1. Risk Terrain Modeling: A Tool for Crime Prevention & Reduction in New York City?
Michelle Thompson
GEOG 596a Capstone Proposal
Spring 2017 Session 2
MGIS Candidate
Pennsylvania State University - World campus

2. Introduction & Purpose of the Study Methodology Expected Results
Roadmap
Introduction & Purpose of the Study
Literature Review
Methodology
Expected Results
Projected Timeline

3. Introduction – why Nyc & why now?
In 2015 the NYPD released its crime complaint data geocoded at the street level in a user-friendly format on NYC Open Data
Overlay crime, environmental, and sociodemographic data
Study of NYC, the largest city (by population) in America, adds to the literature
Making data accessible by and understandable for the public

4. Introduction – overall purpose
Explore the validity of risk terrain modeling- based policing in New York City's Bronx, Kings, Queens, and New York counties.

5. Risk Terrain modeling
Risk Terrain Modeling, “an approach to spatial risk analysis...is used to identify risks that come from features of a landscape and model how they co-locate to create unique behavior settings for crime” (Kennedy & Caplan, Rutgers University). 

6. Relevant Theoretical background
Social Disorganization Theory: lack of bonding in community leads to lack of concern about crimes occurring in the area; high residential turnover – who should be here vs who shouldn't be here?
Routine Activities: everyone has their daily routine activities and in the course of those activities, the paths of offenders and victims do cross.
Crime Pattern Theory: majority of crimes occur in a minority of areas. There is a pattern to crime occurrences, and at certain times and in certain places the co-location of necessary crime elements are more likely to occur.

7. Research results = basis for a "risk-based" approach to policing
 lit review Summary
Previous use of RTM include: Newark, NJ; Kansas City, MO; and Chicago, IL 
Environmental and sociodemographic factors influence future risk of the occurrence of certain types of criminal events
Research results = basis for a "risk-based" approach to policing 
Several factors associated with the increased likelihood for each crime, but specific factors vary by crime type.

8. Aggravated assault (felony assault) risk factors
Liquor Stores – alcohol consumption -> poor decision- making, spatial influence of 864 feet 
Bus stops – act as public transportation hubs, allowing access to and escape from crime scene, spatial influence of 1728 feet
Locations of public High schools – presence of vulnerable targets and potential offenders whose decision-making and self-control skills not fully developed, spatial influence of 216 feet

9. burglary risk factors
Public Housing Developments – the physical embodiment of the social disorganization concept, spatial influence 300ft
Pawn Shops – represent potential places to offload stolen goods, spatial influence between 300 and 900ft
Bus stops – act as public transportation hubs, allowing access to and escape from crime scene, spatial influence between 300 and 900ft

10. Robbery risk factors
Night Clubs – the physical embodiment of the social disorganization concept, spatial influence of 462ft
Drug Dealing locations – street robbery can finance the purchase of drugs, spatial influence of 924ft
Banks – vulnerable targets leaving banks may provide ample opportunities for robbery, spatial influence 1386ft 

11. Methodology – Gathering the data
Crime Data
2016 aggravated assaults, burglaries, and robberies data will be filtered out from the NYPD Complaint Data Current YTD dataset. 
These points will then be geocoded to create the three 2016 selected crime files (one point dataset for each type of crime)
Bus Stops 
ArcMap's Merge geoprocessing tool will be used to create a study area bus stop point shapefile from five bus stop datasets found on NYU'S Spatial Data Repository

12. Gathering the data (continued)
Additional Risk Factor Data
Pawn shops, night clubs, banks, liquor stores  - Legally Operating Businesses and RefUSA
Public High Schools – School Point Locations (updated 2014)
Public Housing Locations – Map of NYCHA Developments
Drug Dealing Areas – Data Source TBD

13. Methodology – gis tools and applications
GIS Tools and Applications: ArcMap
Vector Geoprocessing
Adding data to ArcMap & Setting up the processing environment
Geocoding point datasets and merging necessary features
Buffering

14. Methodology – gis tools and applications
GIS Tools and Applications: ArcMap
Raster Analysis
Converting buffer vector files to raster files
Using Raster Calculator to create the overall risk terrain surface
Converting Raster layer back to vector shapefiles

15. Methodology – gis tools and applications
GIS Tools and Applications: ArcMap
Combining Vector Risk Terrain & Crime Data
Spatial Join of crime data to risk area polygons, gives a count of the amount of crimes that occurred in each risk polygon
Creating crime rate for each risk area polygon, using the crime count divided by the risk polygon area

16. Methodology – statistical analysis
SPSS: Will be used to conduct an Independent Samples T Test
GridCode: Risk Grouping Variable
Test Variable: Crime Rates

17. RTM will be an applicable tool for NYC
Expected Results
RTM will be an applicable tool for NYC 
Burglaries, robberies, and aggravated assaults (felony assaults) will occur in areas deemed "Risky" by the RTM statistically more often than in the "non risky" areas

18. Project Timeline May 2017: Data Collection
Obtain crime data and study area boundary polygons
Download 2016 Crime Data
Create 4 borough study area polygon shapefile from NYC Borough Boundaries shapefile
Obtain Risk Factor Data
Download, filter, and clean risk data for later geocoding
Geocode risk factor data
Compile prepared GIS datasets into a geodatabase for analysis in ArcMap

19. Project Timeline June 2017: Vector Geoprocessing
Merge bus files together to create master bus file
Geocode public high school locations
Geocode banks locations
Geocode pawn shop locations
Geocode drug dealing locations
Geocode night club venues
Create spatial influence buffers
Columbia University provides a LION address locator for NYC streets, updated in 2015

20. Project Timeline July 2017: Raster Processing & Risk Layer Analysis
Converting buffer vector files to raster files 
Using Raster Calculator to create the risk terrain surfaces 
Converting Raster layer back to vector shapefiles
Combining Vector Risk Terrain & Crime Data 
Spatial Join of crime data to risk area polygons, gives a count of the amount of crimes that occurred in each risk polygon 
Creating crime rate for each risk area polygon, using the crime count divided by the risk polygon area 

21. Project timeline
August 2017: Statistical Analysis 
SPSS: Will be used to conduct an Independent Samples T Test 
GridCode: Risk Grouping Variable 
Test Variable: Crime Rate
Evaluate results using Levene's Test for Equality

22. Project Timeline September & October 2017: Final Preparation
Revisions and edits after advisor and peer feedback
November 2017: Conference Presentation
Present at The American Society of Criminology Annual Meeting
Theme: Crime, Legitimacy and Reform: Fifty Years after the President's Commission
November 15-18, 2017, Philadelphia PA
December 2017: Final Capstone Paper Submission

23. Questions/Comments/Concerns?
Thank you
Questions/Comments/Concerns?

24. references
Caplan et al. 2014 
Caplan and Kennedy, n.d. Risk Terrain Modeling Compendium  
Caplan and Kennedy 2012 A theory of risky places. Retrieved from   
Drucker, J. (2011, Mar). Risk factors of aggravated assault. RTM Insights 
Drawve, G. and Barnum, J. D. (2017). Place-based risk factors for aggravated assault across police divisions in Little Rock, Arkansas. Journal of Crime and Justice.
Eversley, M. (2017, Jan. 4) NYC sees historic drop in crime. Retrieved from 
Gaziarifoglu, Y. (2010, October). Risk factors of street robbery. Retrieved from 
Kennedy, L. W. (2015, October). Crime prediction using risk terrain modeling: Thinking spatially about crime and behavior settings. Retrieved from  intl.org/fileadmin/user_upload/Evenements/Observatory_meeting_2015/Les_Kennedy.pdf

25. References continued
Moreto, W. D. (2010). Applying risk terrain modeling to urban residential burglary in Newark, NJ. Retrieved from 
NYPD (n.d.) Seven major felony offenses. Retrieved from  .pdf 
Pollak, M. (2006, Sept. 17). Knowing the distance. Retrieved from 
Rutgers Center on Public Security. (2014). Risk terrain modeling: A case study of robbery in Kansas City, MO. Retrieved from 
Sytsma, V. (2011, May). A pilot application of Risk Terrain Modeling: Aggravated assault in Newark, NJ. Retrieved from 
Toomey, M. and Kennedy, L.W. (2011, Apr. 29). An analysis of modern early warning systems: How might Risk-Terrain Modeling contribute to the development of an optimal system? Retrieved from 
Weisburd, D., Groff, E. R., and Yang, S. (2014). The importance of both opportunity and social disorganization theory in a future research agenda to advance criminological theory and crime prevention at places. The Journal of Research in Crime and Delinquency, 5(4),