Safer College Campuses and Communities Through the Use of Geospatial Information

Technology

George Roedl and Gregory ElmesWest Virginia University

Quick Facts U.S. college campuses report ~90,000 crimes 98% crimes are property crimes (non-violent) 93% crimes against college students occur off-

campus Campuses have lower crime rates than

surrounding communities No consensus on what community factors

affect campus crime or student victimization

Increasing Student and Community Safety partnership

1. Establish crime mapping and analysis capability across multiple jurisdictions to support providing a safer campus and communities;

2. Utilize geospatial technologies to provide spatial and non-spatial information for problem oriented, intelligence-led decision making and resource allocation by law enforcement agencies and policy makers;

3. Enhance community crime reduction participation through increased awareness and information dissemination with a multi-faceted combination of in-person and online solutions, including an interactive map server;

4. Promotes coordination and cooperation among additional law enforcement agencies in the same area and with other researchers.

Study AreaMorgantown WV29,600 2010 Census~100,000 county

West Virginia University37,699 2010 Enrollment + Staff

Local LEAs

Top reported offenses: 2010 & 2011

Categories of InterestCampus Disclosure Act (Clery Report)ArsonAssault/Battery•Aggravated, simple, intimidationBurglaryDestruction/VandalismForgeryMurder•Negligent, non-negligent, justifiedRobberySexual AssaultTheft•Pick-pocket, purse snatching, shoplifting, from building, from vehicle, from coin operated device, vehicle parts, all othersVehicle Theft

MPD 2010 MPD 2011 WVUPD 2010 WVUPD 2011All Incidents 3279 2916 1324 1686Categories of Interest 2007 1787 400 468

61.21% 61.28% 30.21% 27.76%

Categories of Interest

Analyses of Discussion

1) Temporal Trend Analysis2) Spatial Trend Analysis3) Spatio-temporal Analysis4) Spatial Regression Analysis

By Month and Day

High degree of similarity between WVU and Morgantown which varies slightly each year

By Time of Day

Locations with many incidentsMPD 2010 # MPD 2011 #

300 SPRUCE STREET 48 215 HORNBECK ROAD 35215 HORNBECK ROAD 47 1851 EARL CORE ROAD 221851 EARL CORE ROAD 24 1 STADIUM DRIVE 209901 HIGH STREET 21 471 CHESTNUT STREET 191157 VAN VOORHIS ROAD 20 300 SPRUCE STREET 171345 UNIVERSITY AVENUE 20 1157 VAN VOORHIS ROAD 141 STADIUM DRIVE 18 9901 GRANT AVENUE 13235 JONES AVENUE 16 350 PATTESON DRIVE 12109 WILSON AVENUE 15 109 WILSON AVENUE 11444 CHESTNUT STREET 14 51 FALLING RUN ROAD 11

12.11% 75 WALL STREET 1110.35%

WVUPD 2010 # WVUPD 2011 #MOUNTAINLAIR 40 STUDENT RECREATION CENTER 33STUDENT RECREATION CENTER 28 HEALTH SCIENCES CENTER 27BOREMAN HALL SOUTH 22 COLISEUM 23HEALTH SCIENCES CENTER 20 MOUNTAINLAIR 22COLISEUM 17 ARNOLD HALL 19STADIUM 17 BOREMAN HALL SOUTH 17660 N HIGH STREET 15 SUMMIT HALL 17BROOKE TOWER 15 VANDALIA HALL (BLUE) 17MOUNTAINLAIR PARKING GARAGE 11 WISE LIBRARY 16BOREMAN HALL NORTH 9 DADISMAN HALL 15DADISMAN HALL 9 44.02%

50.75%

Spatial Characteristics of CrimeUnder certain conditions, times or locations, crimes exceed the average or expected rates to form distinct clusters known as hot spots

Crime can be characterized as having random, dispersed, or clustered patterns

There are a variety of ways to measure intensity of crime incidents to identify hot spots. One method is Kernel Density (KD)

Spatio-temporal Analysis

Some crime incidents are likely to happen within defined spatial and temporal proximity of previous incidents

Most studies focus on a single LEA jurisdiction

Knox Space-Time Interaction Test

Close in space, not time

Close in time, not space

Close in space & time

Not close in space or time

Examples

2011-9-26 12:15 2011-101182 Theft 23D GRAND LARCENY (FROM VEHICLE) (=>$1000) 116 BLUEGRASS VILLAGE2011-10-13 08:41 2011-106993 Theft 23F PETIT LARCENY (THEFT FROM MOTOR VEHICLE) 102 BLUEGRASS VILLAGE2011-10-24 14:39 2011-110681 Theft 23F PETIT LARCENY (THEFT FROM MOTOR VEHICLE) 124 BLUEGRASS VILLAGE2011-10-31 10:55 2011-112973 Theft 23F PETIT LARCENY (THEFT FROM MOTOR VEHICLE) 110 BLUEGRASS VILLAGE

2010-12-17 18:33 2010-118740 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 51 FALLING RUN ROAD2010-12-28 02:10 2010-121515 Burglary 220 61-3-11(A) BURGLARY DAYTIME, BREAK & ENTER 51 FALLING RUN ROAD2011-1-2 17:25 2011-543 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 51 FALLING RUN ROAD2011-1-6 22:04 2011-2171 Burglary 220 61-3-11(A) BURGLARY DAYTIME, BREAK & ENTER 51 FALLING RUN ROAD2011-1-8 17:35 2011-2778 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 51 FALLING RUN ROAD2011-1-8 22:59 2011-2871 Burglary 220 61-3-12 ENTERING WITHOUT BREAKING 51 FALLING RUN ROAD2011-1-9 17:29 2011-3101 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 15 FALLING RUN ROAD2011-1-9 17:29 2011-3101 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 15 FALLING RUN ROAD2011-1-9 22:24 2011-3165A Burglary 220 61-3-11(A) BURGLARY DAYTIME, BREAK & ENTER 49 FALLING RUN ROAD2011-1-10 16:00 2011-3372 Burglary 220 61-3-11(A) BURGLARY DAYTIME, BREAK & ENTER 51 FALLING RUN ROAD2011-1-10 16:00 2011-3372 Burglary 220 61-3-11(A) BURGLARY NIGHTIME 51 FALLING RUN ROAD

Burglaries close in space and time

Theft from vehicles close in space and time

Knox Test Results MPD (Knox) WVUPD (Knox)

MPD & WVUPD (Knox)

MPD & WVUPD

(Summed)

MPD & WVUPD

(Difference)

Incident Type

n= Observed/ Expected

n= Observed/ Expected

n= Observed/ Expected

n= MPD + WVUPD Observed

Knox minus Summed

All Types 1874 305/177.55 * 602 100/81.49 * 2476 435/271.77 * 2476 405 30

Arson 5 0/0.00 3 0/0.00 8 0/0.00 8 0 0

Assault 360 22/12.75 * 116 8/5.98 476 31/19.49 * 476 25 1

Burglary 287 20/3.29 * 12 0/0.03 299 20/3.31 * 299 20 0

Destruction 537 39/21.40 * 173 11/7.50 * 710 55/31.56 * 710 50 2

Robbery 35 0/0.06 7 0/0.00 42 0/0.07 42 0 0

Sexual 26 0/0.02 10 0/0.13 36 0/0.08 36 0 0

Theft 756 58/30.44 * 283 24/18.75 1039 87/50.58 * 1039 82 5

Veh.Theft 45 0/0.05 1 N/A 46 0/0.05 46 0 0

* p<0.0001

Examining potential causes of crime LISA Clusters

98 High/High20 Low/Low20 High/Low

Threshold p=0.05

925 total census blocks

Spatial regression analysis used to identify possible associations between spatially clustered census blocks and demographic predictor variables

Findings & Implications (short list)1. Incidents follow university trends

• Planning and resource allocation should consider activities and extra population

• JOINTLY!!2. Large number of cross-jurisdictional hotspots

• Student safety is more than Clery statistics3. Demographics/places not very good predictors

• Opportunity & Alcohol4. Supports previous reports of campus low/declining crime rates relative to

community5. Gap in the literature when it comes to addressing student safety off campus

• GIS a valuable tool for exploring gaps• Very little done where college students are considered victims despite the

statistically significant difference between crime on and off campus involving students

6. Potential to influence decision makers at colleges and within college town • Empirical demonstration and case studies

Thank You,Questions?

This project is supported by Award No. 2009-IJ-CX-0205 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice.The opinions, findings, and conclusions or recommendations expressed in this presentation are those of the authors and do not necessarily reflect those of the Department of Justice.

Forthcoming:Elmes and Roedl (In-press). The Use of Geospatial Information Technology to Advance Safer Campus Communities. In: Leitner M (ed.) Crime Modeling and Mapping Using Geospatial Technologies. Springer New York.