TY - JOUR
T1 - Hidden in Plain Sight
T2 - A Machine Learning Approach for Detecting Prostitution Activity in Phoenix, Arizona
AU - Helderop, Edward
AU - Huff, Jessica
AU - Morstatter, Fred
AU - Grubesic, Anthony
AU - Wallace, Danielle
N1 - Publisher Copyright:
© 2018, Springer Nature B.V.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - Machine learning
KW - Natural language processing
KW - Phoenix
KW - Prostitution
KW - Spatial analysis
UR - http://www.scopus.com/inward/record.url?scp=85056114901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056114901&partnerID=8YFLogxK
U2 - 10.1007/s12061-018-9279-1
DO - 10.1007/s12061-018-9279-1
M3 - Article
AN - SCOPUS:85056114901
SN - 1874-463X
VL - 12
SP - 941
EP - 963
JO - Applied Spatial Analysis and Policy
JF - Applied Spatial Analysis and Policy
IS - 4
ER -