GIS for Crime Analysis Training Course

Participants can configure a GIS workspace and select suitable spatial data structures.

• Vector, raster and network datasets used in crime analysis
• Point, line and polygon representations for operational incidents
• Feature classes, layers, tables and relational joins
• Map projections and coordinate reference systems for local analysis
• Scale, extent and resolution effects on crime-map interpretation
• ArcGIS Pro and QGIS interface functions for analytical workflows

Participants can design a geodatabase structure that supports repeatable crime analysis.

• Incident-level data model with identifiers, timestamps and classifications
• Offence coding crosswalks between local categories and NIBRS fields
• Unique identifiers for incidents, calls, persons and locations
• One-to-many relationships between incidents and multiple offences
• Domain values and validation rules for controlled attributes
• Metadata records covering source, refresh date and known limitations

Participants can assess crime-data fitness and document limitations affecting conclusions.

• ISO 19157 data-quality dimensions applied to incident datasets
• Completeness checks for missing dates, coordinates and offence codes
• Positional accuracy assessed against verified reference locations
• Duplicate-record detection using identifiers, dates and location fields
• Reporting bias, underreporting and enforcement-generated data distortions
• Audit trail requirements for reproducible spatial analysis

Participants can geocode records, measure match quality and correct common location errors.

• Address standardisation before batch geocoding incident records
• Locator matching scores and manual review thresholds
• Intersection, landmark and postcode geocoding limitations
• Reviewing unmatched records through source-system and address checks
• Offset settings and their effects on street-segment maps
• Geocoding sensitive locations without revealing protected addresses

Participants can assemble contextual layers needed to interpret crime locations accurately.

• Administrative boundaries, beats and sectors for accountability reporting
• Road networks, transit stops and pedestrian access routes
• Land use, parcels and building footprints as environmental context
• Schools, licensed premises and critical assets as reference layers
• Population and daytime-footfall denominators for rate calculations
• Layer currency and source authority documented for operational maps

Participants can produce clear exploratory maps that support accurate pattern interpretation.

• Graduated symbols for incident counts at discrete locations
• Choropleth classification using natural breaks, quantiles and equal intervals
• Rate maps calculated with population, households or exposure measures
• Colour schemes chosen for sequential, diverging and categorical data
• Map layouts containing title, legend, scale, source and date
• Small multiples used to compare offences, areas and periods

Participants can identify and compare crime hotspots using appropriate density methods.

• Fishnet grids built at operationally meaningful cell sizes
• Kernel density estimation with documented bandwidth and cell-size choices
• Comparing density surfaces across offences and reporting periods
• Hotspot boundaries interpreted without false precision claims
• Weighted incidents incorporating harm scores or severity values
• Threshold rules for identifying priority hotspot areas

Participants can test whether observed crime concentrations differ from random spatial patterns.

• Nearest-neighbour index for point-pattern dispersion assessment
• Global Moran's I for overall spatial autocorrelation
• Local Moran's I identifying high-high and low-low clusters
• Getis-Ord Gi* statistics for statistically significant hotspots
• Spatial weights matrices based on distance, contiguity and neighbours
• P-values, z-scores and multiple-testing cautions in hotspot reporting

Participants can analyse time-based crime patterns alongside their geographic distribution.

• Time series built from daily, weekly and monthly incident counts
• Seasonality separated from long-term trend using moving averages
• Temporal aggregation chosen to match operational decision cycles
• Space-time cube analysis for emerging pattern detection
• Heat charts comparing hour, day and offence category
• Temporal windows documented to avoid misleading period comparisons

Participants can identify repeat-event patterns for targeted prevention and investigation.

• Repeat-address analysis using standardised locations and time windows
• Near-repeat patterns measured by distance and interval thresholds
• Repeat victimisation rates calculated by person, premise and asset
• Incident linkage indicators for serial offending hypotheses
• Temporal decay considered when prioritising repeat-event responses
• Prevention actions aligned with repeat-location problem mechanisms

Participants can use street and transport networks to assess movement and accessibility risks.

• Network datasets prepared with restrictions, turn rules and impedance
• Service-area analysis for patrol coverage and response reach
• Shortest-path analysis between incidents, facilities and suspect locations
• Journey-to-crime assumptions, distance decay and origin uncertainty
• Street-segment analysis for corridor crime and transit risks
• Barriers, permeability and guardianship considered in route interpretation

Participants can build a risk-terrain model linking crime patterns with place-based factors.

• Risk terrain modelling distinguishes risky places from event concentrations
• Risk factors selected through theory, evidence and local knowledge
• Distance bands and spatial influence zones for risk features
• Reclassifying factor surfaces into comparable risk values
• Weighted overlay methods for composite environmental risk scores
• Validation against holdout incidents and operational observations

Participants can combine GIS evidence with intelligence products while protecting sensitive information.

• Intelligence cycle linked to spatial collection and assessment requirements
• Entity-location-time links represented through relational data structures
• Threat, vulnerability and consequence mapped for protected assets
• Confidence statements attached to source-derived geographic assessments
• Sensitive-source handling through aggregation, masking and access controls
• Analytical indicators distinguished from confirmed operational facts

Participants can translate spatial findings into proportionate deployment and prevention options.

• Directed patrol areas selected from hotspot persistence and harm measures
• Patrol allocation compared against demand, travel time and coverage
• Problem-oriented policing responses aligned with SARA findings
• Situational crime prevention measures mapped to opportunity structures
• Before-and-after evaluation designs for targeted interventions
• Displacement and diffusion of benefits assessed spatially

Participants can communicate spatial findings clearly to operational and executive audiences.

• Analytical briefing structure: question, method, finding, implication and action
• Map annotations highlighting priorities without overstating certainty
• Executive dashboards separating indicators, alerts and explanatory analysis
• Operational recommendations assigned owners, locations and review dates
• Uncertainty statements covering data gaps, bias and analytical limits
• Briefing questions anticipated through documented method choices

Participants can apply privacy and governance controls to crime and security mapping outputs.

• Purpose limitation applied to incident, victim and offender data
• Data minimisation principles for analytical extracts and map products
• Aggregation techniques reducing re-identification risk in public reporting
• Role-based access controls for sensitive geodatabases and layers
• Retention schedules and disposal procedures for analytical datasets
• Disclosure review checks before publication or external distribution

Participants can establish a repeatable GIS workflow for routine analytical production.

• Workflow stages from data intake through briefing distribution
• Standard operating procedures for recurring hotspot products
• Quality-assurance checklist covering data, methods, maps and recommendations
• Version control conventions for geodatabases, projects and outputs
• Refresh schedules aligned with operational reporting cycles
• Peer review criteria for analytical validity and communication quality

Participants can build, review and present a complete GIS crime or security analysis briefing with defensible recommendations.

• Problem statement defining decision owner, scope and analytical question
• Cleaned incident dataset with documented geocoding and quality checks
• Context layers selected to test plausible environmental risk factors
• Hotspot, temporal and spatial-statistical outputs compared for consistency
• Operational recommendations linked to findings, constraints and responsible owners
• Peer review against evidence, privacy, cartography and briefing criteria