Real-Time Crime Analytics Platform: 28% Crime Reduction, 99.9% Uptime
Challenge: Reactive policing with no pattern analysis—officers lacked real-time intelligence on hotspots and emerging crime series.
Solution: AI-powered real-time crime mapping with predictive analytics, integrated with CAD/RMS and mobile dashboards for field deployment.
28%
Crime reduction
99.9%
Uptime
100K+
Incidents/year
Zero
Security breaches
The Challenge
A metropolitan police department covering 15 jurisdictions and serving 2 million residents faced a familiar problem: policing by reaction, not intelligence. Crime data lived in CAD and RMS systems but wasn't analyzable in real time. Hotspot identification took weeks. Pattern detection—linking a string of burglaries or auto thefts—relied on seasoned analysts poring over spreadsheets. By the time insights reached patrol officers, the patterns had often shifted.
The department needed to move from reactive to proactive. They wanted officers to know, in the field, where risk was highest and what patterns were emerging. They needed a system that could ingest incident data, apply analytics, and surface actionable intelligence—without adding administrative burden or compromising CJIS compliance.
- •No real-time crime mapping—analysts produced static maps days or weeks after incidents
- •Manual pattern detection couldn't keep pace with volume (100K+ incidents annually)
- •Field officers had no access to hotspot or trend intelligence during patrol
- •Legacy systems couldn't support predictive analytics or machine learning
LTK Soft was selected for our 16 years of law enforcement experience, CJIS compliance track record, and proven work building crime mapping and analytics systems. We understood the data flows, security requirements, and operational realities of a multi-jurisdiction agency.
Our Solution
We built a real-time crime analytics platform that ingests incident data from CAD/RMS, applies predictive models and pattern detection, and delivers GIS-based dashboards to analysts and field officers via web and mobile. The architecture was designed for CJIS from day one—encrypted, audited, and hosted on AWS GovCloud.
Phase 1: Data Integration
API integration with CAD and RMS. Automated extraction of incident type, location, time, and related fields. Geo-coding and normalization for analytics.
Phase 2: Real-Time Analytics Engine
Streaming pipeline for near-real-time updates. Hotspot identification (kernel density). Temporal pattern analysis. Series detection for linked incidents.
Phase 3: Predictive Models
ML models trained on historical data to predict high-risk areas and time windows. Proactive deployment recommendations with confidence scores.
Phase 4: Dashboards & Mobile
Web dashboards for analysts. Mobile-friendly views for field officers. Role-based access. Audit trail for every query.
Technologies implemented: Python, TensorFlow, PostGIS, AWS GovCloud, React, PostgreSQL.
Timeline: 12 months from discovery to production, including security accreditation and user training.
Implementation Highlights
- PostGIS for spatial analytics—heatmaps, clustering, and route analysis at scale with sub-second query times.
- Predictive model tuned for property crime hotspots; validated on holdout data before deployment.
- Mobile-first design so officers could check hotspots and trends from patrol vehicles without VPN complexity.
- Zero security breaches in 16 years of CJIS work—security was designed in, not bolted on.
Results & Impact
| Metric | Before | After |
|---|---|---|
| Crime in identified hotspots | Baseline | 28% reduction |
| System uptime | N/A | 99.9% |
| Incidents analyzed annually | Manual sampling | 100K+ |
| Time to actionable intelligence | Days/weeks | Near real-time |
Business impact: Proactive deployment in hotspots drove measurable crime reduction. Officers had intelligence when it mattered. Analysts spent less time on manual mapping and more on strategic work. The platform became a mission-critical tool—and it had to be reliable: 99.9% uptime over three years in production.
"We went from flying blind to having a clear picture of where and when to deploy. The impact on public safety has been significant." — Command Staff (agency name confidential)
What They Got
- Real-time crime mapping with hotspot identification and temporal analysis
- Predictive analytics engine for high-risk areas and time windows
- Automated pattern detection for series crimes (burglary, auto theft)
- Web dashboards for analysts with drill-down and export
- Mobile-accessible views for field officers
- Integration with CAD/RMS for automated data ingestion
- CJIS-compliant infrastructure with full audit trails
Technologies Used
Full tech stack: Python (TensorFlow, scikit-learn), PostGIS (spatial analytics), AWS GovCloud (EC2, RDS, S3), React (dashboards), PostgreSQL (incident and model data).
PostGIS was chosen for geospatial performance at scale. Python and TensorFlow enabled rapid iteration on predictive models. AWS GovCloud met CJIS hosting requirements. React delivered responsive dashboards that worked on desktops and mobile devices.
Lessons Learned
- Spatial + temporal matters. Crime patterns shift by time of day and day of week. Models that combined location and time outperformed location-only approaches.
- Real-time beats batch. Moving from weekly batch maps to near-real-time updates changed how commanders made deployment decisions.
- Trust requires reliability. 99.9% uptime wasn't luck—it was design. Redundancy, monitoring, and disaster recovery were built in from the start.
"LTK Soft understands law enforcement. They built a system that works in the real world—reliable, secure, and actionable. Our officers use it every day."
Command Staff, Metropolitan Police Department
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