INTERVENTION PLANNING INTEL
REPORT (IPIR): City, State
Priority Crime Type
Analysis Period
XXX
XX/XX/XXXX – XX/XX/XXXX
Mean Block Length
Cell Size (Micro-place)
XXX feet
XXX feet
OVERVIEW
#### incidents of [crime] that occurred in City, State during the period of [date –
date] were analyzed. Analyses within this IPIR include:

Descriptive Analysis
o Nearest Neighbor (NN) analysis: suggests that the distribution of robbery
is significantly clustered.
o

Temporal Heat Map: visually communicates day-of-week and time-of-day
breakdowns of [crime] incidents; suggests uneven temporal distribution
of incidents.
Risk Terrain Modeling (RTM)
o ## environmental risk factors were tested. RTM identified ## that
significantly enhanced the risk of [crime] at places. The most attractive
factor was XXX, which carried a relative risk value of ###.
o
Highest risk places were nearly ### times more likely to experience
[crime] than some other places.
o
The highest risk places cover #% of [city’s] land area and account for
##% of [crime] incidents. Moderately high risk places cover #% of the
area and account for ##% of [crime] incidents. Above average risk places
cover ##% of the area and account for ##% of [crime] incidents. In the
aggregate, all places with above average risk scores account for ##% of
the crime located in less than ###% of [city’s] land area.
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PIN MAP
This IPIR analyzed all incidents (N=XXXX) of [crime] that occurred on the streets of [city]. All data
were obtained from [source] as [XY coordinates, shapefiles and/or geodatabases] and prepared for
analysis in [GIS].
The map, below, displays each [crime] incident location. Visual inspection of the points on the map
suggests that [crimes] are not uniformly distributed throughout the jurisdiction and may be
clustered at certain areas. A Nearest Neighbor (NN) analysis was conducted to determine if incident
locations are, in fact, statistically closer than expected under spatial randomness. Results of the NN
analysis suggest that the distribution of [crime] is statistically significantly clustered.
Having verified that [crime] incidents cluster spatially, this IPIR lends answers as to why they cluster.
[Insert Pin Map Here]
Shows the spatial distribution of individual crime incidents during the analysis period.
2
TEMPORAL HEAT MAPS
Day-of-Week by Months in a Year
[Insert Graphic Here]
Shows a temporal breakdown of crime incidents during the analysis period.
[For a easy to follow tutorial on producing temporal heat maps in Microsoft Excel, see
http://www.riskterrainmodeling.com/be-the-trainer.html]
Day-of-Week by Hours in a Day
[Insert Graphic Here]
Shows a temporal breakdown of crime incidents during the analysis period.
3
RISK TERRAIN MODELING (RTM)
RTM diagnoses why crimes have already clustered at certain places and forecasts where crimes are
likely to emerge in the future. Risk terrain maps show the presence, absence, or intensity of all
statistically validated risk factors for crime (weighted accordingly) at every location throughout a
landscape. RTM "paints a picture" of place-based contexts for crime.
A risk terrain model was produced for incidents of [crime] in [City, State] during the analysis period
using the Risk Terrain Modeling Diagnostics (RTMDx) Utility, a free software application produced by
the Rutgers University Center on Public Security (available at www.rtmdx.com). A risk terrain map for
this model is presented below. It shows where conditions are most vulnerable for [crime] to occur.
RTM: Date – Date [Your time frame, e.g. 1 year]
An analysis was designed to represent the risk factors for #### [crime] incidents considering the
potential spatial influences of ## risk factors: [List all factors tested, here]. All geographic calculations
were conducted using square raster cells of ###ft and an average block length value of ###ft. There
were ##### raster cells used in the analysis, of which #### cells contained events.
A significant Risk Terrain Model for [crime] was found that contains ## risk factors. The model has
relative risk scores for each micro-level place ranging from 1 for the lowest risk place to ### for the
highest risk place. These scores allow places to be easily compared. For instance, a place with a score
of ### has an expected rate of crime that is ### times higher than a place with a score of 1.
Relative Risk Score Summary
Mean = ###
Standard deviation (SD) = ###
“Highest Risk” places ≥ SD * 2 + Mean = ###
Range: 1 - ###
[Insert Risk Terrain Map Here]
[Shows a risk terrain map of the jurisdiction, with additional information. For example, commentary may
communicate how “Moderately High Risk places + Highest Risk places = 11% of Land Area & 32% of
Crimes)”]
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Percent of Crime Incidents in High-Risk Places
Percent of Crime Incidents
100
90
80
70
60
50
40
30
20
10
0
Cell Designations
Displayed above is the percent of [crime] incidents located within high-risk places of City, State.
Overall, the highest risk places experienced a disproportionate share of [crime] incidents. For example,
the top 5% of these places accounted for nearly ##% of the [crime] incidents during the analysis
period; the top 25% of highest risk places accounted for over ##%.
Looking at the table below, risk factors in the risk terrain model for [crimes] should be interpreted
according to their operationalization, spatial influence, and relative risk value. For example: places
within a high density of Pawn Shops (i.e., clustered within a bandwidth of 181ft) are nearly 2 times
more likely to experience robbery incidents than other places. Or, places within 905ft (proximity) to
grocery stores with liquor licenses are nearly 3½ times more likely to experience [crime] incidents
than other places.
Risk Factor
Grocery Stores w/ Liquor License
Pawn Shops
Off-Premise Liquor License
On-Premise Liquor License
Laundromats
Check Cashing Centers
Etc.….
Operationalization
Proximity
Density
Density
Density
Density
Proximity
…
Spatial Influence
905
181
181
181
362
1086
…
Relative Risk Value
3.43
1.98
1.93
1.80
1.59
1.34
…
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HIGH-RISK CLUSTER ANALYSIS
A Getis-Ord Gi* statistic (Z score) for each place was calculated using spatial statistics tools in [a GIS].
Results reveal many high-risk clusters. High-risk clusters represent groups of places with high risk
scores next to (contingent to) other places with similarly high risk scores. The distribution of high-risk
clusters for [crime] is displayed in the map below.
[Insert High-Risk Cluster Map Here]
[Shows results from a Getis-Ord Gi* statistical analysis – e.g., produced in a GIS]
RTM SUMMARY
[Crime] incidents tend to cluster at certain places and this clustering can be explained (in part) by
certain features of the landscape that attract illegal behavior to these places. Individually, these
attractive landscape features create risks of [crime] outcomes. But, places where several or all of these
features co-locate create even greater risks. These spatial patterns of risk, or vulnerability, for [crime]
occur beyond random chance and are statistically significant.
The highest risk places in [city/jurisdiction] exist within just ## square miles, or #% of the land area,
and account for ##% of all [crime] incidents. These places have at least ## times greater likelihood of
experiencing [crime] than other places in the jurisdiction. In total, ##% of [city’s] land area is
“Moderately High Risk” or “Highest Risk” and accounts for ##% of all [crime] incidents. Many of these
risky places are clustered and, therefore, can be efficiently targeted for interventions aimed at crime
prevention and risk reduction.
Much information and insights can be gained from the risk terrain model results table. The tabular
output communicates not only what factors are significant attractors of [crime] but also which ones are
not particularly important (by their omission). Comparing relative risk values (RRVs) across model
factors is useful for prioritizing risky features and for speculating why some features may pose
exceptionally high risks compared to others.
A review of the Temporal Heat Maps can inform the production of (additional) temporally constrained
risk terrain models that isolate the risk factors attracting criminal behavior at peak days or time periods.
For instance, risk terrain models can be produced for the crime-dense periods of [“Monday thru
Sunday, from 7pm-4am”] or [“Friday thru Sunday, from 10pm-5am”]. Another possibility is to
produce risk terrain models for particular police shifts (i.e., 3 risk terrain models for each 8-hour shift).
That is, some factors, such as schools, may pose high-risks during one time of the day but not during
others – allowing for customized policing tactics based on working shifts.
[See http://www.riskterrainmodeling.com/intervention-planning.html for information about how to
interpret these results for action]
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REVIEW & ACTIONABLE RECOMMENDATIONS
[Add your insights and conclusions here. Synthesize the results for action.]
[See http://www.riskterrainmodeling.com/intervention-planning.html for information on interpreting
results for action]
Tips for Implementing Risk-Based Interventions
1. Have clearly defined target areas that are distinctly identified as high-risk.
2. Risk factors in the target area should be clearly identified so the intervention activities can focus on
these risks. Let the IPIR inform strategies that tell police what to do when they get to the target areas,
not just where to go.
3. Develop strategies for both action and analysis. Risk is dynamic. The very presence of police in an
area changes the risk calculation for both motivated offenders and potential victims. Utilize analysts to
consider opportunities for tactical applications of intervention strategies – e.g., at specific times or
particular places within the target areas. Pull intel from a variety of sources to interpret the relevance of
risk factors at risky places and at certain times. Consider the possibility that different policing actions
can have the best effects in some places, but not others, depending on the nuances of these places
and their situational contexts.
4. Actions that are done in a target area should clearly relate to the intervention strategy directed at
specific risk factors.
5. Expect that risk factors may become less risky over time. The risk from landscape features should
abate over time as a result of the intervention. For this to be measurable, a clear accounting of what the
intervention actions did to reduce risk should be articulated as part of the implementation plan.
Routinely reassess the meaningfulness of target areas, risk factors, and intervention strategies – they
may need to change along with the dynamic nature of illegal behavior and crime patterns that are
responding to the intervention activities.
6. Let the IPIR inform the intervention and the intervention results inform subsequent analyses to better
the “next round” of intervention activities. Spatial or temporal lags might result in longer term impacts to
lower risk places earlier in the intervention period, while higher risk places may need more time to
respond to the point where long-term effects are measurable.
7. Plan interventions so that you can measure outcomes reliably. This is important because you’ll want
to be able to repeat the effect next time you respond to a similar problem. That should be the essence
of problem-oriented policing and risk management. If crime counts reduced and you can attribute it
directly to your intervention activities, then you not only get the credit for success, but you know what to
do next time the problem occurs. If your intervention strategy did not have the desired result, then you
have a foundation for improving it next time. Once you get it right, you can repeat it.
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