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

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

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

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

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). 

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.

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.

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

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

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 

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

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

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

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

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

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

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

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

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

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 

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

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

Questions/Comments/Concerns? Thank you Questions/Comments/Concerns?

references Caplan et al. 2014 https://crimemapping.info/article/risk-terrain-modeling-strategic-tactical-action/   Caplan and Kennedy, n.d. Risk Terrain Modeling Compendium   Caplan and Kennedy 2012 A theory of risky places. Retrieved from   http://www.rutgerscps.org/uploads/2/7/3/7/27370595/risktheorybrief_web.pdf   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 https://www.usatoday.com/story/news/2017/01/04/nyc-sees-historic-drop-crime/96179104/  Gaziarifoglu, Y. (2010, October). Risk factors of street robbery. Retrieved from http://www.rutgerscps.org/uploads/2/7/3/7/27370595/robberyrisks.pdf Kennedy, L. W. (2015, October). Crime prediction using risk terrain modeling: Thinking spatially about crime and behavior settings. Retrieved from http://www.crime-prevention- intl.org/fileadmin/user_upload/Evenements/Observatory_meeting_2015/Les_Kennedy.pdf

References continued Moreto, W. D. (2010). Applying risk terrain modeling to urban residential burglary in Newark, NJ. Retrieved from http://www.rutgerscps.org/uploads/2/7/3/7/27370595/burglaryrtm_casestudy_brief.pdf  NYPD (n.d.) Seven major felony offenses. Retrieved from http://www.nyc.gov/html/nypd/downloads/pdf/analysis_and_planning/seven_major_felony_offenses_2000_2015 .pdf  Pollak, M. (2006, Sept. 17). Knowing the distance. Retrieved from http://www.nytimes.com/2006/09/17/nyregion/thecity/17fyi.html?_r=0  Rutgers Center on Public Security. (2014). Risk terrain modeling: A case study of robbery in Kansas City, MO. Retrieved from http://www.riskterrainmodeling.com/uploads/2/6/2/0/26205659/kcpd_robberyrtmbrief.pdf Sytsma, V. (2011, May). A pilot application of Risk Terrain Modeling: Aggravated assault in Newark, NJ. Retrieved from http://www.rutgerscps.org/uploads/2/7/3/7/27370595/aggassaultrtm_casestudy_brief.pdf 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 http://www.rutgerscps.org/uploads/2/7/3/7/27370595/earlywarningsystems_workingpaper.pdf 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), 499-508.