Advanced Analytics

Overview#

A crime analyst notices that street-level incidents spike every Thursday evening in a specific neighbourhood. To confirm the pattern, she needs to cross-tabulate incident counts with weather data, overlay a geospatial heatmap, and project the trend forward by three weeks. None of that is possible with a spreadsheet export. The Advanced Analytics module is built for exactly this kind of question.

It provides a powerful analytics engine for creating custom metrics, dashboards, and real-time calculations. Over 20 metric types cover everything from basic aggregations to time-series forecasting, geospatial clustering, entity network scoring, and custom formula evaluation. Organizations get deep operational insight without waiting for a data engineer to build a report.

Key Features#

Custom Metric Definitions: Create reusable metric calculations with flexible formulas tailored to your organisation's specific needs.
Real-Time Calculations: Execute metric calculations on demand with enterprise-grade performance for rapid decision-making.
Configurable Dashboards: Build analytics dashboards with drag-and-drop grid layouts and multiple widget types including line charts, bar charts, pie charts, tables, metric cards, and heatmaps.
Time-Series Analysis: Perform trend analysis, seasonality detection, moving averages, growth rate calculations, and forecasting across your data.
Geospatial Analytics: Generate density heatmaps, perform geographic clustering, calculate pairwise distances, and measure coverage areas for location-based data.
Entity Network Analysis: Compute network centrality scores and relationship strength between entities to identify key actors and connections.
Custom Formulas: Define and evaluate custom mathematical formulas safely, supporting arithmetic, comparisons, and aggregation functions without code injection risks.
Event Streaming: Real-time event sourcing captures domain events across cases, evidence, alerts, and entities for live analytics updates.
Historical Tracking: All calculations are stored for historical analysis, enabling trend visualisation and period-over-period comparisons.
Dashboard Sharing: Share dashboards publicly within your organisation or with specific team members for collaborative analysis.

Metric Types#

Basic Aggregations: Count, sum, average, min, max, median, percentiles (90th, 95th, 99th), and percentage calculations.
Time-Series: Trend analysis with direction and correlation strength, seasonality detection by day-of-week or month, moving averages, growth rates, and simple forecasting.
Geospatial: Density heatmaps with configurable grid size, geographic clustering for hotspot identification, average distance calculations, and coverage area measurement.
Entity Analytics: Network centrality ranking by connection count, and relationship strength scoring between entity pairs.
Custom Formulas: User-defined mathematical expressions with variable substitution and built-in functions (SUM, AVG, COUNT, MIN, MAX, ABS, ROUND).

Use Cases#

Law enforcement analysts build geospatial heatmaps of incident data to identify crime hotspots before patrol schedules are set, giving commanders evidence-based deployment decisions rather than gut instinct.

Financial crime investigators run time-series analysis on transaction volumes to detect unusual spikes that coincide with known money-laundering windows, flagging accounts for deeper review.

Intelligence agencies use entity network centrality scoring to identify the most connected individuals within a target network, prioritising surveillance resources on high-centrality nodes.

Defence and government leadership teams receive weekly executive dashboards showing case resolution rates, alert response times, and resource utilization trends, all generated automatically without analyst intervention.

Operational Performance Monitoring: Track case resolution rates, alert response times, evidence processing throughput, and other key performance indicators with custom metrics and dashboards.
Crime Pattern Analysis: Use geospatial heatmaps and clustering to identify geographic hotspots, and time-series analysis to detect temporal patterns in incident data.
Investigation Analytics: Monitor investigation progress with custom dashboards showing case status distributions, team workload metrics, and resource utilization.
Executive Reporting: Build shareable dashboards with high-level KPIs, trend lines, and forecasts for leadership briefings and stakeholder communication.

Integration#

The Advanced Analytics module connects with other Argus modules to provide cross-domain insights:

Case Management: Track case lifecycle metrics, resolution rates, and workload distribution.
Alert Management: Monitor alert volumes, escalation patterns, and response times.
Evidence Management: Analyse evidence processing rates, tagging patterns, and chain-of-custody metrics.
Entity Management: Compute network centrality and relationship strength across entity graphs.
OSINT: Track query execution rates and result caching effectiveness.

Open Standards#

GraphQL (June 2018 specification): All metric queries, dashboard mutations, and temporal score operations are exposed through a strongly typed GraphQL API, enabling introspection and interoperability with any compliant GraphQL client.
ISO 8601: All time-series period boundaries, event timestamps, and metric calculation timestamps are serialised as ISO 8601 UTC strings, ensuring unambiguous exchange of temporal data across integrations.
JSON (RFC 8259): Event payloads, widget configurations, metric metadata, and formula variable sets are all encoded as JSON, the interchange format for both internal event sourcing and API responses.
WGS 84 (EPSG:4326): Geospatial calculations, density heatmaps, DBSCAN geographic clustering, pairwise haversine distances, and bounding-box coverage areas, all operate on decimal-degree latitude/longitude coordinates referenced to the WGS 84 geodetic datum.
SQL (ISO/IEC 9075): The calculation engine executes ISO standard SQL against PostgreSQL, including standard ordered-set aggregate functions such as PERCENTILE_CONT and date-truncation functions for time-series grouping.
OAuth 2.0 (RFC 6749) / JSON Web Tokens (RFC 7519): Every query and mutation is protected by an IsAuthenticated permission class that validates JWT bearer tokens issued by the platform's OAuth 2.0 authorisation server before any metric or dashboard operation is executed.
W3C WCAG 2.2 / WAI-ARIA 1.2: Dashboard widgets (line charts, bar charts, pie charts, tables, heatmaps, metric cards) are rendered in the web front-end and must conform to WCAG 2.2 accessibility guidelines and WAI-ARIA roles to meet the platform's public-sector accessibility obligations.

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