crime risk forecasting and predictive analytics - esri uc

Risk Forecasting & Predictive AnalyticsHunchLab Research Update

Robert Cheethamcheetham@azavea.com

@rcheetham

Michael UrciouliMichael.Urciouli@phila.gov

Philadelphia Police Department

Crime Analysis Unit founded in 1997

Desktop ArcGIS– Analysis to support investigations

– Support COMPSTAT process

Web-based crime analysis

3 staff

Who is Azavea?

25 people- software engineers- spatial analysts- project managers

Spatial Analysis

Web & Mobile

High Performance Geoprocessing

User Experience

R&D

10% Research ProgramPro Bono ProgramTime-to-Give-Back ProgramEmployee-focused Culture Projects with Social Value

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

Dashboard

Space + Time

Space + Time

Animation

Early Warning & Notification

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

– Cyclical Forecasting

Near Repeat Pattern

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

Near repeat victimization– Incident at a nearby location at a later time

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

Near Repeat Pattern Analysis

An example– 180 days of burglaries – Philadelphia Division 6

Near Repeat Pattern Analysis

An example– 180 days of burglaries – Philadelphia Division 6

Near Repeat Pattern Analysis

An example– 180 days of burglaries – Philadelphia Division 6

Near Repeat Pattern Analysis

Online Version

Near Repeat Pattern AnalysisHow 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 Ratcliffe

Temple University

Cyclical Patterns

Improving CompStat– “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?– 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

Cyclical 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

Cyclical Forecasting

Cyclical Forecasting

Cyclical Forecasting

Cyclical Forecasting

Cyclical 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

Cyclical 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

Cyclical Forecasting

Measure cyclical patterns

Identify non-cyclical trend

Forecast expected count

+

bit.ly/gorrcrimeforecastingpaper

Cyclical Forecasting

Cyclical Forecasting

Cyclical Forecasting

Cyclical Forecasting

How Do We Know It Works? 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

Ongoing R&D

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 Research Funding

Acknowledgements

National Science Foundation – SBIR Program– Grant #IIP-0637589– Grant #IIP-0750507

Philadelphia Police Department Jerry Ratcliffe, Temple University Tony Smith, University of Pennsylvania Peirce County, WA

Upcoming Conferences

Crime Mapping Research Conference, October IACP, October

Many Thanks!© Photo used with permission from Alphafish, via Flickr.com

QuestionsMichael UrciouliMichael.Urciouli@phila.gov

@rcheetham