AI Applications for the Demographic Challenge

Artificial intelligence offers powerful tools to understand, forecast, and respond
to demographic change in rural areas. Key application domains include:

1. Population Forecasting
Machine learning models trained on historical census data, migration flows, and
socioeconomic indicators can forecast municipal-level population trajectories with
higher accuracy than traditional cohort-component methods. Random forests and
gradient boosting approaches have demonstrated 15–30% lower prediction error
compared to linear extrapolation over 10-year horizons.

2. Service Accessibility Analysis
Geospatial AI can identify communities at risk of becoming 'service deserts' by
modelling travel times to hospitals, schools, and commercial services under
different depopulation scenarios. Graph neural networks applied to road networks
help planners optimise the location of shared service hubs.

3. Natural Language Processing for Policy Analysis
Large language models and information extraction pipelines allow automated
analysis of municipal regulations, land registries, and demographic reports,
helping researchers quickly synthesise evidence across hundreds of documents.

4. Elderly Care Technology
Conversational AI agents provide companionship and health monitoring for isolated
elderly residents. Computer vision systems detect falls and unusual activity
patterns, triggering alerts to family members or emergency services.
Wearable devices combined with ML models predict hospitalisation risk up to
72 hours in advance.

5. Causal Inference for Policy Evaluation
Difference-in-differences and synthetic control methods, implemented in Python
using CausalPy and DoWhy, allow researchers to rigorously evaluate the impact of
rural revitalisation programmes on population retention, birth rates, and
economic activity.

The DemIA Living Lab
The DemIA Living Lab at the University of Salamanca (USAL / BISITE) develops
open-source AI tools and datasets specifically for these demographic challenges,
making them freely available to researchers, municipalities, NGOs, and companies.
Templates cover topics from time-series forecasting and geospatial analysis to
LLM-based document retrieval and computer vision for rural monitoring.
