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.

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. About Us
Robert Cheetham
President & CEO
cheetham@azavea.com
215.701.7713
Jeremy Heffner
HunchLab Product Manager
jheffner@azavea.com
215.701.7712

3. About Azavea
• Founded in 2000
• 32 people
• Based in Philadelphia
– Boston office
– Minneapolis office
• Geospatial + web + mobile
– Software development
– Spatial analysis services

4. Clients & Industries
• Public Safety
• Municipal Services
• Public Health
• Human Services
• Culture
• Elections & Politics
• Land Conservation
• Economic Development

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

6. 10 Steps to Optimize Your Crime Analysis

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. Data Quality

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. 2. Examine Dates & Times
• Dates & Times
– Event-related times
• Actual occurrence time
– From / to time interval
• Report time
• Officers responded time

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. 2. Examine Dates & Times

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. Analytic Techniques

15. 4. Test Predictive Accuracy of KDE
• Kernel Density Estimation
– A smoothing technique that generates hotspot maps

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. 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. 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. 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. 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. 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. 5. Test NNI and use Gi*

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. 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. 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. 5. Test NNI and use Gi*
• Is it really better than KDE?
– Example from Chainey

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. 6. Run the Near Repeat Calculator

29. 6. Run the Near Repeat Calculator

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. 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. 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. 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. 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. 8. Generate a Risk Terrain Model
Gun shootings example
Source: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm

36. 8. Generate a Risk Terrain Model
Gun shootings example
Source: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm

37. 8. Generate a Risk Terrain Model
Gun shootings example
Source: Rutgers, http://www.rutgerscps.org/rtm/irvrtmgoogearth.htm

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. Use Cases

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. 9. Open Up Data (Appropriately)
• Public crime mapping sites
– Omega Group
• http://www.crimemapping.com/

42. 9. Open Up Data (Appropriately)
• Data portals
– Chicago Data Portal
• http://data.cityofchicago.org/
• Raw incident data from 2001 to present

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. 10. Find Research Partners
(A brief plug)

45. Q&A

46. About Us
Robert Cheetham
President & CEO
cheetham@azavea.com
215.701.7713
Jeremy Heffner
HunchLab Product Manager
jheffner@azavea.com
215.701.7712
www.azavea.com/hunchlab