340 N 12th St, Suite 402Philadelphia, PA 19107
www.azavea.com/hunchlab
Crime Risk Forecasting
Near Repeat Pattern Analysis and Load Forecasting
About Us
Robert CheethamPresident & [email protected]
Jeremy HeffnerHunchLab Product [email protected]
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 & [email protected]
Jeremy HeffnerHunchLab Product [email protected]
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