Inspiration
Caixa Enginyers seeks to expand responsibly by combining financial growth with social equity. The idea was to design a data-driven tool that identifies municipalities offering both economic potential and social need, balancing profitability with inclusion.
What it does
AFORA – Pila AAA+ computes a composite score for every Spanish municipality based on social, demographic, and economic indicators. It quantifies trade-offs between profitability (e.g., household income, business density) and social awareness (e.g., aging population, education, local employment). The result is a ranked map guiding optimal office expansion.
How we built it
We gathered open datasets from INE and the Spanish Tax Agency, cleaned and normalized indicators, and applied multi-criteria statistical modeling. Weighting schemes and Pareto optimization define a trade-off curve between economic and social objectives. A Python pipeline outputs visual heatmaps and ranked recommendations.
Challenges we ran into
Data heterogeneity across sources required careful normalization and matching by municipality codes. Defining a fair trade-off between social and financial priorities demanded iterative calibration and stakeholder feedback.
Accomplishments that we're proud of
We built an interpretable scoring model grounded in transparent mathematics. The tool integrates geospatial and socioeconomic data into a unified visualization that supports evidence-based decision making.
What we learned
Balancing competing objectives requires both statistical rigor and ethical awareness. We learned how to quantify fairness and opportunity in regional planning through accessible analytics.
What’s next for AFORA – Pila AAA+
Next steps include integrating real-time financial indicators, refining the scoring through reinforcement learning, and deploying a web dashboard for continuous planning and scenario simulation.
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