Predictive Analytics
Domain-specific prediction services for municipal operations — revenue forecasting, demand prediction, risk scoring, infrastructure degradation, and anomaly detection tuned for municipal data patterns.
Prediction Latency
Revenue MAPE
Risk Score Refresh
Anomaly Detection
Purpose-Built for Canadian Municipalities
How It Works
The identity journey, step by step
From first registration to golden record resolution — how a resident's identity evolves across the platform.
Budget Revenue Forecasting
A finance director generates multi-year revenue forecasts for the upcoming budget cycle.
How it works
The finance director opens the Predictive Analytics dashboard and selects 'Revenue Forecasting'. She chooses property tax, utility, and recreation revenue streams, sets the horizon to 5 years, and generates forecasts with 95% confidence intervals. The results show expected revenue, upper and lower bounds, and seasonal patterns. She exports the projections directly into budget management for the draft budget.
Step 1 of 5
Purpose & Scope
What this module owns
Clear ownership boundaries prevent duplication and ensure every capability has exactly one authoritative home.
Owns
8Delegated to
4Model training infrastructure
Training data
Visualization
Access control
These capabilities are handled by dedicated modules and consumed via stable API contracts — keeping boundaries clean and ownership unambiguous.
Core Capabilities
What it does
4 capability groups comprising 7 discrete capabilities — each with API surface, business rules, and data ownership.
Multi-variate time-series models for property tax, utility, recreation, parking, and transit revenue with seasonal adjustment and confidence intervals.
Multi-Variate Time-Series
Models for property tax, utility, recreation, parking, and transit revenue using Prophet, ARIMA, and LSTM algorithms.
Seasonal Adjustment
Automatic detection and adjustment for seasonal patterns, holiday effects, and cyclical trends in municipal revenue streams.
Confidence Intervals
Configurable confidence levels (90%, 95%, 99%) with upper and lower bounds for every forecast point.
Budget Integration
Direct integration with budget management for revenue projections across multiple fiscal horizons (1M, 3M, 1Y, 5Y).
Forecast 311 call volumes, permit applications, recreation registrations, transit ridership, and facility usage with holiday/event adjustments.
Service Request Forecasting
Predict 311 call volumes and service request types by time of day, day of week, and season.
Facility & Program Demand
Forecast recreation registrations, facility bookings, and transit ridership for capacity planning.
Holiday & Event Adjustments
Automatic adjustments for statutory holidays, local events, weather impacts, and seasonal patterns.
Capacity Planning
Translate demand forecasts into staffing requirements, facility availability, and resource allocation recommendations.
Real-World Scenarios
Who uses this, and how
4 persona-driven scenarios showing how Predictive Analytics works in practice — from resident registration to privacy compliance.
Finance Director
Multi-Year Tax Revenue Forecast
The finance director needs accurate 5-year property tax revenue projections for the long-term financial plan, accounting for assessment growth, appeal rates, and economic conditions.
Steps
- 1Finance director selects property tax revenue domain and 5-year horizon
- 2The model uses historical assessment rolls, appeal rates, new construction permits, and economic indicators
- 3Prophet time-series model generates forecasts with 95% confidence intervals
- 4Results show expected revenue of $142M (Year 1) to $168M (Year 5) with ±3.2% band
- 5Director creates a 'recession scenario' adjusting economic growth down 2% — sees revenue impact of -$4.8M over 5 years
- 6Exports both baseline and scenario to budget management for the long-term financial plan
Outcome
Data-driven revenue projections replace manual spreadsheet estimates. Council receives forecasts with confidence intervals and scenario comparisons for informed decision-making.
View scenario
AP Supervisor
Accounts Payable Fraud Detection
The AP supervisor uses risk scoring to identify potentially fraudulent vendor invoices before payment is released.
Steps
- 1Risk scoring model runs nightly against all pending AP transactions
- 2Model flags 3 invoices with risk scores above 0.85 (critical threshold)
- 3AP supervisor opens the risk dashboard and reviews SHAP explanations for each
- 4Invoice #1: duplicate amount and vendor, different invoice numbers — confirmed duplicate
- 5Invoice #2: vendor changed bank account 2 days before invoice — escalated for verification
- 6Invoice #3: amount 4x historical average for this vendor — approved after confirming scope change
Outcome
One duplicate invoice ($12,400) caught before payment. One suspicious bank change flagged for verification. Risk-based review replaces manual sampling, increasing detection rates while reducing review workload.
View scenario
Asset Manager
Road Rehabilitation Prioritization
An asset manager needs to prioritize 200+ road segments for rehabilitation within a constrained capital budget, optimizing lifecycle costs.
Steps
- 1Infrastructure degradation model generates PCI deterioration curves for all 847 road segments
- 2Model identifies 63 segments predicted to fall below PCI 40 (intervention threshold) within 5 years
- 3Asset manager creates scenarios: 'worst-first', 'network optimization', and 'budget-constrained ($5M/year)'
- 4Network optimization scenario shows 18% better lifecycle cost outcome than worst-first approach
- 5Manager selects the 27 highest-priority segments for Year 1 based on optimization results
- 6Exports prioritized list with cost estimates and condition projections for capital budget submission
Outcome
Data-driven prioritization optimizes $25M in road rehabilitation spending over 5 years. O.Reg. 588/17 asset management planning requirements are met with condition-based lifecycle forecasts.
View scenario
Utility Operations Manager
Water Leak Early Detection
The utility operations team uses anomaly detection to identify water main leaks before they become emergencies, reducing water loss and infrastructure damage.
Steps
- 1Anomaly detection model monitors all district metered area (DMA) flow data in real-time
- 2Model detects a sustained 15% increase in night-flow for DMA-12 over 72 hours
- 3prediction.anomaly.detected event fires with high severity and location details
- 4Operations manager reviews the anomaly — no seasonal explanation, no planned flushing, high confidence
- 5Field crew dispatched to DMA-12, locates a 6-inch main leak using acoustic detection
- 6Leak repaired within 8 hours of detection — estimated 450,000 litres of water saved
Outcome
Early leak detection prevents estimated $22,000 in water loss and avoids potential road collapse. Mean time to detection reduced from 14 days (customer complaint) to 3 days (anomaly alert).
View scenario
Internal Architecture
How it's built
4 architectural layers comprising 24 components — from API gateway to data quality engine.
4 layers · 24 total components
Predictive Analytics
Every module owns a single bounded context, exposes stable APIs, and can be composed into any Civic product — that's the architecture that scales.
Krutik Parikh
Creator of Civic
Data Model
Entity Architecture
4 entities with 5 relationships — the authoritative schema for this bounded context.
Entities
Select an entity to explore its fields and relationships
API Surface
Integration Endpoints
9 RESTful endpoints across 5 resource groups — plus 5 domain events for async integration.
/api/v1/predictions/models
List all registered prediction models
/api/v1/predictions/models/{id}/predict
Generate on-demand prediction from a model
/api/v1/predictions/models/{id}/accuracy
Get model accuracy metrics and drift status
Ecosystem
Products that depend on this module
15 Civic products consume Predictive Analytics — making it one of the most critical platform services in the ecosystem.
Property Tax
Revenue forecasting, assessment appeal risk scoring
View product →
Utility Billing
Consumption forecasting, leak detection, demand prediction
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Budget Management
Revenue/expenditure forecasting, what-if scenarios
View product →
Asset Management
Infrastructure degradation, lifecycle cost forecasting
View product →
Road & Pavement Management
PCI deterioration curves, treatment optimization
View product →
Fleet Management
Replacement scoring, demand forecasting
View product →
Stormwater Management
Flood risk, infrastructure failure probability
View product →
Fire Services
Property fire risk scoring
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Parking
Demand prediction, dynamic pricing
View product →
Transit
Ridership forecasting, route optimization
View product →
Accounts Payable
Fraud detection scoring
View product →
Recreation Management
Program demand forecasting
View product →
Analytics & BI
All predictive analytics features
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Climate & ESG
Emission projections, climate scenario modeling
View product →
Work Order & 311
Service request volume prediction
View product →
Technical Specifications
Performance, Compliance & Configuration
Prediction Latency (on-demand)
Batch Forecast Generation
Model Accuracy (Revenue)
Model Accuracy (Demand)
Risk Score Refresh
Anomaly Detection Latency
Scenario Simulation
Explainability
FAQ
Frequently Asked Questions
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