This document provides a summary of predictive policing technologies and their implications. It discusses how policing has evolved from political to community-based models using broken windows theory. Two predictive policing software programs, PredPol and HunchLab, use data analysis to predict future crime locations. While these programs aim to efficiently allocate police resources, concerns include privacy violations, implementation costs, and potential biases. The document examines debates around predictive policing and big data collection in law enforcement.

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Dennis Ellis
Cloudy with a Chance of Robbery: Predictive Policing in an Era of Public Scrutiny
The convergence of society and technology is becoming an increasingly curious topic and
as we continue to move further into the eerily Orwellian digital age where there are seemingly
new worries about government surveillance on an almost weekly basis we find ourselves in a
major legal, financial, and social conundrum. The meteoric rise in the use of the internet and
smart devices controlled by massive satellites postulate a number of queries into the legitimacy
of such advanced technological possibilities. Geographic information systems (GIS) offer us
ways to map destinations digitally, locate missing electronics, and develop comprehensive maps
for a number of discourses. Law enforcement at all levels are driving toward becoming more
technologically sophisticated so as to keep up with modern society, especially a society that can
use such technology for a number of crimes and connections to other criminals. The allocation of
tax money for law enforcement is a concern for every jurisdiction and as a country still
recovering from economic collapse our resources must always be scrutinized. Finally,
surveillance and big data have a number of implications relating to the Fourth Amendment and
while the courts linger on these issues, technology continues to advance and is now offering
ways to predict crimes before they happen.
Policing has evolved over the years from being largely political to more astute and
militaristic to the more current philosophy of community policing. Predicated on the broken
windows (Wilson & Kelling, 1982) and problem-oriented (Braga et al, 1999; Weisburd, Telep,
Hinkle, & Eck, 2010) models of policing, the community model strives through a cooperative
effort by residents businesses, public agencies, and the police to eliminate underlying issues and

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social ills research has shown perpetuates criminal activity (Braga et al, 1999; Skogan, 1990;
Weisburd, Telep, Hinkle, & Eck, 2010; Wilson & Kelling, 1982). The notions presented in these
texts discuss the impact that disorder has on crime and while the impact might not be completely
direct (i.e. actual broken windows may not result in robbery or vagrancy) there does seem to be
reason to believe disorder and incivility cause people to lose informal social control of their
neighborhoods leading them to disorganization (Bursik & Grasmick, 1993). Using this
knowledge police departments have been fighting for years to implement programs that focus on
these issues with some success although that is largely dependent on the willingness of residents
to fight for their neighborhoods and the willingness of governments to allocate tax dollars to
fixing these issues. Often times neighborhoods fall into states of being nearly unrepairable with
large numbers of vacant and condemned buildings coupled with street-level disorders such as
prostitution, drug use, and vandalism (Bursik & Grasmick, 1993; Wilson & Kelling, 1982). The
overall aim with these models is one of prevention through collaborative efforts with the idea
that fighting the source of these issues will solve problems more thoroughly that fighting
symptoms though traditional criminal justice procedures. These models are now being used by
software developer and law enforcement in the form of predictive policing defined by Comacho-
Collados and Liveratore (2014) as “the application of quantitative techniques to foretell where
crimes will take place in the short-term future…taking data from disparate sources, analyzing
them, and then using the results to anticipate, prevent, and respond more effectively to future
crimes” and used this definition in their study of the technical aspect of a program implemented
in Spain that saw some success. The actuarial-predictive model is the latest development in the
fight for crime prevention and the combined use of the vast research on crime and large
databases is revolutionizing how law enforcement operates.

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Technology offers interesting opportunities for crime prevention through the use of
various modes ranging from GIS satellites, crime cameras, red-light cameras, license plate
readers, electronic monitoring (EM) and the like but these all come at two potential costs:
privacy and taxes. Surveillance by the government has long been of concern to people of all
parties yet legislation has typically ruled in favor of the government and the police
(P.A.T.R.I.O.T. Act, United States v. Knox, others). In today’s world we have seemingly come
full circle with the ones dreamed up by fiction writers like Orwell, Huxley, and Rand where we
cannot escape being watched by Big Brother and all the while this watching is done on the tax-
payers dollar. All of these sorts of sources can be thought of under the umbrella term “big data”
which is defined by Joh (2014) as “the application of artificial intelligence to the vast amount of
digitized data now available” and in her article presents a number of key concepts for the
predictive policing model, specifically: place, individual, and surveillance. Her study focused on
New York City’s CompStat program which considers a number of data sources and use them to
help precinct commanders employ their resources. Place is an important factor as crime tends to
occupy smaller geographic areas over certain periods of time and the use of software algorithms
that consider liquor store locations, in-and-out routes of areas, parks, and other spatial variations
offers a view that hinges on the Crime Prevention Through Environmental Design (CPTED)
model (Joh, 2014; Newman, 1972). The role of the individual is calculated through sifting of
social networking sites and accounts of potential or suspected offenders and works in a similar
fashion to the counter-insurgency used by the U.S. Military in the battles in the Middle East; this
allows for law enforcement to study and connect groups of people who may play different roles
in a variety of crimes (Joh, 2014). Finally the collection of all of this data is done by domain
awareness systems (DAS) which takes in data from camera, license plate readers, gunshot

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readers and other types of sensors to develop a map of where this is happening in the city and to
store the information for easy retrieval (Joh, 2014). The amount of data these can collect and
store is astounding and the success New York has seen has encouraged other cities to use similar
methods and has allowed for certain companies to develop software that aims to predict crimes
more specifically that what we have seen used in New York.
Two companies, PredPol and HunchLab are at the forefront of predictive policing
technology and the use of algorithms to predict crime and place officers where they need to be
when they need to be there. PredPol focuses solely on three criteria place, type, and time of
crime and thus is more focused on property crimes which do make up the majority of reported
crimes (PredPol, 2016). It attempts to keep biases out of their model by not including
information on relevant offenders known to the area while allowing veteran officer’s intuition to
play a role in how they use the technology. This model is where CompStat came from and is
known at the Near Repeat model as it uses the place, time, and type criteria to serve as a sort of
educated guess (Koss, 2015). HunchLab, however is the more cutting-edge and risky program. It
uses a wide variety of factors including geographic, seasonal, known offenders, time of day, and
just about any other considerable data point relatable to crime to predict not only what type of
crime and where but even offering suggestions as to whom may be the offender (HunchLab,
2016). It uses two models, the aforementioned Near Repeat model and the Risk Terrain model
which uses GIS technology and compares it with behavioral, social, physical, and environmental
factors to develop predictions (Koss, 2015). The use of this combination is where there is some
potential blowback from those questioning the legitimacy of surveillance and data in relation to
the Fourth Amendment’s “Right to Privacy” clause and to this point the courts have ruled in
favor of the police but have also left this up for future debate as the ever-evolving world of

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technology seems to produce new option and new data (Joh, 2014; Koss, 2015). These
technologies are not without their drawbacks as concerns about human fallibility and the Fourth
Amendment protections against unreasonable searches and seizures show the limits that
programs like PredPol and HunchLab have from both a scientific and legal perspective (Koss,
2015). The ideas that Koss (2015) presents that these technologies could predict a crime down to
the time, person and even exact type (a heroin transaction as opposed to a drug transaction) are
interesting but somewhat flawed in her argument regarding Fourth Amendment rights. These
technologies aim more to place officers where they should be for potential crimes; they do not
tell them whom to stop although HunchLab does offer a service that shows known offenders
living in the area but the police are generally familiar with those types of people from the nature
of their work. Her argument that it could create biases is limited and the police are routinely
checked for profiling, not to mention that the courts have ruled that stop-and-frisk’s are legal and
have been researched to have considerable benefit (Joh, 2014; Koss, 2015). In fact, in a
comprehensive study by Perry, McInnis, Price, Smith, and Hollywood (2013) they highly
recommend a model closer to that of HunchLab that focuses on using spatial, environmental, and
social data for departments to develop crime fighting strategies. The writers also touch on other
key concepts such as cost, implementation, and tailoring the programs to specific departments
and areas with distinct crime issues (Perry et al, 2013). Cost is a particularly interesting
consideration as police departments are a tax funded agency and citizens theoretically would like
to know how their money is being spent. This also plays into the Fourth Amendment argument
as those feeling this violate their rights would likely be quite reluctant to pay for such
technologies that are seemingly in a gray area from the courts perspective. Considering the large

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investment that purchasing, implementing, and maintaining these programs would require it
certainly would not be surprising to see people questioning their legitimacy.
The late twentieth and early twenty-first centuries brought forth a number of major
technological advances from the internet and personal computers to smart phones and drones and
while the vast majority of these used simply for personal enjoyment they have become major
players in the way data is compiled and stored. Governments can use this massive amount of
data to develop policy and to implement programs aimed at efficient use of public services. The
use of big data and technology is not without its concerns though as people expect a certain level
of privacy inside and outside of their homes which can seemingly be compromised by the use of
cameras and massive databases being watched by people who use the information for their
entities needs. This is a format being used not only by criminal justice agencies but also entities
such as Target, Walmart or Amazon (Joh, 2014). The use of such data by law enforcement and
government offers a number of opportunities for efficient police work and quick retrieval of
information when on patrol or even in an investigation. However, they do have some drawbacks
in the form of human fallibility and the potential of violating certain Fourth Amendment rights
that must be considered before implementation. The courts have only limited rulings on this
issue and removing bias from police work in left to the department and individual officers. The
cost of these programs should be scrutinized and they should only be implemented if the cost is
equitable. HunchLab is seemingly the better of the two major programs as it uses both Near
Repeat and Risk Terrain modeling to develop its maps and build a database that also take
department specific data and algorithms into consideration. This program is being used to some
degree of success by the St. Louis County Police Department (St. Louis County Police, 2016).
The use of these programs is a great evolution in broken-windows, problem-oriented, and

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community based policing that will allow the police to run more efficiently and to consider
macro and micro level community problems and enter them into the database for crime control.

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References
Braga, A. A., Weisburd, D. L., Waring, E. J., Mazerolle, L. G., Spelman, W., & Gajewski, F.
(1999). PROBLEM‐ORIENTED POLICING IN VIOLENT CRIME PLACES: A
RANDOMIZED CONTROLLED EXPERIMENT*.Criminology, 37(3), 541-580.
Camacho-Collados M, & Liberatore F. (2015). A decision support system for predictive
police patrolling. Decision Support Systems, 75, 25-37. doi:10.1016/j.dss.2015.04.012
Dolly, C. (2016, May 7). Predictive Policing in St. Louis County [E-mail interview].
Joh, E. E. (2014). Policing by numbers: Big data and the fourth amendment. Washington Law
Review, 89(1), 35
Koss, K. K. (2015). Leveraging predictive policing algorithms to restore fourth amendment
protections in high-crime areas in a post-wardlow world. Chicago-Kent Law Review, 90(1), 301
Newman, O. (1972). Defensible space: Crime prevention through urban design. New York:
Macmillan.
"Next Generation Predictive Policing." Web. 13 May 2016.
Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., Hollywood, J., Rand online collection, . .
. Rand Safety and Justice (Program). (2013). Predictive policing: The role of crime forecasting
in law enforcement operations. Santa Monica, CA: RAND. doi:10.7249/j.ctt4cgdcz
"Predict Crime | Predictive Policing Software | PredPol." Web. 13 May 2016.

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Skogan, W. G. (1990). Disorder and decline: Crime and the Spiral of Decay in American
Neighborhoods. New York :Toronto :New York: Free Press ;Collier Macmillan Canada
;Maxwell Macmillan International.
Weisburd, D., Telep, C. W., Hinkle, J. C., & Eck, J. E. (2010). Is problem‐oriented policing
effective in reducing crime and disorder? Criminology & Public Policy, 9(1), 139-172.
Wilson, J. Q., & Kelling, G. L. (1982). The police and neighborhood safety: Broken
windows. Atlantic monthly, 127(2).

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Course Reflection
I enjoyed this unit and especially the openness that was the final course project. Many of
the courses I have taken limit what can be done for a final project although that is somewhat
expected as they are aimed toward specific material whereas this was a more open exploration of
writing and rhetoric. The time we spent analyzing projects like Freeman and Merskin’s was quite
interesting and I would like to do some similar analysis of crime related programming. Although
we had nearly six weeks to work on this I still felt kind of rushed at the end, although that was
partially my own doing and more related to family obligations that the course structure. In my
opinion, it might have worked better for the previous two units to be part of this one in a build up
to a final project such that the first unit could be working on an annotated bibliography, the
second a thorough literature review, and the final an analysis of the literature/field research done.
However, this was a perfectly fine format and I especially enjoyed the annotated bibliography
portion of it. I will be using this going forward as I found it quite helpful. One of the projects I
am working on in an Independent Study will be greatly helped by this format.
I feel like my performance in this course was quite frankly lacking from that of previous
courses and I attribute this to the aforementioned family obligations and partially to being
unfamiliar with literacy narratives and more intensive critical analysis using naysayers and the

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like. This course has open my eyes for future rhetorical work and I will be taking this with me as
I go forward. The ideas presented of being more open-minded with rhetoric and developing a
voice is a nice break from the seemingly cookie-cutter ideas presented in other writing based
courses. I have also reserved a spot on my desk for the Graff book as his simplistic way of
outlining writing strategies was also interesting and helpful and I will be using them in courses
going forward. Thank you for your time this semester Mr. Kimbrell and despite my words here, I
will take a lot of this course with me going forward.