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10 Steps to Optimize Your Crime Analysis             340 N 12th St, Suite 402             Philadelphia, PA 19107          ...
About Us    Robert Cheetham    President & CEO    cheetham@azavea.com    215.701.7713   Jeremy Heffner   HunchLab Product ...
About Azavea• Founded in 2000• 32 people• Based in Philadelphia   – Boston office   – Minneapolis office• Geospatial + web...
Clients & Industries•   Public Safety•   Municipal Services•   Public Health•   Human Services•   Culture•   Elections & P...
web-based crime analysis, early warning, and risk forecasting
10 Steps to Optimize Your Crime Analysis
10 Steps to Optimize Your Crime Analysis    Crime Analysis             Data              Analytic                         ...
Data Quality
1. Examine Geocoding Accuracy• Geocoding  – Process of turning addresses into geographic coordinates  – Examine accuracy  ...
2. Examine Dates & Times• Dates & Times   – Event-related times      • Actual occurrence time          – From / to time in...
2. Examine Dates & Times• Dates & Times   – Examining accuracy      • Data entry defaults      • Data validation on input ...
2. Examine Dates & Times
3. Examine Polygon Hierarchy• Polygon Hierarchy   – A set of geographic areas that nest within each other used     to orga...
Analytic Techniques
4. Test Predictive Accuracy of KDE• Kernel Density Estimation   – A smoothing technique that generates hotspot maps
4. Test Predictive Accuracy of KDE• When we look at a hotspot map what are we assuming?   – That crimes will happen in the...
4. Test Predictive Accuracy of KDE• Predictive Accuracy Index   – Spencer Chainey, Jill Dando Institute      • http://www....
4. Test Predictive Accuracy of KDE• Predictive Accuracy Index Steps    1. Generate kernel density map for historic period ...
4. Test Predictive Accuracy of KDE• Predictive Accuracy Index   – Caveats      • Number is relative to crime type and geog...
4. Test Predictive Accuracy of KDE• Predictive Accuracy Index   – Caveats      • Number is relative to crime type and geog...
5. Test NNI and use Gi*• Gi*  – Spencer Chainey, Jill Dando Institute        • http://www.popcenter.org/conference/confere...
5. Test NNI and use Gi*
5. Test NNI and use Gi*• How do we know hotspots exist though?   – Calculate nearest neighbor index (NNI)      • Determine...
5. Test NNI and use Gi*• Summary of Steps   –   Test for clustering with nearest neighbor index   –   Calculate crime coun...
5. Test NNI and use Gi*• Summary of Steps   –   Test for clustering with nearest neighbor index   –   Calculate crime coun...
5. Test NNI and use Gi*• Is it really better than KDE?   – Example from Chainey
6. Run the Near Repeat Calculator• Near Repeat Pattern Analysis   – Measures ‘contagion’ effect of crime incident   – How ...
6. Run the Near Repeat Calculator
6. Run the Near Repeat Calculator
6. Run the Near Repeat Calculator• Near Repeat Calculator   – http://www.temple.edu/cj/misc/nr/• Papers   – Near-Repeat Pa...
7. Conduct a Randomized Trial• Randomized Controlled Trial   – An experiment where study subjects (e.g. locations) are    ...
7. Conduct a Randomized Trial• Philadelphia Foot Patrol Experiment   – Jerry Ratcliffe, Temple University      • http://bi...
8. Generate a Risk Terrain Model• Risk Terrain Modeling   – Joel Caplan & Les Kennedy, Rutgers University      • http://ww...
8. Generate a Risk Terrain Model• Steps to Building a Model   1. List potential risk factors      •   Literature review   ...
8. Generate a Risk Terrain Model                       Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm...
8. Generate a Risk Terrain Model                       Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm...
8. Generate a Risk Terrain Model                       Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm...
8. Generate a Risk Terrain Model• Risk Terrain Modeling   – Risk Terrain Modeling Manual      • http://www.rutgerscps.org/...
Use Cases
9. Open Up Data (Appropriately)• Increase transparency and community engagement  –   Open data movement  –   Increases tru...
9. Open Up Data (Appropriately)• Public crime mapping sites   – Omega Group      • http://www.crimemapping.com/
9. Open Up Data (Appropriately)• Data portals   – Chicago Data Portal      • http://data.cityofchicago.org/      • Raw inc...
10. Find Research Partners• Benefits of Conducting Research   – Lowers the risk of trying something new   – Supplements li...
10. Find Research Partners         (A brief plug)
Q&A
About Us              Robert Cheetham              President & CEO              cheetham@azavea.com              215.701.7...
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10 Steps to Optimize Your Crime Analysis

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As we develop our crime analysis software, HunchLab, we are always on the look out for ways of examining and improving data quality as well as new academic research that shows promise to enhance crime analysis.

In this one-hour webinar, we first explain some of the ways we examine data quality when we utilize historic incident datasets for research and analysis and how you can use these techniques in your department. Then, we walk through a series of analytic techniques and practices that can help your department improve your crime analysis processes.

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Transcript of "10 Steps to Optimize Your Crime Analysis"

  1. 1. 10 Steps to Optimize Your Crime Analysis 340 N 12th St, Suite 402 Philadelphia, PA 19107 215.925.2600 info@azavea.com www.azavea.com/hunchlab
  2. 2. About Us Robert Cheetham President & CEO cheetham@azavea.com 215.701.7713 Jeremy Heffner HunchLab Product Manager jheffner@azavea.com 215.701.7712
  3. 3. About Azavea• Founded in 2000• 32 people• Based in Philadelphia – Boston office – Minneapolis office• Geospatial + web + mobile – Software development – Spatial analysis services
  4. 4. Clients & Industries• Public Safety• Municipal Services• Public Health• Human Services• Culture• Elections & Politics• Land Conservation• Economic Development
  5. 5. web-based crime analysis, early warning, and risk forecasting
  6. 6. 10 Steps to Optimize Your Crime Analysis
  7. 7. 10 Steps to Optimize Your Crime Analysis Crime Analysis Data Analytic Use Cases Quality Techniques • Geocoding • Kernel Density Map • Open data Predictive Accuracy • Dates & Times • Find research • NNI and Gi* partners • Polygon Hierarchies • Near Repeat Calculator • Randomized controlled trial • Risk Terrain Modeling
  8. 8. Data Quality
  9. 9. 1. Examine Geocoding Accuracy• Geocoding – Process of turning addresses into geographic coordinates – Examine accuracy • Correct locations – Geocoding method » Commercial geocoder » Street center line » Parcel database – POIs and landmarks – Incorrect clustering at: » Precinct locations, zip code and city centroids • High geocoding success rates – Ratcliffe suggests at least 85% (lowest acceptable) » http://bit.ly/ratcliffegeocoding – Examine unsuccessful geocodes for patterns
  10. 10. 2. Examine Dates & Times• Dates & Times – Event-related times • Actual occurrence time – From / to time interval • Report time • Officers responded time
  11. 11. 2. Examine Dates & Times• Dates & Times – Examining accuracy • Data entry defaults • Data validation on input • Clustering by time cycles – Day of week – Day of month – Day of year – Hour of day – Minute of hour
  12. 12. 2. Examine Dates & Times
  13. 13. 3. Examine Polygon Hierarchy• Polygon Hierarchy – A set of geographic areas that nest within each other used to organize resources (i.e. divisions, districts, PSAs, beats)• What makes a good hierarchy? – Perfectly nested polygons • No sliver polygons – Areas should be periodically rebalanced based on changing crime levels – Consider that splitting areas based on streets means one side of street is in one district / other side is in a different district.
  14. 14. Analytic Techniques
  15. 15. 4. Test Predictive Accuracy of KDE• Kernel Density Estimation – A smoothing technique that generates hotspot maps
  16. 16. 4. Test Predictive Accuracy of KDE• When we look at a hotspot map what are we assuming? – That crimes will happen in the hotspots again. – But… • How predictive is it? • How much historic data should we use? • What search radius should we use? • What is the density cutoff for a hotspot? Source: Chainey, http://www.popcenter.org/conference/conferencepapers/2010/Chainey-Gi-hotSpots.pdf
  17. 17. 4. Test Predictive Accuracy of KDE• Predictive Accuracy Index – Spencer Chainey, Jill Dando Institute • http://www.palgrave-journals.com/sj/journal/v21/n1/full/8350066a.html – Incorporates: • Desire for a high hit rate – Lots of crime incidents in a prior ‘hotspot’ • Desire for a small geographic area – Less to patrol, etc.
  18. 18. 4. Test Predictive Accuracy of KDE• Predictive Accuracy Index Steps 1. Generate kernel density map for historic period 2. Measure predictive validity against future time period 3. Higher number better• Example from Chainey
  19. 19. 4. Test Predictive Accuracy of KDE• Predictive Accuracy Index – Caveats • Number is relative to crime type and geography • Best for comparing different techniques (or parameter variations of techniques) for the same predictive period
  20. 20. 4. Test Predictive Accuracy of KDE• Predictive Accuracy Index – Caveats • Number is relative to crime type and geography • Best for comparing different techniques (or parameter variations of techniques) for the same predictive period Is there something similar but better than kernel density?
  21. 21. 5. Test NNI and use Gi*• Gi* – Spencer Chainey, Jill Dando Institute • http://www.popcenter.org/conference/conferencepapers/2010/Chainey-Gi-hotSpots.pdf – LISA statistic • Local indicator of spatial association – Compares local averages to global averages – Generates map visually similar to KDE
  22. 22. 5. Test NNI and use Gi*
  23. 23. 5. Test NNI and use Gi*• How do we know hotspots exist though? – Calculate nearest neighbor index (NNI) • Determines if clustering exists – NNI ~ 1: data is randomly distributed – NNI < 1: data is clustered – NNI > 1: data is uniformly distributed • Helps to answer if we have enough historic data for statistical significance • If data is not clustered neither Gi* nor KDE should be used
  24. 24. 5. Test NNI and use Gi*• Summary of Steps – Test for clustering with nearest neighbor index – Calculate crime counts within a grid – Run Gi* statistic – Set color ramp breakpoints based on fixed statistical significance levels
  25. 25. 5. Test NNI and use Gi*• Summary of Steps – Test for clustering with nearest neighbor index – Calculate crime counts within a grid – Run Gi* statistic – Set color ramp breakpoints based on fixed statistical significance levels Remember our friend the predictive accuracy index?
  26. 26. 5. Test NNI and use Gi*• Is it really better than KDE? – Example from Chainey
  27. 27. 6. Run the Near Repeat Calculator• Near Repeat Pattern Analysis – Measures ‘contagion’ effect of crime incident – How does one burglary change the risk that another burglary will occur nearby in the coming days?• Common in Some Types of Crime – Burglary – Theft from Vehicle – Gun Crime – Robbery – Bicycle Theft
  28. 28. 6. Run the Near Repeat Calculator
  29. 29. 6. Run the Near Repeat Calculator
  30. 30. 6. Run the Near Repeat Calculator• 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
  31. 31. 7. Conduct a Randomized Trial• Randomized Controlled Trial – An experiment where study subjects (e.g. locations) are randomly assigned to different treatment protocols – Academics do this regularly• But why do this yourself? – Proves/disproves a technique’s efficacy for your department – Successfully mimicking a published trial gives you the skills to experiment based on local anecdotal evidence
  32. 32. 7. Conduct a Randomized Trial• Philadelphia Foot Patrol Experiment – Jerry Ratcliffe, Temple University • http://bit.ly/phillyfootpatrol – Concentrated patrol in 60 violent crime hotspots – Outlines full methodology • Experimental design • Evaluation – Result was a net reduction of 53 violent crimes
  33. 33. 8. Generate a Risk Terrain Model• Risk Terrain Modeling – Joel Caplan & Les Kennedy, Rutgers University • http://www.rutgerscps.org/rtm/ – Forms a combined risk surface of several spatial risk factors that correlate with a particular type of crime – Describes the environmental context within crime occurs
  34. 34. 8. Generate a Risk Terrain Model• Steps to Building a Model 1. List potential risk factors • Literature review • Departmental experience 2. Assemble GIS data sets for each factor 3. Operationalize each factor and test for correlation 4. Combine correlated factors into combined risk terrain
  35. 35. 8. Generate a Risk Terrain Model Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm
  36. 36. 8. Generate a Risk Terrain Model Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm
  37. 37. 8. Generate a Risk Terrain Model Gun shootings exampleSource: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm
  38. 38. 8. Generate a Risk Terrain Model• Risk Terrain Modeling – Risk Terrain Modeling Manual • http://www.rutgerscps.org/rtm/ – Online training • http://www.rutgerscps.org/rtm/webinar.html
  39. 39. Use Cases
  40. 40. 9. Open Up Data (Appropriately)• Increase transparency and community engagement – Open data movement – Increases trust – Allows novel uses for crime data – Increases perceived value of good data• Not a new idea – NIJ guide released in 2001 • https://www.ncjrs.gov/pdffiles1/nij/188739.pdf
  41. 41. 9. Open Up Data (Appropriately)• Public crime mapping sites – Omega Group • http://www.crimemapping.com/
  42. 42. 9. Open Up Data (Appropriately)• Data portals – Chicago Data Portal • http://data.cityofchicago.org/ • Raw incident data from 2001 to present
  43. 43. 10. Find Research Partners• Benefits of Conducting Research – Lowers the risk of trying something new – Supplements limited resources • Labor • Software • Knowledge/techniques/statistical rigor – Encourages cutting edge analysis• Types of Partners – Academic crime researchers – Nonprofits – Commercial entities
  44. 44. 10. Find Research Partners (A brief plug)
  45. 45. Q&A
  46. 46. About Us Robert Cheetham President & CEO cheetham@azavea.com 215.701.7713 Jeremy Heffner HunchLab Product Manager jheffner@azavea.com 215.701.7712www.azavea.com/hunchlab

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