Dashboard
Monitor crime analysis patterns and system performance
Cases Processed
1,247
+12.5%Connections Found
89
+23.1%Accuracy Rate
94.2%
+1.3%Recent Activity
New connection detected
Vehicle pattern match in Stuart Park cases
System update completed
Enhanced entity extraction deployed
Analyst report generated
Weekly insights summary ready
Performance Metrics
Entity Extraction
Police Report Input
Extracted Entities
{
"crime_type": "Unlawful Entry",
"location": "Stuart Park",
"entry_method": "Jemmy window",
"stolen_property": [
"Makita drill set",
"Vintage watch"
],
"vehicle_involved": {
"type": "Toyota Hilux",
"color": "white",
"feature": "Dented tailgate"
},
"confidence_score": 0.92
}
Entity Schema Configuration
Crime Details
- • Crime Type
- • Date/Time
- • Location
- • Severity Level
Methods & Tools
- • Entry Method
- • Tools Used
- • Weapon Type
- • Escape Method
Property & Vehicles
- • Stolen Items
- • Vehicle Make/Model
- • Vehicle Features
- • Property Value
Persons Involved
- • Suspect Description
- • Witness Accounts
- • Victim Type
- • Relationship
Extraction Performance
Schema Design
Entity Extraction Schema
Comprehensive blueprint for extracting structured data from unstructured police narratives
Core Entities
- • Crime Type
- • Location
- • Date/Time
- • Entry Method
Property & Vehicles
- • Stolen Items
- • Vehicle Details
- • Property Value
- • Description
Persons Involved
- • Suspect Description
- • Witness Accounts
- • Victim Type
- • Relationship
Schema Blueprint
Entity Relationships
CASE → LOCATION
One-to-many relationship between crime cases and their locations
CASE → VEHICLE
Many-to-one relationship linking cases to vehicles involved
CASE → PROPERTY
One-to-many relationship for stolen items per case
Detailed Entity Schema
Crime Entity
Location Entity
Schema Evolution Timeline
Initial Schema Design
Basic crime entities and relationships
Enhanced Entity Types
Added vehicle and property entities
Advanced Relationships
Complex person and temporal relationships
Schema Validation Rules
Required Fields
- • crime_type (non-empty string)
- • location.suburb (valid suburb name)
- • timestamp (valid ISO format)
- • entry_method (controlled vocabulary)
Validation Examples
- • Vehicle plates: [A-Z]{3}\d{3}
- • Phone numbers: \d{4} \d{3} \d{3}
- • Coordinates: -?\d+\.\d+,\s*-?\d+\.\d+
- • Postcodes: \d{4}
Schema Builder Interface
Database Model
Neo4j Graph Database Architecture
Leveraging graph databases for complex relationship discovery in crime data
Graph Structure
Nodes
- • Crime (primary entity)
- • Location (geographic)
- • Vehicle (make/model/features)
- • Property (stolen items)
- • Person (suspects/witnesses)
- • Method (entry techniques)
Relationships
- • CRIME_OCCURS_IN → Location
- • CRIME_INVOLVES → Vehicle
- • CRIME_TARGETS → Property
- • PERSON_INVOLVED_IN → Crime
- • SIMILAR_METHOD → Method
Network Visualization
Cypher Query Examples
Find Similar Crimes
MATCH (c:Crime)-[:CRIME_OCCURS_IN]->(l:Location)
WHERE l.suburb = "Stuart Park"
AND c.type = "Unlawful Entry"
RETURN c, l
LIMIT 10Vehicle Pattern Detection
MATCH (c:Crime)-[:CRIME_INVOLVES]->(v:Vehicle)
WHERE v.make = "Toyota"
AND v.model = "Hilux"
RETURN v, count(c) as crime_count
ORDER BY crime_count DESCComplex Relationship Query
MATCH (c1:Crime)-[:SIMILAR_METHOD]->(c2:Crime)
MATCH (c1)-[:CRIME_INVOLVES]->(v:Vehicle)
MATCH (c2)-[:CRIME_INVOLVES]->(v)
RETURN c1, c2, v
WHERE v.feature = "Dented tailgate"Query Performance
Storage Efficiency
Scalability
Graph Database vs Traditional SQL
Neo4j Advantages
- ✓ Native graph storage for complex relationships
- ✓ Pattern matching with Cypher query language
- ✓ Real-time traversal of connected data
- ✓ Flexible schema evolution
Traditional SQL Limitations
- ✗ Expensive JOIN operations for relationships
- ✗ Fixed schema constraints
- ✗ Poor performance on deep traversals
- ✗ Complex queries for pattern detection
Ethical Framework
Ethical AI Governance
Ensuring responsible use of AI in law enforcement intelligence
Privacy & Anonymity
- • Complete PII removal
- • Hash-based identifiers
- • No biometric data
- • Secure data handling
Bias Prevention
- • Diverse training data
- • Regular bias audits
- • Fairness metrics tracking
- • Human oversight required
Transparency
- • Explainable AI decisions
- • Audit trail maintenance
- • Open documentation
- • Regular ethics reviews
Ethical Guidelines
Data Governance
Anonymization Protocol
- • Remove all personal identifiers
- • Replace with hash-based IDs
- • Strip biometric data
- • Aggregate sensitive locations
Access Controls
- • Role-based permissions
- • Multi-factor authentication
- • Session timeouts
- • Audit logging
Usage Restrictions
Prohibited Uses
- • Facial recognition
- • Predictive policing
- • Individual targeting
- • Discriminatory profiling
Permitted Uses
- • Crime pattern analysis
- • Intelligence gathering
- • Investigative support
- • Resource allocation
Compliance Framework
Monitoring Dashboard
Ethical Review Process
Initial Assessment
Ethics board review
Bias Testing
Algorithmic fairness
Impact Analysis
Community review
Ongoing Monitoring
Continuous oversight
Case Studies
Impact Metrics
Case Study 1: Residential Serial Break-ins
Challenge
12 break-ins across Darwin suburbs over 6 months with similar modus operandi but no apparent connections.
Solution
Analyzed crime narratives to extract common patterns including entry methods, stolen items, and vehicle descriptions.
Results
- • Identified 8 linked crimes
- • Recovered $45,000 in stolen property
- • 1 arrest made
- • 34% reduction in similar crimes
Key Insights
Case Study 2: Vehicle Theft Ring
Challenge
15 vehicle thefts reported across 3 suburbs with similar vehicle description patterns.
Solution
Connected vehicle features across reports to identify common suspect vehicle.
Results
- • Unlocked 23 related crimes
- • Identified 2 suspects
- • Recovered 12 stolen vehicles
- • 41% increase in recovery rate
Breakthrough Discovery
Linked previously unrelated cases through shared vehicle features, revealing a coordinated theft ring spanning 4 months.
Case Study 3: Coordinated Property Crimes
Challenge
Complex property crimes involving multiple locations and modus operandi variations.
Solution
Analyzed temporal patterns and property types to identify coordinated criminal activity.
Results
- • Connected 7 separate incidents
- • Prevented 3 additional crimes
- • 28% faster case resolution
- • $67,000 property recovered
