Crime Risk Forecasting: Near Repeat Pattern Analysis & Load Forecasting

340 N 12th St, Suite 402Philadelphia, PA 19107

215.925.2600info@azavea.com

www.azavea.com/hunchlab

Crime Risk Forecasting

Near Repeat Pattern Analysis and Load Forecasting

About Us

Robert CheethamPresident & CEOcheetham@azavea.com215.701.7713

Jeremy HeffnerHunchLab Product Managerjheffner@azavea.com215.701.7712

Agenda

• Company Background• HunchLab

– Risk Forecasting• Near Repeat Pattern Analysis• Load Forecasting

– Future Research Topics

• Q&A

About Azavea

• Founded in 2000

• 25 people

• Based in Philadelphia

– Boston satellite office

• Geospatial + web + mobile

– Software development

– Spatial analysis services

Clients & Industries

• Public Safety• Municipal Services• Public Health• Human Services• Culture • Elections & Politics• Land Conservation• Economic Development

Azavea & Governments

HunchLab

web-based crime analysis, early warning, and risk forecasting

Crime Analysis

– Mapping (spatial / temporal densities)

– Trending

– Intelligence Dashboard

Early Warning

– Statistical & Threshold-based Hunches (data mining)

– Alerting

Risk Forecasting

– Near Repeat Pattern

– Load Forecasting

Near Repeat Pattern Analysis

Contagious Crime?

• Near repeat pattern analysis • “If one burglary occurs, how does the risk change nearby?”

What Do We Mean By Near Repeat?

• Repeat victimization– Incident at the same location at a later time (likely

related)

• Near repeat victimization– Incident at a nearby location at a later time (likely

related)

• Incident A (place, time) --> Incident B (place, time)

Near Repeat Pattern Analysis

• The goal:– Quantify short term risk due to near-repeat victimization

• “If one burglary occurs, how does the risk of burglary for the neighbors change?”

• What we know:– Incident A (place, time) --> Incident B (place, time)

• Distance between A and B• Timeframe between A and B

• What we need to know:– What distances/timeframes are not simply random?

Near Repeat Pattern Analysis

• The process– Observe the pattern in historic data– Simulate the pattern in randomized historic data– Compare the observed pattern to the simulated patterns– Apply the non-random pattern to new incidents

• An example– 180 days of burglaries in Division 6 of Philadelphia

Near Repeat Pattern Analysis

Near Repeat Pattern Analysis

Near Repeat Pattern Analysis

Near Repeat Pattern Analysis

Near Repeat Pattern Analysis

• How can you test your own data?– Near Repeat Calculator

• http://www.temple.edu/cj/misc/nr/

• Papers– Near-Repeat Patterns in Philadelphia Shootings (2008)

• One city block & two weeks after one shooting– 33% increase in likelihood of a second event

Jerry RatcliffeTemple University

Demo

Load Forecasting

Improving CompStat

• Load forecasting• “Given the time of year, day of week, time of day and

general trend, what counts of crimes should I expect?”

What Do We Mean By Load Forecasting?

• Load forecasting• Generating aggregate crime counts for a future timeframe

using cyclical time series analysis

Measure cyclical patterns

Identify non-cyclical trend

Forecast expected count

+

bit.ly/gorrcrimeforecastingpaper

Load Forecasting

• Measure cyclical patterns• Take historic incidents (for example: last five years)• Generate multiplicative seasonal indices

– For each time cycle:» time of year» day of week» time of day

– Count incidents within each time unit (for example: Monday)– Calculate average per time unit if incidents were evenly

distributed– Divide counts within each time unit by the calculated average

to generate multiplicative indices» Index ~ 1 means at the average» Index > 1 means above average» Index < 1 means below average

Load Forecasting

Load Forecasting

Load Forecasting

Load Forecasting

Load Forecasting

• Identify non-cyclical trend• Take recent daily counts (for example: last year daily

counts)• Remove cyclical trends by dividing by indices

• Run a trending function on the new counts– Simple average

» Last X Days

– Smoothing function» Exponential smoothing» Holt’s linear exponential smoothing

Load Forecasting

• Forecast expected count• Project trend into future timeframe

– Always flat» Simple average» Exponential smoothing

– Linear trend» Holt’s linear exponential smoothing

• Multiple by seasonal indices to reseasonalize the data

Load Forecasting

Measure cyclical patterns

Identify non-cyclical trend

Forecast expected count

+

bit.ly/gorrcrimeforecastingpaper

How Do We Know It’s Accurate?

• Testing• Generated forecasting packages (examples)

– Commonly Used» Average of last 30 days» Average of last 365 days» Last year’s count for the same time period

– Advanced Combinations» Different cyclical indices (example: day of year vs. month of year)» Different levels of geographic aggregation for indices» Different trending functions

• Scoring methodologies (examples)– Mean absolute percent error (with some enhancements)– Mean percent error– Mean squared error

• Run thousands of forecasts through testing framework• Choose the right technique in the right situation

Demo

Research Topics

Research Topics

• Analysis– Real-time Functionality

• Consume real-time data streams• Conduct ongoing, automated analysis• Push real-time alerts

• Risk Forecasting– Load forecasting enhancements

• Machine learning-based model selection• Weather and special events

– Combining short and long term risk forecasts• NIJ project with Jerry Ratcliffe & Ralph Taylor• Neighborhood composition modeling using ACS data

– Risk Terrain Modeling

Research Topics

• Current Implementation Funding– Local Byrne Memorial JAG solicitation due July 21, 2011

• http://www.ojp.usdoj.gov/BJA/grant/jag.html

• Research Funding

Q&A

Contact Us

Robert CheethamPresident & CEOcheetham@azavea.com215.701.7713

Jeremy HeffnerHunchLab Product Managerjheffner@azavea.com215.701.7712