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Authors
Tonelli, Michelle
Subjects
predictive policing
predictive analytics
FICO
Secure Flight
Five Vs
risk assessments
big data
Advisors
Nieto-Gomez, Rodrigo
Rollins, John
Date of Issue
2016-09
Date
Sep-16
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
One of the more contentious uses of big data analytics in homeland security is predictive policing, which harnesses big data to allocate police resources, decrease crime, and increase public safety. While predictive analytics has long been in use to forecast human behavior, the framework has not proved to be a flawless undertaking. In an effort to improve outcomes of predictive policing, this thesis assesses two high-profile programs—the nation's most popular credit-scoring system and a federal flight-risk program—to determine the greatest pitfalls inherent to programs using predictive analytics. The programs are assessed using what is commonly known in big data as the four Vs—volume, velocity, variety, veracity—but with an added component of the author's creation: verification. Through this framework, it became apparent that the hardest Vs for any predictive policing program to fulfill are veracity and verification. As the field of predictive policing expands, programs face the challenge of ensuring that data used for analysis is accurate and remains accurate, and that the metrics used to verify risk assessments are sound.
Type
Thesis
Description
Department
Homeland Security
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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