Assignment 2 Semester 1
Unique No: 279370
Due 7 April 2026
, UNISA | Criminology & Policing | Crime Analysis Assignment
CRIME ANALYSIS, POLICING STRATEGIES, AND INTELLIGENCE
GIS, Crime Pattern Analysis, Problem-Solving Policing & Crime Threat Analysis
1.1 Geographic Information Systems (GIS) and Crime Pattern Analysis (CPA)
1.1.1 Information Provided by Geographic Information Systems (GIS)
Geographic Information Systems (GIS) are powerful digital mapping and spatial analysis
tools that allow law enforcement agencies to visualise, capture, store, analyse, and manage
crime-related data in a geographic context (Boba Santos, 2013). GIS integrates data layers
to produce detailed crime maps that inform policing decisions. According to Chainey and
Ratcliffe (2005), GIS enables police analysts to understand not just where crimes occur,
but also why certain areas are hotspots.
GIS documents can provide the following categories of information:
(a) Spatial Distribution and Location of Crime
GIS documents precisely map where crimes are occurring within a jurisdiction. Using
geocoded data, analysts can pinpoint crime locations on a map down to street level. For
example, a series of residential burglaries in Pretoria's east suburbs can be plotted to reveal
that 80% cluster within a 500-metre radius of a specific shopping mall, suggesting a target-
rich environment (Ratcliffe, 2004). This spatial distribution information helps commanders
deploy patrol resources where they are most needed.
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, UNISA | Criminology & Policing | Crime Analysis Assignment
(b) Crime Hotspots
One of GIS's most valuable functions is the identification of crime hotspots — geographic
areas that experience a disproportionately high volume of criminal activity. Kernel density
estimation (KDE) and point mapping are techniques GIS uses to visually represent hotspot
areas (Chainey & Ratcliffe, 2005). For example, a GIS analysis of assault data in
Johannesburg's inner city might reveal a consistent hotspot around taxi ranks between 22:00
and 02:00, enabling proactive late-night patrols specifically in those areas.
(c) Temporal-Spatial Patterns
GIS documents can combine time data with location data to reveal when and where crimes
cluster. Time-series mapping shows shifts in hotspots over days, weeks, or seasons. For
example, a GIS map might reveal that vehicle thefts shift geographically from residential
areas during weekdays to entertainment district parking lots on weekends. This type of
temporal-spatial information assists in forecasting where crimes are likely to occur next
(Santos, 2016).
(d) Environmental and Physical Features
GIS documents overlay crime data with environmental features such as land use, road
networks, public transport routes, schools, parks, liquor outlets, and poorly lit areas
(Clarke & Eck, 2005). This is particularly useful in applying Crime Prevention Through
Environmental Design (CPTED) principles. For example, GIS analysis may show a
correlation between unlicensed liquor outlets and assault hotspots, informing both policing
and municipal licensing decisions.
(e) Repeat Victimisation Patterns
GIS identifies repeat victimisation — properties, businesses, or individuals targeted
multiple times — and maps these concentrations geographically (Farrell & Pease, 2017).
For example, a particular ATM location that appears repeatedly on a GIS robbery map
signals a high-risk target that warrants protective intervention.
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