Floodlines

Executive Summary

Floodlines integrates FEMA, Census, NFIP, and Vermont geospatial datasets to estimate relative flood mitigation need across Vermont municipalities. Need is modeled as a combination of flood exposure and social vulnerability. Funding alignment is evaluated by comparing FEMA mitigation investments against modeled need. The analysis finds that funding is only weakly associated with modeled structural need and more strongly associated with historical flood losses, suggesting a predominantly reactive allocation pattern.

Key Findings

• Need and funding exhibit weak correlation.
• NFIP claims are substantially more predictive of funding.
• Roughly half of Vermont municipalities received no FEMA mitigation funding.
• Findings are stable across normalization methods and model specifications.

Research Question

This analysis evaluates whether flood mitigation funding in Vermont aligns with underlying need. “Need” is treated as a normative construct (what funding should target), while observed funding patterns reflect real‑world allocation dynamics.

Data Sources

• FEMA Hazard Mitigation Assistance (HMA) – project‑level mitigation funding data
• National Flood Insurance Program (NFIP) – claims and policy data
• FEMA National Risk Index (NRI) – expected annual loss (EAL), risk, vulnerability, and resilience indicators
• U.S. Census Bureau American Community Survey (ACS) – demographic and socioeconomic variables
• FEMA National Flood Hazard Layer (NFHL) and Vermont ANR River Corridors – spatial flood exposure proxies
• U.S. Census Bureau (TIGER/Line Shapefiles) – Vermont town boundaries; base geography for aggregation

Data Processing and Integration

Spatial Data

• All spatial datasets were standardized to an equal‑area coordinate system (EPSG:5070) for accurate area calculations, then converted to WGS84 (EPSG:4326) for web mapping.
• Flood exposure was estimated by calculating the percent of each town’s land area within:
  • FEMA high‑risk flood zones (NFHL)
  • State‑defined river corridors
• NFHL coverage varies across the state; a coverage flag was included to account for incomplete mapping.

NRI Aggregation

• FEMA NRI data (census tract level) were spatially intersected with town boundaries.
• Tract values were allocated to towns using area‑weighted proportions; sliver overlaps (<1%) were removed and weights renormalized.
• Aggregated metrics include Expected Annual Loss (EAL), population, building and agricultural value, and composite risk/vulnerability/resilience indices.

ACS Socioeconomic Data

• Multiple ACS tables were cleaned and merged using GEOID identifiers.
• Key indicators were calculated per Census guidance (percent below poverty, percent elderly, percent without vehicle access).
• Margins of error (MOE) were retained and propagated where appropriate.

FEMA HMA Funding Data

• Project‑level funding was inflation‑adjusted to 2025 dollars using CPI (FRED) and filtered to flood‑related mitigation projects.
• Projects were assigned to towns using regex parsing, manual mapping, and fuzzy matching for ambiguous cases.
• Town‑level aggregates: total funding, funding per capita & per housing unit (log‑transformed), and funding by period (pre‑2011, 2011–2022, 2023+).

NFIP Claims and Policies

• Claims and policies were cleaned and matched to towns; claims were inflation‑adjusted and aggregated by town and period.
• Insurance penetration rates were calculated from active policy counts.

Final Dataset

• All datasets were merged at the town level using GEOID and town name; consistency checks ensured alignment across population and housing variables.
• The final dataset includes all Vermont towns, including those with zero funding.

Need Index Construction

A composite need index was developed to estimate relative flood mitigation need across towns.

Components

• Risk (Exposure): primary measure was Expected Annual Loss (EAL) from FEMA NRI.
• Vulnerability (Socioeconomic): percent below poverty, percent elderly, percent without vehicle access.

Method

• Variables were evaluated using both z-score and rank-based normalization; rank-based normalization was selected for all published indices.
• Risk and vulnerability composites were combined with equal weighting in models containing both components.

Rationale

EAL was selected as the primary risk variable for its multidimensional loss capture. A parsimonious set of vulnerability variables preserves interpretability while retaining signal.

Model Evaluation

Robustness Checks

• Need indices were highly consistent across normalization methods (Spearman correlation ≈ 0.91–1.00).
• Rankings were stable with modest variation at the extremes.

Sensitivity Analysis

• Leave‑One‑Variable‑Out (LOVO): tested how removing each variable affected rankings and underfunded town identification.
• Weight Variation: tested alternative weightings (e.g., 70/30, 30/70 risk vs. vulnerability).
• Key result: the model is robust to weighting; the choice of risk variable (EAL vs. exposure) is most consequential.

Model Comparison

• Specifications tested: exposure‑based models (river corridors, NFHL), EAL‑based models, per‑capita risk models, and FEMA NRI composites.
• Finding: EAL‑based models are more stable and policy‑relevant; additional variables provide limited extra explanatory power.

Normalization Choice

Rank-based normalization was used for all published indices rather than z-scores.

• Vermont flood and funding data are highly skewed, with extreme outliers compressing z-score distributions.
• Rank normalization produces bounded, interpretable values relative to peer towns.
• Variables with different units and distributions (EAL, flood exposure, socioeconomic indicators) can be compared on a common scale without distributional assumptions.
• Sensitivity testing showed minimal substantive difference between rank-normalized and z-score models; town rankings remained highly consistent across methods.

Model Selection

Three models were selected for the web analysis based on complementary strengths in predictive signal, stability, and external benchmarkability:

• EAL-based model – primary analytical model; strongest ability to predict funding access.
• EAL per capita model – robustness check; most stable across normalization methods.
• FEMA NRI model – external benchmark using FEMA’s own composite risk framework.

Funding Alignment Analysis

Gap Index

Gap = normalized need − normalized funding per capita (log-scaled). Positive values indicate underfunding relative to need; negative values indicate overfunding.

Observed: moderate positive correlation with need (~+0.32 to +0.49), indicating the index captures allocation mismatch rather than simply re‑labeling need.

Correlation Analysis

• Need vs. funding: weak correlation (~0.10–0.30).
• Claims vs. funding: stronger correlation (~0.55), suggesting funding aligns more with past damage than forward‑looking need.

Regression Analysis

• Logistic regression (funding access): AUC ≈ 0.6–0.8 (moderate ability to predict which towns receive funding).
• OLS models using log-transformed funding per capita produced near-zero explanatory power (R² ≈ 0).
• Interpretation: structural variables weakly explain who gets funding and do not explain funding amounts.

Spatial Analysis

• Moran’s I indicates significant spatial clustering of need.
• Choropleth maps confirm geographic consistency of risk patterns; funding does not exhibit the same alignment.

Quadrant Analysis

Towns were categorized by need and funding into zero funding, underfunded (high need, low funding), aligned, overfunded, and low priority groups.

• ~52% of towns received zero funding.
• High‑need towns are overrepresented among underfunded and zero‑funded groups; distribution across quadrants is consistent across model specifications.

Key Findings

• Flood mitigation funding exhibits weak alignment with modeled structural need across Vermont municipalities.
• Historical flood losses, as measured through NFIP claims, are substantially more predictive of funding allocation than forward-looking risk and vulnerability indicators.
• Approximately half of Vermont towns received no FEMA Hazard Mitigation Assistance funding, including numerous communities identified as high-need under multiple model specifications.
• The observed patterns are robust across alternative definitions of need, normalization approaches, and sensitivity analyses.

Key Limitations

• NFHL flood mapping is incomplete across Vermont (52%).
• NFIP participation is extremely low (<2% of housing units), limiting claims as a comprehensive risk proxy.
• Town‑level aggregation may obscure within‑town variation.
• Risk scores (EAL, NRI) are modeled annualized estimates, not cumulative observed damage.
• Funding data reflect approved projects only, not unmet demand or unsuccessful applications.
• Funding reflects only the federal share obligated under FEMA Hazard Mitigation Assistance (HMA) localizable to a specific town.
• County-wide, regional, and statewide mitigation projects could not be reliably attributed to individual municipalities and were excluded from town-level funding totals.

Reproducibility

All data sources used in this analysis are publicly available. Data processing, model construction, sensitivity testing, and dashboard generation scripts are available in the project repository.

Abbreviated Dashboard Data Dictionary

The interactive dashboard uses a flat town table (town_stats.csv) and a simplified town GeoJSON to power choropleths, charts, rankings, and town-level summaries. A complete field-level data dictionary is available in the project repository.

Identifiers & base attributes

• GEOID: 10‑digit Census town FIPS identifier (primary join key).
• town_name: Standardized town label used in UI.
• population: Total resident population (integer).
• area_sq_km: Town land area (km²).
• pct_river_corridor: Percent of town area inside state river corridor polygons.

Core socioeconomic & exposure fields

pct_below_poverty, percent_elderly, pct_no_vehicle, pct_renter_occupied, median_income — socioeconomic indicators used to build the vulnerability component shown in the vulnerability choropleth and stats card.
IFLD_EALT_weighted, EAL_per_capita — expected annual loss (EAL) measures used as primary exposure/risk inputs.

Vulnerability composite

vulnerability, vulnerability_rank, vulnerability_rel — composite (percentile‑rank mean of poverty, elderly, no‑vehicle) used in the vulnerability choropleth and as part of need.

Modeled indices (per‑model naming convention)

For each model suffix (examples used in export: eal, eal_per_capita, nri) the dashboard expects the following patterns:

• risk_{model} — raw (0–1) risk index for the model.
• risk_rank_{model} — percentile rank (0–1) of the risk index.
• risk_{model}_rel — relative-to-state risk (centered) used to label “above/below state average.”
• need_{model}, need_rank_{model}, need_{model}_rel — need index, rank, and relative metric.
• gap_{model}, gap_rank_{model}, gap_{model}_rel — funding gap (need − funding), its rank, and relative gap scaled to mean absolute gap.
• quadrant_{model} — categorical quadrant for need vs funding (zero_funding, underfunded, aligned, overfunded, low_priority).

Funding and NFIP fields

• funding_total: Total FEMA mitigation funding obligated to the town (inflation‑adjusted).
• funding_per_capita: Funding / population (USD/person).
• funding_rank, funding_rel: Percentile rank and log‑based relative funding metric.
• claims_paid_per_capita, claims_rank, claims_rel: NFIP claims per person plus rank and relative metric.

State summary row

The export appends a synthetic town_name = "State of Vermont" row populated with population‑weighted averages (and totals where appropriate) so the dashboard can show statewide comparisons without extra aggregation.

Provenance & Notes

• Export datasets, field definitions, and backend schemas are documented in the project repository.
• Relative metrics are generally centered as (x / x̄) − 1; gap metrics are scaled by mean absolute gap to preserve sign and interpretability.
• Percentile ranks (rank(pct=True)) are the canonical comparison metric used throughout the dashboard.