  • Spatial analysis

ASJC Scopus subject areas

  • Geography, Planning and Development


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