This document summarizes a crime mapping and analysis project conducted for the Georgia Tech Police Department. The objectives were to map crime incidents from 2010-2015, identify crime hot spots, and direct police resources. Crime data was cleaned, geocoded and analyzed in ArcGIS. Point density analysis identified the most crime-heavy grids, with the area around Student Center, Ferst Drive, and North Avenue Apartments among the highest. The analysis can help GTPD better deploy patrols and resources to reduce crime in these locations.

1. Crime Mapping & Analysis –
Georgia Tech
Jonathan D’Cruz

2. 2
Introduction
• Crime analysis is a law enforcement
function that involves systematic analysis for
identifying and analyzing patterns and trends
in crime and disorder. Information on
patterns can help law enforcement agencies
deploy resources in a more effective manner,
and assist detectives in identifying and
apprehending suspects.
Source: http://www.esri.com/library/brochures/pdfs/crime-analysis.pdf

3. 3
Background
• The mission of the Georgia Tech Police
Department (GTPD) is listed below:
 To strive for excellence and integrity in providing a safe and
secure environment and instilling confidence in the Georgia Tech
community.
 To enforce laws and campus policy, to serve the public, to prevent
and detect criminal activity, and to reduce the fears of the public
through community interaction and education.
 To promote growth and development for the members of the
Department.
Source: GTPD Website

4. 4
The “Problem”
• Crime Mapping is dedicated to helping law
enforcement agencies provide the public with
valuable information about recent crime
activity by neighborhood.
• It is used by analysts in law enforcement
agencies to map, visualize, and analyze
crime incident patterns.
• Mapping crime, using Geographic
Information Systems (GIS), allows crime
analysts to identify crime hot spots, along with
other trends and patterns.
Source: http://www.esri.com/library/brochures/pdfs/crime-analysis.pdf

5. 5
Objectives
• To map incidents of crime in and around
Georgia tech for the period 2010-2015 (To
date)
• To identify crime hot spots and other patterns
and trends to aid in the efficient deployment
of police resources and maximize effectiveness
• To identify locations and neighborhoods
where major crimes occur (Rape, Sexual
Assault, Battery, etc.) and the incidents of
criminal activity are high in order to direct
police patrols and presence in those affected
areas

6. 6
Study Area
• The study area includes areas in and around Georgia
Tech which fall under the jurisdiction of the GTPD
• A map with a grid which represents the area under
study is provided below (red outline marks GT campus)
• Each grid has a size 0f 1000 by 1000 feet

7. 7
Methods And Procedures
• The data was first downloaded, cleaned, geocoded and
imported into ArcGIS
• The data was then projected to NAD 1983 StatePlane
Georgia West FIPS 1002 Feet to minimize distortions
• A fishnet was created to provide a frame of reference for the
data
• A separate table containing crime categorization data was
created and joined to the crime data points table using a
simple join
• The crime data points were then thematically mapped
based on the category of the crime (Major versus Minor), data
was also categorized using select by attributes
• A point density analysis was then run to identify crime hot
spots
• A spatial join between the fishnet and crime data points was
carried out to identify grids with the highest incidents of
crime and a summarized table was create

8. 8
Project Flowchart
Download
Data
Project Data
Create
Fishnet
Polygon
Thematically
Map data
points based
on
classification
of crime
Tabular Join
(crime data
points and
crime
classification
table)
Spatial
Summarized
Join crime
data points
and Fishnet
Clean
Data
Geocode
Data
Import Data
into ArcGIS
Point Density
Analysis
(Major and
all crimes)
Analysis

9. 9
Data Used
• The data was downloaded from the GTPD website
• The link is given below
http://www.police.gatech.edu/crimeinfo/crimelogs/
• The data included incident reports for the years from 2010 to
2015 (to date)
• The data set contained the following data
• Incident Date
• Incident Time
• Offense Description
• Case Status
• Patrol Zone
• Address
• Longitude Co-ordinates
• Latitude Co-ordinates

10. 10
Data Used
• There are 47 Categories of crime
in the data set
• The top 10 most reported crimes
are shown in the pie chart to the
right
• Due to the various classifications
of crimes we have classified them
broadly as major and minor
crimes
• Minor Crimes include
miscellaneous offences, non-
crime, etc.
• Major Crimes include Battery,
Sexual Assault, Rape

11. Data Used
11

12. 12
Assumptions/Limitations
• All crime incident reports were treated equally for the first
point density analysis which is not true in real life.
• The type of crime should be allocated weightages for a more
accurate view of the crime map
• The data is representative of the period 2010-2015 and as
such data from previous years would improve the analysis
• That being said crime mapping needs to be conducted
periodically (as close to real time) as the map may change
with time as patrol routes, population demographics and
developments (housing, offices, police station location etc.)
change with time
• This is an aggregated analysis over the past few years. This
can further be broken down into annual and monthly crime
maps.
• For the purpose of simplicity we have conducted an
aggregated analysis
• The grid size of the fishnet is 1000 x 1000 Feet

13. 13

14. 14

15. 15

16. 16

17. 17

18. 18

19. 19
Results
• The grid cells listed have more than 100
reported crime incidents (major & minor) and
as such patrolling and police presence in
these areas should be increased
• The grid encompassing the Student Center,
Ferst Drive & 6th
Street NW, Fowler and 10th
Street, 4th
Street & Fowler and North Avenue
Apartments reported the most crime.
• This may be due to a higher number of
students populating a certain grid for housing
or recreational purposes. Increasing police
presence, surveillance and establishing more
emergency call boxes may improve the
situation and reduce the number of crimes
occurring in the specified grid
Label All Major Minor
H5 771 29 742
E7 721 39 682
F4 713 23 690
I7 693 48 645
G7 689 51 638
G8 632 35 597
G4 502 15 487
H7 408 14 394
F7 389 20 369
G6 355 12 343
F6 351 12 339
F3 339 14 325
E3 303 5 298
H6 279 9 270
E5 253 5 248
E4 249 9 240
H8 218 7 211
D3 215 5 210
E6 165 7 158
G5 152 4 148
H4 151 1 150
D6 148 2 146
I8 144 3 141
E8 142 2 140
G3 116 4 112

20. 20
Discussion/Recommendations
• We expected to see more reports of crime in and around
Home Park. However, we found few incidents of crime in
those locations not neglecting miscellaneous offenses and
traffic violations
• The data obtained from GTPD was improperly formatted and
required cleaning to a high degree
• The PIN code of the area in which the crime is reported
should be included in the incident report as it makes
geocoding much easier
• The data collated for analysis represents an aggregated data
set (2010-2015). However, implementing a temporal analysis
(not included in project) displaying the changing point
density or grid based crime density can certainly increases the
effectiveness of the model

21. 21
Discussion/Recommendations
• Creation of a mobile police outpost in grid G7 or H7 may help reduce crime.
• Increase police patrols and presence in cells E7, F4, G4, G7, G8, H5 and I7.
The streets to be patrolled to reduce incidents have been identified as the
following:
• 10thStreet NW and Fowler Street NW (E7)
• McMillian Street NW and Hemphill Avenue NW (F4)
• Ferst Drive NW (G4)
• 4thStreet NW, 5thStreet NW, Brittain Drive NW and Techwood Drive
NW (G7)
• Spring Street NW and 5thStreet NW (G8)
• State Street NW and Tech Parkway NW (H5)
• US 278 and Centennial Way NW (I7)
• Increase CCTV surveillance in cells E7, F4, G4, G7, G8, H5 and I7.
• Revisit data entry best management practices with all department personnel
in order to decrease the number of incorrect data entries.
• Conduct an additional temporal crime analysis to provide the department
with a more complete picture of where and when crime is occurring.

22. Thank You!
22