Risk Prediction and Hot Spot Analysis

Overview#

A fire chief reviewing station coverage for the coming summer notices that one district scores in the top quartile for fire risk every July, driven by a combination of old building stock, low humidity forecasts, and a spike in historical call volume during that window. The Risk Prediction domain surfaces that pattern before summer arrives, not after the first major incident. It calculates dynamic risk scores across six public safety categories, fire, EMS, traffic, crime, mental health, and natural disaster, for configurable geographic zones. Scores update in real time as weather, events, and incoming incident data shift the picture.

Key Features#

• Multi-Category Risk Assessment: Calculate risk scores across six critical public safety domains (fire, EMS, traffic, crime, mental health, natural disaster), each using specialised algorithms that account for category-specific factors, historical patterns, and environmental conditions.

• Zone-Based Geographic Analysis: Define and analyse risk across customisable geographic zones with boundary management, enabling granular risk assessment at the neighbourhood or district level.

• Historical Pattern Analysis: Use historical incident data with temporal and seasonal factor analysis to identify recurring patterns and predict future risk periods.

• Real-Time Risk Adjustments: Dynamically adjust risk scores based on current weather conditions, special events, and detected anomalies for up-to-the-minute accuracy.

• Automated Alert Generation: Receive automatic notifications when risk levels in monitored zones cross defined thresholds, enabling rapid response to emerging threats.

• Prevention Recommendations: Get AI-powered prevention recommendations with impact assessments, helping agencies take proactive measures to reduce risk before incidents occur.

• Resource Deployment Planning: Use risk intelligence to make evidence-based decisions about where to position personnel and equipment for maximum impact.

Use Cases#

Predictive risk analysis is valuable in any domain where preventing incidents is more cost-effective than responding to them. Primary industries include public safety and emergency services, urban planning and smart cities, and insurance and risk management.

• Fire Prevention: Identify high-risk zones based on building age, infrastructure condition, population density, weather, and seasonal patterns to prioritise fire inspections and station placement.

• EMS Resource Positioning: Predict EMS demand patterns to optimally position ambulances and paramedics, reducing response times for medical emergencies.

• Crime Hot Spot Analysis: Analyse historical crime patterns combined with demographic and temporal factors to identify areas requiring increased patrol presence.

• Traffic Incident Forecasting: Predict traffic incident likelihood based on road conditions, weather, time of day, and event schedules to support traffic management and safety operations.

• Natural Disaster Preparedness: Assess flood, wildfire, and severe weather risk to guide evacuation planning and emergency resource pre-positioning.

• Mental Health Crisis Response: Identify areas with elevated mental health crisis risk to ensure appropriate crisis intervention resources are available.

Risk Categories#

Integration#

The Risk Prediction domain connects with the broader platform to deliver comprehensive intelligence:

• Incident Management: Historical incident data feeds risk models
• Dispatch: Risk intelligence informs unit positioning and deployment
• Analytics: Risk trends contribute to operational dashboards
• Alert System: Elevated risk zones trigger automated notifications
• Mapping: Risk heat maps overlay on geographic displays

Open Standards#

• GeoJSON (RFC 7946): Zone boundaries are stored, queried, and returned in GeoJSON format via the boundary_geojson field, enabling interoperability with any compliant mapping client.
• WGS 84 (EPSG:4326): All geographic coordinates (zone centres, event locations) are expressed in the WGS 84 geodetic datum, and haversine great-circle distance calculations between zones and events assume this reference system.
• OASIS Common Alerting Protocol (CAP): Weather alerts consumed by the real-time risk adjustment pipeline use the NWS CAP standard severity, certainty, and urgency enumeration values to determine risk multipliers.
• GraphQL: The full query and mutation surface for risk zones, scores, alerts, and prevention recommendations is exposed as a typed GraphQL schema, consumable by any spec-compliant client.
• ISO 8601: All datetime values exchanged through the API are serialised as ISO 8601 strings, ensuring consistent time representation across clients and time zones.
• OAuth 2.0 / OpenID Connect: Access to all risk domain queries and mutations is gated by the platform's OIDC-based authentication and bearer-token authorisation layer.
• JSON: Demographic, infrastructure, and contributing-factor payloads are carried as standard JSON objects, stored as JSONB in PostgreSQL and surfaced as JSON scalars in the GraphQL schema.

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14