Econlens AI: Discovering Hidden Regional Economic Opportunities and Bottlenecks Using Geospatial Data and Explainable AI

Author

Dr. Dhakshinamoorthy T, Anusha T. S, Dharshika. S, Esakkiammal. I , Bavadharini .M


Abstract

Regional economic development is influenced by people, labour demand, businesses, infrastructure, resources, satellite observations, and external conditions. These indicators are often distributed across separate sources and may be difficult to interpret together. EconLens AI is a GIS-powered economic intelligence concept designed to move from disconnected regional indicators to an integrated view of economic opportunity and possible bottlenecks. The project presentation frames its central question as whether a region has economic, social, infrastructure, and business assets that are not translating into observed economic activity.

The proposed workflow organizes data sources, prepares and aligns regional data, constructs potential and observed-activity indicators, and supports opportunity detection, bottleneck diagnosis, forecasting, and scenario analysis. The interface concept focuses on region selection, geospatial analysis, economic insights, scenario simulation, reports, and an AI assistant. The project materials include illustrative dashboard and analytical examples; these should not be treated as verified measurements or validated model results without supporting datasets and evaluation. This paper documents the project concept, processing flow, system modules, dashboard design, opportunities, challenges, and future validation requirements.


Keywords

EconLens AI, GIS, regional economic intelligence, opportunity gap, bottleneck detection, geospatial analysis, forecasting, scenario analysis.



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References


  1. World Bank, “World Development Indicators,” World Bank Data. Available: https://databank.worldbank.org/source/world-development-indicators.
  2. Ministry of Statistics and Programme Implementation, Government of India, “Periodic Labour Force Survey (PLFS),” official publications and reports. Available: https://mospi.gov.in/.
  3. OpenStreetMap contributors, “OpenStreetMap,” collaborative geographic database. Available: https://www.openstreetmap.org/.
  4. World Bank, “World Bank Data Catalog,” geospatial and development data resources. Available: https://datacatalog.worldbank.org/.
  5. M. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, vol. 30, 2017.
  6. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  7. McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, pp. 56–61, 2010.
  8. EconLens AI project team, “EconLens AI: Discovering Hidden Regional Economic Opportunities and Bottlenecks,” supplied project presentation, HackElite, 2026.
  9. EconLens AI project website. Available: https://econlens-ai-five.vercel.app/.

 

 

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