Structured Data → SQL Views → Analytical Outputs
Structured data in DIVA is transformed into analytical outputs through SQL-based views, aggregations, and relational queries, enabling consistent interpretation, comparison, and decision-oriented analysis. The analytical layer can also incorporate advanced techniques, including AI-based models, to support pattern recognition and predictive insights when relevant.
Core Analytical Functions
The analytical layer in DIVA is built on structured SQL views and relational queries that transform integrated datasets into consistent, comparable, and decision-oriented outputs.
Relational Aggregation
Data is aggregated across multiple indicator tables using relational joins, enabling cross-category analysis and integrated territorial insights.
SQL-Based Views
Reusable SQL views standardize analytical logic and queries, ensuring consistent outputs across dashboards, reports, and applications.
Comparative Analysis
Indicators are compared across sources, categories, and time dimensions through structured queries and percentage-based calculations.
Ranking and Metrics
Advanced SQL functions (e.g., window functions and ranking) enable prioritization, benchmarking, and performance evaluation of territorial indicators.
The analytical layer in DIVA transforms structured data into measurable and interpretable outputs, supporting spatial analysis, indicator monitoring, and evidence-based decision-making processes.