GIS4SG
Mapování a modelování
kriminality I
podzim 2017
Petr Kubíček
kubicek@geogr.muni.cz
Laboratory on Geoinformatics and Cartography (LGC)
Institute of Geography
Masaryk University
Czech Republic
Dotazy Kanaroglou a
kol.(2005)
1. Jaká je motivace studie?
2. Jaká jsou cíle studie?
3. Čím se liší použitá metoda od předchozích
studií?
4. Jaké faktory vstupují do výpočtu?
5. Kolik monitorovacích stanic bylo umístěno?
6. Byly měřeny skutečné zplodiny (znečištění) z
dopravy?
7. Jak byl získán prvotní povrch znečištění
(pollution surface)?
8. Jaká kritéria (2) byla uplatněna?
9. Byla užita skutečná data znečištění?
GIS4SG
Dotazy Kanaroglou a
kol.(2005)
• Čím byla ovlivněna konečná L-A analýza?
• Jaká metoda byla vybrána pro analýzu?
• Jaká vzdálenost od zdroje znečištění byla
vybrána jako hraniční?
• Jak se lišily vypočtené alternativy?
Dotazy Kanaroglou a
kol.(2005)
• Co potvrdilo reálné nasazení senzorů?
• Jaké jsou podle vás slabé stránky použitého
modelu?
GIS4SG
CRIME MAPPING AND
ANALYSIS
The role of „place‟ in crime
Two key considerations (Spencer Chainey)
• Crime has an inherent geographical quality
• Crime is not randomly distributed
Crime has an inherent
geographical quality
The four dimensions of crime:
• Legal (a law must be broken).
• Victim (someone or something has to be
targeted).
• Offender (someone has to do the crime).
• Spatial (it has to happen at a place somewhere,
in space and time).
Crime is not randomly
distributed
If crimes were random:
– Equal chance of them happening anywhere at
anytime.
But crime is not randomly distributed
• Concentrated into places of activity
– Crime hotspots
• Series follow geographic patterns
– Serious and volume crime
Where it all has begun?
• From pin maps to
virtual pin maps.
• Space and time
limitations and
overlaps.
• Crime typology
problems.
Current use of GIS in police
practice
• Community policing
Major GIS Trends in Law
Enforcement
Predictive Policing
• Geographic Profiling
• Temporal patterns
• Weather
• Risk-Terrain Modelling
• Socioeconomic Indicators
• Near-Repeat Patterns
Descriptive vs. Predictive
modelling
Podstata prediktivního
modelování
• Doposud jsme se zabývali problémem, jak počítač
„vidí“ geografická data prostřednictvím popisných
(deskriptivních) technik a vytváří z nich oblasti s
určitými vlastnostmi.
• Další logický krok je použití „prediktivních –
předpovědních“ technik k vytvoření
extrapolačních map předvídajících budoucí
podmínky.
• Využití v řadě oblastí:
– Predikce kriminality.
Predictive Crime Analysis
• WHAT?
• „Predictive policing in the context of place
is the use of historical data to create a
spatiotemporal forecast of crime hot
spots.
• WHY?
• that will be the basis for police resource
allocation decisions with the expectation
that having officers at the proposed place
and time will deter or detect criminal
activity.“
Risk Terrain Modeling
Prediction
• Risk terrain modeling
(RTM) is an approach to
risk assessment in which
separate map layers
representing the influence
and intensity of a crime
risk factor at every place
throughout a geography is
created in a geographic
information system (GIS).
• Map layers are combined
to produce a composite
“risk terrain” map with
values that account for all
risk factors at every place
throughout the
geography.
• Available in PDf – ask your
lecturer 
RTM steps
1. Select an outcome event of particular interest
2. Choose a study area
3. Choose a time period
4. Obtain base maps of your study area
5. Identify aggravating and mitigating factors related to
the outcome event
6. Select particular factors to include in the RTM
7. Operationalize the spatial influence of factors to risk
map layers
8. Weight risk map layers relative to one another
9. Combine risk map layers to form a composite map
10. Finalize the risk terrain map to communicate
meaningful and actionable information.
Step 1 -2
1. Select an outcome event of particular interest
Gun shooting incidents.
2. Choose a study area on which risk terrain
maps will be created.
The Township of Irvington, NJ.
Step 3
STEP 3: Choose a time period to create risk
terrain maps for.
• Six month time period: January 1 to June 30.
• It is expected that this time period will
adequately assess the place‐based risk of
shootings during the next 6‐month time period
(July 1 to December 31).
• Data availability and comparability ?? Is it
really justifiable and valid for the Czech
Republic?
Step 4
• STEP 4: Obtain base
maps of your study
area.
• Two base maps were
obtained from Census
2000 TIGER/Line
Shapefiles:
– 1) Polygon shapefile of
the Township and
– 2) Street centerline
shapefile for the
Township.
Step 5
STEP 5: Identify aggravating and
mitigating risk factors that are related to
the outcome event.
• Three aggravating factors were identified based on
a review of empirical literature:
– dwellings of known gang members (habitual
offenders),
– locations of retail business infrastructure (bars,
strip clubs, bus stops, check cashing outlets, pawn
shops, fast food restaurants, and liquor stores),
– locations of drug arrests (places, where the police
action happened).
Step 6
• STEP 6: Select particular risk factors to
include in the risk terrain model.
• All three risk factors identified in Step 5 will be
included.
• Raw data in tabular form (i.e. Excel spreadsheets)
was provided by the Township police and the
many datasets they maintain, validate and
update regularly to support internal crime
analysis and police investigations.
• Attributes + addresses + time stamps + ??
• State of the art of the investigation including
the punishment and legal procedure.
Step 7
• STEP 7: Operationalize
risk factors to risk map
layers.
• The tabular data was
geocoded to street
centerlines of Irvington
to create point features
representing:
– the locations of gang
members‟ residences
(hiden on the map to
protect the gang
members),
– retail business
outlets,
– and drug arrests,
respectively as three
separate map layers.
Step 7a – gang member
residence
The spatial influence of the “gang members‟ residences” risk factor
was operationalized as: “Areas with greater concentrations of gang
members residing will increase the risk of those places having
shootings.” So, a density map was created from the points of gang
members‟ residences.
Step 7b - infrastructure
• The spatial influence of the “infrastructure” risk
factor was operationalized as:
• “High concentrations of bars, strip clubs, bus
stops, check cashing outlets, pawn shops, fast
food restaurants, and liquor stores will increase
the risk of those dense places having shootings.”
Step 7C – the drug arrest
the “drug arrest” risk factor was operationalized as:
• “Areas with high concentrations of drug arrests
will be at a greater risk for shootings
because these arrests create new „open turf‟ that
other drug dealers fight over to control.“
Step 7 – map density method
details
• Kernel density values were calculated
for each of the risk map layers so that
points lying near the center of a cellʹs
search area would be weighted more
heavily than those lying near the edge,
in effect smoothing the distribution of
values.
• Cells within each density map layer were
classified into four groups according
to standard deviational breaks. The
dark blue colored cells had values in the
top five percent of the distribution and
were considered the “highest risk”
places.
Step 7d – distance from
infrastructure
• The spatial influence of the “infrastructure” risk
factor was also operationalized as:
• “The distance of one block, or about 350ft
(app. 100 m), from a facility poses the greatest
risk of shootings because victims are often
targeted when arriving at or leaving the
establishment.”
7e – final operationalization
• We are only interested in knowing where places
are the most at risk for shootings, so we used a
binary‐valued schema to designate the
“highest risk” places across all four risk map
layers.
• The highest risk places of each risk map layer,
respectively, will be given a value of “1”; all other
places will be given a value of “0”.
• All risk factors are operationalized as
aggravating factors, so these values will
remain positive.
Step 7 - reclassification
Step 7 – final comparison
• We now have four (final)
risk map layers,
operationalized from three
risk factors.
• Binary reclassification – 0 – 1
• The cells of different map
layers are the same size and
were classified in a standad
way, the risk map layers can
be summed together to
form a composite risk
terrain map.
Step 8 + 9 - Inter Risk Map
Layer Weighting and CRTM
All risk map layers will carry equal weights to produce an
un‐weighted risk terrain model. It is assumed, for example,
that being in a place with a high concentration of drug arrests
poses the same risk of having a shooting as being in a place
with a high concentration of gang member residences. Unless we
know better  !!
STEP 10 - Finalize the Risk Terrain
Map to Communicate Meaningful
Information.
• Clip our risk terrain map
to the boundary of
Irvington.
• produce a final map with
shades of grey and layout.
Step 10 – make the risk count
• convert the risk terrain map from raster to vector
we can (still using the regular structure
converted to square polygons):
• count the number of shootings that actually
occur in the high‐risk areas during the
subsequent time period;
• calculate the square area of the highest risk
areas (i.e., places with a composite risk value of
3);
Step 10 – make the risk count
• Select all street segments within these areas to
inform police commanders about where patrols
might be increased.
• Operationalise the command and controll on the
day by day basis.
RTM validation
• Comparison with the
subsequent time
period (June 1 –
December 31) – high
risk RTM classes and
hot spot analysis of
actual shooting
accidents.
• About 50% (15 out of
31) of the shootings
during the subsequent
time period (July 1 to
December 31)
happened in these
high‐risk cluster areas.
Things to remeber
• Remember, risk terrain modeling is only a tool
for spatial risk assessment; it is not the solution
to crime problems.
• You (the analyst) give value and meaning to
RTM, so be innovative in your thinking about risk
factors and how risk terrain maps can be applied
to police operations.
Risk Terrain Modelling