Meta-visualization framework for spatiotemporal analytics: From data generation to advanced visualization on maps

This study introduces a modular, end-to-end framework for enhancing map-based decision-making. The proposal operates both as a standalone pipeline and as a complementary “glue layer” for existing Geographic Information Systems (GIS). The framework addresses key limitations in GIS, including accessibility, cost, and unconventional data integration such as digital map scans and IoT streams. It comprises three core phases: (1) automated data acquisition and preparation, leveraging advanced techniques to extract geospatial insights from map scans and contextual information enrichment with open data and IoT sensors; (2) flexible integration with diverse systems (e.g. GIS, or relational data warehouses); and (3) interactive map-based visualization enabling spatiotemporal analysis via layered meta-visualization. The framework aims to visually and functionally enrich maps by integrating embedded insights from multimedia data sources, and displaying temporal variations and dynamic geographic characteristics on the map. By unifying heterogenous data into dynamic, interactive maps, the framework reveals hidden spatiotemporal relationships and supports actionable insights through intuitive navigation. A proof-of-concept in precision agriculture demonstrates the utility of the proposal by integrating heterogeneous data—including soil maps, climate records, and real-time sensors—to generate actionable crop recommendations. The case study highlights the framework’s applicability and potential to improve decision-making accuracy and efficiency by bridging gaps between complex data and diverse user roles, from policymakers to field workers. Beyond Agriculture, the modular design enables seamless adaptation across domains and roles. By democratizing access to advanced geospatial analytics, the framework overcomes traditional GIS limitations, offering scalable, user-centric solutions for complex spatial challenges.