A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting

Date01 June 2020
Published date01 June 2020
Subject MatterArticles
A Theory-Driven
Algorithm for
Real-Time Crime
Hot Spot Forecasting
YongJei Lee
, SooHyun O
, and
John E. Eck
Real-time crime hot spot forecasting presents challenges to policing. There is a high
volume of hot spot misclassifications and a lack of theoretical support for forecasting
algorithms, especially in disciplines outside the fields of criminology and criminal
justice. Transparency is particularly important as most hot spot forecasting models
do not provide their underlying mechanisms. To address these challenges, we oper-
ationalize two different theories in our algorithm to forecast crime hot spots over
Portland and Cincinnati. First, we use a population heterogeneity framework to find
places that are consistent hot spots. Second, we use a state dependence model of
the number of crimes in the time periods prior to the predicted month. This algo-
rithm is implemented in Excel, making it extremely simple to apply and completely
transparent. Our forecasting models show high accuracy and high efficiency in hot
spot forecasting in both Portland and Cincinnati context. We suggest previously
developed hot spot forecasting models need to be reconsidered.
crime hot spot, forecasting, population heterogeneity, state dependence, Excel
School of Public Affairs, University of Colorado, Colorado Springs, CO, USA
School of Criminology, Criminal Justice and Strategic Studies, Tarleton State University, RELLIS Campus,
School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA
Corresponding Author:
YongJei Lee, School of Public Affairs, University of Colorado, Colorado Springs, CO 80918, USA.
Email: ylee@uccs.edu
Police Quarterly
2020, Vol. 23(2) 174–201
!The Author(s) 2019
Article reuse guidelines:
DOI: 10.1177/1098611119887809
With the advent of computer mapping and geographic information systems,
real-time crime hot spot forecasting has become important to policing
(Bowers, Johnson, & Pease, 2004; Gorr & Olligschlaeger, 2002; Groff &
La Vigne, 2002). Nevertheless, crime hot spot forecasting presents several
challenges. There is a high volume of crime hot spot misclassif‌ications and a
lack of theoretical support for existing forecasting algorithms, especially in dis-
ciplines outside the f‌ields of criminology and criminal justice (Gorr & Lee, 2012,
2015). In addition, many algorithms are complex and proprietary. Thus, they
require expensive contracts with private vendors and lack transparency
(Ferguson, 2012).
Transparency is particularly important when police use of stop-question-frisk
tactics in hot spots: A tactic linked to both racially disparate treatment of non-
Whites and excessive interference in the daily lives of people who are not
involved in crime (Sullivan & O’Keeffe, 2017). Recently, New York, Chicago,
and Los Angeles police departments were sued for not releasing information
about the algorithms used by their predictive policing programs (Collins, 2018;
Rieland, 2018). Therefore, transparency is just as important a criterion for eval-
uating hot spot forecasting models as predictive eff‌iciency and accuracy.
To respond to these challenges, we create a forecasting algorithm based on
two models suggested by scholars in the f‌ield of predictive policing (Bowers
et al., 2004; Johnson, 2008). We use a population heterogeneity model to f‌ind
places that are consistently experiencing crimes in the forecasted month. This
narrows our focus to places with consistently high levels of crime. Second, we
apply a state dependence model to account for short-term elevations in risk at
these places. We implement this combination of models in Microsoft Excel,
making it extremely simple to apply and completely transparent. It does not
need highly specialized expertise to implement but can be modif‌ied by agencies
as needed. Importantly, people outside of policing can examine how it works,
should they be concerned that it produces undesired outcomes. In short, it is
completely transparent.
Our forecasting models show high eff‌iciency and accuracy in hot spot fore-
casting relative to existing hot spot forecasting models. These results demon-
strate how basic theories could lead researchers and practitioners to build a
sound algorithm for hot spot forecasting. After we review a brief history and
utility of the past crime hot spot forecasting models in the following section, we
describe the two theoretical frameworks, both population heterogeneity and
state dependence in more detail. We also explain how we operationalize both
models in the methods section. Followed by the results section, we f‌inally discuss
the implications of this simple, theory-driven forecasting method for policing
practices and its limitations as well.
Lee et al. 175

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT