Predictive Policing
CRIME FORECASTING FOR THE FUTURE
Its hot!
PERF’s survey found that only 38 percent of responding police departments are currently using
predictive policing, but 70 percent expect that they will implement this strategy within the next
two to five years.
This is not “Minority Report”
“Minority Report is about predicting who will commit a crime before they commit it. This is
about predicting where and when crime is most likely to occur, not who will commit
it.”( Brantingham)
Who is interested in this?
Civil Rights Activists – Have concerns of these techniques intruding on the rights of citizens,
especially the poor and minorities
Practitioners (Analysts) – Have a professional interest on how this can make their work better /
more useful
Police Chiefs – Eager to find new techniques to reduce crime
The U.S. Government – New forum for funding, research, literature, and evaluation
The Private Sector – Sees potential for funding of research grants, consulting and software
development
Researchers-have background to design predictive models
(Hollywood et. al)
Predictive Policing
This is the application of analytical techniques to identify likely targets for police intervention
Borrowed from Business
Walmart found that large weather events resulted in increased sales of water, duct tape and pop
tarts (strawberry). They alter their supply chain to meet the demand before the storm hits.
Goal
Move from a reactive posture to a proactive posture.
Why?
◦ Be more efficient and effective with your resources.
◦ Opportunities to prevent, deter, thwart, mitigate, and respond to crime more effectively (Beck and McCue 2015)
Proactive
Much of traditional policing in reactive in nature.
◦ After the crime
Much of Predictive policing is more proactive
◦ Stopping something before it happens
Example, Richmond VA
Risk-based deployment was tested on New Year’s Eve 2003.
Using a risk-based deployment strategy, police identified locations and times expected to be
associated with increased complaints of random gunfire and proactively deployed police resources to
those locations to prevent or deter crime or respond more rapidly.
The results demonstrated increased public safety associated with the predictive-policing strategy.
Random gunfire complaints were decreased by 47 percent, highlighting the deterrent effect
associated with information-based deployment, while the number of weapons recovered went up
246 percent, underscoring the rapid response possible with effective prepositioning of resources. In
addition, these marked increases in public safety were associated with a reduction in police resources
required, resulting in a $15,000 savings in personnel costs alone during the eight-hour initiative.
The ability to anticipate the time, the location, and the nature of crime supports the police manager’s
ability to proactively allocate resources—preventing or deterring crime through targeted police
presence and enabling rapid response by pre-positioning police assets when and where they are likely
to be needed (Beck and McCue 2015).
What’s in a name?
The term “predictive” can be misleading.
Others terms that can help understand the idea are:
◦ “proactive policing,” “preventative policing,” “adaptive policing,” “evidence-based policing” and “datadriven policing.”
Why it makes sense
Much crime is predictable
◦ Criminals tend to operate in their comfort zones
◦ Do what was successful in the past and generally close to the same time as place as the last crime (not
all).
◦ Criminals and victims follow common life patterns; where those patterns overlap can lead to crimes –
Geographic and temporal features influence the where and when of those patterns
◦ Criminals make ―rational‖ decisions using factors such as area & target suitability, risk of getting caught,
etc.
◦ Can ID many of these patterns and factors; can steer criminals’ decisions through interventions
(Hollywood et. al)
What types of crimes
Stranger offenses
◦ Robberies, burglaries, and thefts.
Not as much for vice or some interpersonal violence
◦ DV perhaps
Some crime will always be stochastic or random
Another Example
Currently, LAPD’s predictive policing program is being applied to a 50-squaremile area. The
program breaks up the larger area into a grid composed of 500- foot squares, or “boxes.” Each
forecast assigns a crime probability score to each box.
Patrol officers are informed of the highest probability boxes and are directed to use any available
time to focus on those boxes.
The department encourages officers to proactively use their knowledge, skills, and experience to
identify reasons why the box has a high crime risk and then actively work to address those issues
(COPS 2014).
https://www.youtube.com/watch?v=U0gX_z0V0nE
Necessary Elements/Ideas
Integrated information and operations:
◦ There are no silos, effectively integrate information and operations.
Seeing the big picture:
◦ Prevention is as important as response, and every incident is an information-gathering opportunity.
Cutting-edge analysis and technology:
◦ There is a wealth of tools and technology already available and it is imperative that departments learn how to
use them.
Linkage to performance:
◦ Understand what your impact was.
Adaptability to changing conditions:
◦ This concept highlights the need for flat-networked organizations, training in how to adapt to strategies based
on information and high professional standards.
Business Model
PP is an overall organizational posture.
Prediction is only part of this model.
1. Collecting the data
2. Analyzing the data
3. Design police operations (use predictions and police experience)
4. Assessment of Criminal Response
Business Process
Four Main areas
1.Methods of predicting crimes
◦ Forecast places and times with an increased risk of crime.
Hot spots
Risk Terrain analysis
◦ Not based on past crime but on risk factors of the environment.
◦ ID geographic features that contribute to crime risks (bars, liquor stores, major roads) and make
predictions about crime risk based on how close a given location is to the risk inducing feature.
Four Main areas
2. Methods for predicting offenders
Identify individuals at risk of offending in the future.
◦ Level of Service Inventor-Revised (LSI-R) used by corrections.
◦ Static and dynamic factors
Four Main areas
3. Methods of predicting perpetrators’ identities
◦ Create profiles that accurately match likely offenders with specific past crimes.
◦ Find suspects using sensor data (GPS, LPNs)
◦ Anchor point (estimating an offenders place of work or frequent activity) based on the location of
criminal activity. (assumes linked crimes)
◦ Offenders commit crimes close to their anchor points.
◦ The frequency of offending decreases as the distance from their anchor point increases.
◦ Suspect prioritization, patrol saturation, neighborhood canvasses
Four Main areas
4. Methods for predicting victims of crime
◦ Identify groups or individuals likely to become victims of crime.
◦ Identify people at risk for victimization (engaged in high risk criminal behavior)
◦ Data mining to learn repeat offenders at risk
◦ Looking at network of people arrested together in Chicago.
◦ For example-living in certain places increases your homicide risk- simply being arrested during this period increases the
aggregate homicide rate by nearly 50%, but being in a network component with a homicide victim increases the homicide rate
by a staggering 900% (from 55.2 to 554.1).
◦ The further away you are socially from a homicide victim the lower your chances of being a victim (Papachristos and Wilderman
2013)
◦ Identify people at risk for domestic violence
◦ Data mining to find DV and other disturbances involving local residents when in other jurisdictions
An example
1.Methods of predicting crimes
◦ Forecast places and times with an increased risk of crime.
Hotspots identification-based on historical crime data
Very small areas seem to have a large amount of crime
◦ One street block, one apartment building
4-6% account for about 50% of crime
Hot spots are of consistent over time
Treatment-13-15 minutes of a stationary patrol unit randomly every two hours.
Hot spot map
Predictions are only half
Predictions are half the battle
◦ The other half is carrying out interventions based in predictions that lead to reductions in crime.
Dangers
Police stop following the predictions
◦ Go back to the good old ways
Police given specific zones at risk treat all those in those zones as criminals.
◦ “The tyranny of the algorithm” (Marx)
◦ Harms legitimacy
Hurried adoption
Cost
No effort to deal with underlying causes of crime
◦ There is something about those zones that make them hot.
Model not well explained to officers
◦ No “Buy-in”
Officers view it as micro-management
◦ Shut down
More Dangers
Officers and police managers trust their own decision making and don’t want competition from
computers.
◦ From civilians
Analysts fall in love with one method
◦ The methods of analysis have to vary with the problem.
Let the analysts do their work
◦ Let the analysts worry about how the model works-you worry about how accurate it is.
Find the balance in methods and implementation.
◦ A decent transparent model that is actually used will outperform a sophisticated system that predicts
better but sits on a shelf.
Some predictions will be wrong-focus on overall organizational efficiency.
Data
Must collect and analyzing data.
◦ Land use data-city planning department
◦ Incident data-RMS/CAD
◦ Time spent data-time on a call
Quality and quantity of the data shape usefulness.
◦ Garbage in, garbage out.
Data often difficult:
◦ Holes in data due to forms
◦ Holes in the data due to practice
◦ Sources are diverse which requires fusion-often difficult
Interventions
Have to have the resources to execute the interventions
◦ Money
Interventions can be complex
◦ Trained them on what you want them to do
Must provide information to officers that is specific enough to carry out the intervention.
◦ Communication
Have to monitor the process of implementation of the intervention
◦ Process evaluation
◦ Skills to do this
Myths of Predictive Policing
The computer knows the future
◦ They only tell you risks not certainties
◦ “It's tough to make predictions, especially about the future.”
The computer does it all for you
◦ No you have get the data, review and interpret the results, make recommendations and take action
Accurate predictions automatically lead to crime reductions
◦ You have to take action to reduce crime-predictions are just directions.
What you need to do it
Substantial top level support.
Resources dedicated to the model
Enthusiastic and interested personnel
Foster a good working relationship between analysts and officers
Challenges
Limited resources
◦ Can impact ability to collect, analyze, implement and analyze
Political pressure
◦ Pressure for quick results not conducive to methodology
Information management
◦ Share good data at higher level than every before
Data overload
◦ Can feel overwhelmed with the amount of data that must be collected
Data quality
◦ Timely, accurate and reliable
Adaptation
◦ Initiatives must be adaptable to changes in operational environment
Tool Box-First Steps
ID data from other agencies that may be helpful in PP
Create PP task force
Create MOUs
Develop software for PP analysis
Create guidelines on how to make use of the predictive outcomes
Questions?