Crime Risk Forecasting: Near Repeat Pattern Analysis & Load Forecasting


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This is a rather technical dive into the near repeat pattern analysis and load forecasting features that we've built into HunchLab. Both of these features are aimed at helping a law enforcement agency to better predict risk levels across their jurisdictions and allocate resources according. While no application of predictive analytics will be perfect, forecasting risk based on models of the past can help officers and analysts to anticipate the appropriate next steps.

Near repeat pattern analysis helps officers quantify the risk that arises from multiple incidents happening close to one another in space and time. What we are quantifying is how the fact that your neighbor's house is burgled raises your risk of a burglary in the coming days and weeks.

With load forecasting we are looking at cyclical temporal patterns in incidents. How does the time of year, time of day, and day of week change the levels of crime incidents that we should expect across a jurisdiction? By modeling these cyclical patterns we can project crime levels into the future, helping law enforcement agencies to allocate resources appropriately as well as better manage organizational accountability.

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Crime Risk Forecasting: Near Repeat Pattern Analysis & Load Forecasting

  1. 1. 340 N 12 th St, Suite 402 Philadelphia, PA 19107 215.925.2600 [email_address] Crime Risk Forecasting Near Repeat Pattern Analysis and Load Forecasting
  2. 2. About Us Robert Cheetham President & CEO [email_address] 215.701.7713 Jeremy Heffner HunchLab Product Manager [email_address] 215.701.7712
  3. 3. Agenda <ul><li>Company Background </li></ul><ul><li>HunchLab </li></ul><ul><ul><li>Risk Forecasting </li></ul></ul><ul><ul><ul><li>Near Repeat Pattern Analysis </li></ul></ul></ul><ul><ul><ul><li>Load Forecasting </li></ul></ul></ul><ul><ul><li>Future Research Topics </li></ul></ul><ul><li>Q&A </li></ul>
  4. 4. About Azavea <ul><li>Founded in 2000 </li></ul><ul><li>25 people </li></ul><ul><li>Based in Philadelphia </li></ul><ul><ul><li>Boston satellite office </li></ul></ul><ul><li>Geospatial + web + mobile </li></ul><ul><ul><li>Software development </li></ul></ul><ul><ul><li>Spatial analysis services </li></ul></ul>
  5. 5. Clients & Industries <ul><li>Public Safety </li></ul><ul><li>Municipal Services </li></ul><ul><li>Public Health </li></ul><ul><li>Human Services </li></ul><ul><li>Culture </li></ul><ul><li>Elections & Politics </li></ul><ul><li>Land Conservation </li></ul><ul><li>Economic Development </li></ul>
  6. 6. Azavea & Governments
  7. 7. HunchLab
  8. 8. <ul><li>web-based crime analysis, early warning, and risk forecasting </li></ul>
  9. 9. <ul><li>Crime Analysis </li></ul><ul><ul><li>Mapping (spatial / temporal densities) </li></ul></ul><ul><ul><li>Trending </li></ul></ul><ul><ul><li>Intelligence Dashboard </li></ul></ul><ul><li>Early Warning </li></ul><ul><ul><li>Statistical & Threshold-based Hunches (data mining) </li></ul></ul><ul><ul><li>Alerting </li></ul></ul><ul><li>Risk Forecasting </li></ul><ul><ul><li>Near Repeat Pattern </li></ul></ul><ul><ul><li>Load Forecasting </li></ul></ul>
  10. 11. Near Repeat Pattern Analysis
  11. 12. Contagious Crime? <ul><li>Near repeat pattern analysis </li></ul><ul><ul><ul><li>“ If one burglary occurs, how does the risk change nearby?” </li></ul></ul></ul>
  12. 13. What Do We Mean By Near Repeat? <ul><li>Repeat victimization </li></ul><ul><ul><li>Incident at the same location at a later time (likely related) </li></ul></ul><ul><li>Near repeat victimization </li></ul><ul><ul><li>Incident at a nearby location at a later time (likely related) </li></ul></ul><ul><li>Incident A (place, time) --> Incident B (place, time) </li></ul>
  13. 14. Near Repeat Pattern Analysis <ul><li>The goal: </li></ul><ul><ul><li>Quantify short term risk due to near-repeat victimization </li></ul></ul><ul><ul><ul><li>“ If one burglary occurs, how does the risk of burglary for the neighbors change?” </li></ul></ul></ul><ul><li>What we know: </li></ul><ul><ul><li>Incident A (place, time) --> Incident B (place, time) </li></ul></ul><ul><ul><ul><li>Distance between A and B </li></ul></ul></ul><ul><ul><ul><li>Timeframe between A and B </li></ul></ul></ul><ul><li>What we need to know: </li></ul><ul><ul><li>What distances/timeframes are not simply random? </li></ul></ul>
  14. 15. Near Repeat Pattern Analysis <ul><li>The process </li></ul><ul><ul><li>Observe the pattern in historic data </li></ul></ul><ul><ul><li>Simulate the pattern in randomized historic data </li></ul></ul><ul><ul><li>Compare the observed pattern to the simulated patterns </li></ul></ul><ul><ul><li>Apply the non-random pattern to new incidents </li></ul></ul><ul><li>An example </li></ul><ul><ul><li>180 days of burglaries in Division 6 of Philadelphia </li></ul></ul>
  15. 16. Near Repeat Pattern Analysis
  16. 17. Near Repeat Pattern Analysis
  17. 18. Near Repeat Pattern Analysis
  18. 19. Near Repeat Pattern Analysis
  19. 20. Near Repeat Pattern Analysis <ul><li>How can you test your own data? </li></ul><ul><ul><li>Near Repeat Calculator </li></ul></ul><ul><ul><ul><li> </li></ul></ul></ul><ul><li>Papers </li></ul><ul><ul><li>Near-Repeat Patterns in Philadelphia Shootings (2008) </li></ul></ul><ul><ul><ul><li>One city block & two weeks after one shooting </li></ul></ul></ul><ul><ul><ul><ul><li>33% increase in likelihood of a second event </li></ul></ul></ul></ul>Jerry Ratcliffe Temple University
  20. 21. <ul><li>Demo </li></ul>
  21. 22. Load Forecasting
  22. 23. Improving CompStat <ul><li>Load forecasting </li></ul><ul><ul><ul><li>“ Given the time of year, day of week, time of day and general trend, what counts of crimes should I expect?” </li></ul></ul></ul>
  23. 24. What Do We Mean By Load Forecasting? <ul><li>Load forecasting </li></ul><ul><ul><ul><li>Generating aggregate crime counts for a future timeframe using cyclical time series analysis </li></ul></ul></ul> Measure cyclical patterns Identify non-cyclical trend Forecast expected count +
  24. 25. Load Forecasting <ul><li>Measure cyclical patterns </li></ul><ul><ul><ul><li>Take historic incidents (for example: last five years) </li></ul></ul></ul><ul><ul><ul><li>Generate multiplicative seasonal indices </li></ul></ul></ul><ul><ul><ul><ul><li>For each time cycle: </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>time of year </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>day of week </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>time of day </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Count incidents within each time unit (for example: Monday) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Calculate average per time unit if incidents were evenly distributed </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Divide counts within each time unit by the calculated average to generate multiplicative indices </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Index ~ 1 means at the average </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Index > 1 means above average </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Index < 1 means below average </li></ul></ul></ul></ul></ul>
  25. 26. Load Forecasting
  26. 27. Load Forecasting
  27. 28. Load Forecasting
  28. 29. Load Forecasting
  29. 30. Load Forecasting <ul><li>Identify non-cyclical trend </li></ul><ul><ul><ul><li>Take recent daily counts (for example: last year daily counts) </li></ul></ul></ul><ul><ul><ul><li>Remove cyclical trends by dividing by indices </li></ul></ul></ul><ul><ul><ul><li>Run a trending function on the new counts </li></ul></ul></ul><ul><ul><ul><ul><li>Simple average </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Last X Days </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Smoothing function </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Exponential smoothing </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Holt’s linear exponential smoothing </li></ul></ul></ul></ul></ul>
  30. 31. Load Forecasting <ul><li>Forecast expected count </li></ul><ul><ul><ul><li>Project trend into future timeframe </li></ul></ul></ul><ul><ul><ul><ul><li>Always flat </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Simple average </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Exponential smoothing </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Linear trend </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Holt’s linear exponential smoothing </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Multiple by seasonal indices to reseasonalize the data </li></ul></ul></ul>
  31. 32. Load Forecasting Measure cyclical patterns Identify non-cyclical trend Forecast expected count +
  32. 33. How Do We Know It’s Accurate? <ul><li>Testing </li></ul><ul><ul><ul><li>Generated forecasting packages (examples) </li></ul></ul></ul><ul><ul><ul><ul><li>Commonly Used </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Average of last 30 days </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Average of last 365 days </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Last year’s count for the same time period </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><li>Advanced Combinations </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Different cyclical indices (example: day of year vs. month of year) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Different levels of geographic aggregation for indices </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Different trending functions </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Scoring methodologies (examples) </li></ul></ul></ul><ul><ul><ul><ul><li>Mean absolute percent error (with some enhancements) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mean percent error </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mean squared error </li></ul></ul></ul></ul><ul><ul><ul><li>Run thousands of forecasts through testing framework </li></ul></ul></ul><ul><ul><ul><li>Choose the right technique in the right situation </li></ul></ul></ul>
  33. 34. <ul><li>Demo </li></ul>
  34. 35. Research Topics
  35. 36. Research Topics <ul><li>Analysis </li></ul><ul><ul><li>Real-time Functionality </li></ul></ul><ul><ul><ul><li>Consume real-time data streams </li></ul></ul></ul><ul><ul><ul><li>Conduct ongoing, automated analysis </li></ul></ul></ul><ul><ul><ul><li>Push real-time alerts </li></ul></ul></ul><ul><li>Risk Forecasting </li></ul><ul><ul><li>Load forecasting enhancements </li></ul></ul><ul><ul><ul><li>Machine learning-based model selection </li></ul></ul></ul><ul><ul><ul><li>Weather and special events </li></ul></ul></ul><ul><ul><li>Combining short and long term risk forecasts </li></ul></ul><ul><ul><ul><li>NIJ project with Jerry Ratcliffe & Ralph Taylor </li></ul></ul></ul><ul><ul><ul><li>Neighborhood composition modeling using ACS data </li></ul></ul></ul><ul><ul><li>Risk Terrain Modeling </li></ul></ul>
  36. 37. Research Topics <ul><li>Current Implementation Funding </li></ul><ul><ul><li>Local Byrne Memorial JAG solicitation due July 21, 2011 </li></ul></ul><ul><ul><ul><li>http:// </li></ul></ul></ul><ul><li>Research Funding </li></ul>
  37. 38. Q&A
  38. 39. Contact Us Robert Cheetham President & CEO [email_address] 215.701.7713 Jeremy Heffner HunchLab Product Manager [email_address] 215.701.7712