Floodlines

How Flood Risk Becomes Public Investment

Floodlines is an independent analysis of flood risk, social vulnerability, and FEMA mitigation funding across Vermont’s 250+ towns. Combining GIS, Census demographics, and federal disaster datasets, the project investigates whether mitigation dollars are reaching the communities that need them most.

Project Links

Live Dashboard

Analysis Article

GitHub Repository

I grew up in Vermont, where rivers occasionally jumped their banks during a January thaw. After Irene in 2011, there was a sense that the state had survived an extraordinary disaster together — “Vermont Strong”. When the floods returned in 2023, that feeling curdled into something more sober: no once-a-century disaster, just a recurring condition. Floodlines grew out of that emotional shift. It gave me an opportunity to combine interests in maps, environmental systems, public policy, and data storytelling while exploring a question that will only become more urgent as climate risks intensify: how do institutions decide what gets protected, and who gets left waiting?

Project Overview

After Tropical Storm Irene and Vermont’s historic 2023 floods, it became increasingly difficult to view flooding as a rare disaster rather than a structural reality. At the same time, debates over floodplain remapping, climate adaptation, and disaster recovery raised broader questions about how mitigation funding is distributed, and whether existing programs are capable of reaching communities facing future risk before major losses occur.

To explore that question, I built a reproducible geospatial analysis pipeline combining FEMA flood-risk datasets, Census demographics, mitigation funding records, and spatial analysis techniques. The resulting dashboard allows users to compare flood exposure, social vulnerability, mitigation investment, and funding gaps across every Vermont municipality.

Across multiple models and sensitivity tests, the findings were remarkably consistent: mitigation funding showed only weak alignment with forward-looking measures of risk and vulnerability, while past flood insurance claims were a substantially stronger predictor of investment.That pattern is partly structural; the largest federal mitigation programs are closely tied to disaster declarations and prior loss history. More than half of Vermont municipalities received no FEMA Hazard Mitigation Assistance funding at all.

From a technical perspective, the project demonstrates geospatial ETL workflows, spatial statistics, composite index construction, sensitivity analysis, and interactive dashboard development with Leaflet and D3. More importantly, it reflects the kind of work I most enjoy: using data, maps, and public-facing storytelling to make complex systems easier to understand and interrogate.

Tools & Technologies:

    Python • Pandas • GeoPandas • NumPy • Leaflet • D3.js • Shapely • libpysal • scikit-learn • statsmodels • OpenFEMA • ACS • GitHub Pages

Gallery



ETL workflow diagram Data sources and analytical workflow used in the study. Federal hazard, insurance, demographic, and mitigation datasets are integrated at the municipal level to estimate flood mitigation need and evaluate how funding aligns with that need. Source: AI-generated.

Default view — Quadrant map Vermont towns classified by relative need and FEMA mitigation funding. The map highlights communities that appear high need with limited investment, historically invested, aligned, or entirely absent from the funding record.

Scatter plot — Need vs. Funding Relationship between modeled need and FEMA mitigation funding. The broad scatter and weak trend suggest limited alignment between structural need and funding allocation; points shift as the risk model changes.

Need index choropleth Composite measure of flood exposure and social vulnerability. Higher-ranked communities face greater modeled need for flood mitigation investment relative to their peers.

Funding gap choropleth Difference between modeled need and received funding. Red towns indicate communities where need exceeds investment compared to the statewide average.

Town detail panel Drill-down view for individual municipalities, including funding, risk, vulnerability, rankings, and quadrant classification.

Visual metaphor for future floods exceeding past high-water marks, a church with a lower and higher waterline. A visual metaphor for the central question explored in Floodlines: when institutions measure risk through past disasters, future floods may arrive above the last high-water mark. Simple, interpretable models can serve as an early-warning tool for decision-makers. Illustration: AI-generated.

References

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