1. Reallocating Patrol Zones Based on Previous
Crime Patterns
Kyung Kim
December 10, 2015
Abstract
This report will cover the application of the PPAC model on Georgia Tech Police
Department (GTPD) crime records from 2011-2014. In Section I, we will talk about
our motivation to do this research. In Section II, we will mathematically introduce,
in detail, how we cleaned up the data and formulated the PPAC model. In Section
III, we will present our results obtained by the application of the PPAC model. Fi-
nally in Section IV, we will entertain the idea of moving forward with our research by
introducing future directions.
1 Introduction
It is time for high school students to apply to colleges, and campus safety is one aspect
that should not be overlooked. Although Georgia Tech is one of the safest schools, to further
relieve students from having these types of concerns, we worked with the Georgia Tech Police
Department to come up with a more efficient way of allocating police officers. We measured
efficiency as how close police officers were to the crime location. In our semester-long project,
we cleaned up data, statistically analyzed them, and came up with an optimization model
that gives patrol location suggestions.
2 Methods – The PPAC Model
2.1 Data Cleaning
To be efficient in our data analysis, we had to process our data. First, we merged many
csv files with different information into one master file. Then, we read that master file with
R, converted its data type to dataframe and removed all null values. This resulted in a
significant number of data point loss, but we figured that only chose to go with quality over
quantity because we still had more than enough data. Then, we used built-in functions to
perform easy, but important statistical test to finalize our dataset. The resulting csv file
contained information (location, type, etc) of data-wise significant crime points.

2. 2.2 Model Assumptions
The PPAC Model may be applied to data only if this assumption is followed: an acceptable
level of service from the police patrol units has been agreed upon relative to the desired level
of citizen safety[1]
.
2.3 General formulation
Our goal is to maximize the coverage of police officers. We will set a boundary which one
police officer can cover, and we will try to cover as many areas as possible with limited
number of police officers. On top of that, we would like to determine hot spots (areas of
concentrated crime) and prioritize, or, if possible, put more police offers to guard that area.
Mathematically, this can be represented as follows:
max
i∈I
aiyi
s.t.
j∈N
xi ≤ yi∀i ∈ I
xj ∈ {0, 1}
yi ∈ {0, 1}
2.4 Applied formulation
We used Python to write a program that can run this optimization function. After we ran
this code, we represented centers of patrol circles with dots. We present to you a sample of
our code.

3. 3 Results & Analysis
We checked for optimality, and it turns out that our optimization model was able to generate
optimal patrol zones.
Further, we plotted these points, and we observed that we should re-zone Georgia Tech police
zones so that each zone has similar number of police officers patrolling at any given time.
That way, we can ensure better distribution of work force, and thus can increase our chance
of spotting crimes.

4. 4 FUTURE WORK
Now that we were able to locate optimal patrol zones, we can work on re-zoning Georgia
Tech police patrol zones. Another problem to work on would be locating optimal patrol
zones depending on time. To do so, however, we will need a more comprehensive dataset. If
we have such a dataset, it would not be hard to repeat our work because we can simply add
time series analysis in our studies.
ACKNOWLEDGMENT
This work is supported by the PURA program, Georgia Tech Police Department, Atlanta
Police Department, and The H. Milton Stewart School of Industrial Engineering. I would
like to thank my faculty advisor David Goldberg, who was extremely supportive. Further,
I would like to thank my teammates Bingyi Bao, Hojin Lee, Davd Wang, and Yuanheng
Wang for helping me get through the work.
References
[1] K. M. Curtin et al., Integrating GIS and Maximal Covering Models to Determine Op-
timal Police Patrol Areas