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

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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.

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Notes

  1. For a more thorough review on this topic, readers should consult Gozdziak and Collett (2005) and Schauer and Wheaton (2006).

  2. Consider, for example, the variations in penalties for prostitutes between Arizona and Texas. In Arizona, there are no financial penalties, but the first, second, third offenses and fourth offenses confer 15, 30, 60 and 180 days of jail time, respectively. In Texas, the first, second and third offenses confer up to 180 days and/or $2000, 1 year and/$4000, 2 years and/or $10,000, respectively (Procon 2017). There are equally diverse penalties for customers, pimps and brothel owners across all 50 states.

  3. In the U.S., the religious right refers to a loose coalition of evangelical Protestants, Latter Day Saints, Roman Catholics, and other groups that support socially conservative policies on prostitution, contraception, abortion and homosexuality. Weitzer (2010, 64) defines abolitionist feminists as those “who argue that the sex industry should be eliminated because of its objectification and oppressive treatment of women, considered to be inherent in sex for sale.”

  4. For more details on the impacts of red-light districts, prostitution, neighborhood effects and municipal regulation in Belgium, see Boels and Verhage (2016) and Prior and Croft (2012).

  5. This list was obtained from the “MySQL Stopwords” list at http://www.ranks.nl/stopwords/.

  6. https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md

  7. Predictive models are different from exploratory models in many ways. For a more thorough discussion, readers should consult Shmueli (2010) or Mack and Grubesic (2009). In short, issues of multicollinearity, heteroskedasticity and endogeneity are important to correct in explanatory models, but this is not the case for good forecasting models.

  8. Low-grade residential is a catch-all term for neighborhoods dominated by poor-quality housing, which includes trailer parks, poorly constructed multi-family apartment buildings and single-family houses.

  9. This was not placed in the Commerceplex by the authors.

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Authors and Affiliations

  1. Center for Spatial Reasoning & Policy Analytics, College of Public Service & Community Solutions, Arizona State University, Phoenix, AZ, 85004, USA

    Edward Helderop & Anthony Grubesic

  2. School of Criminology and Criminal Justice, Arizona State University, Phoenix, AZ, 85004, USA

    Jessica Huff & Danielle Wallace

  3. Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Marina del Rey, Los Angeles, CA, 90292, USA

    Fred Morstatter

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  1. Edward Helderop
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Correspondence to Edward Helderop.

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Helderop, E., Huff, J., Morstatter, F. et al. Hidden in Plain Sight: A Machine Learning Approach for Detecting Prostitution Activity in Phoenix, Arizona. Appl. Spatial Analysis 12, 941–963 (2019). https://doi.org/10.1007/s12061-018-9279-1

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