WAVE — Workforce Analytics & Vulnerability Engine

Track

Civic Engagement & Policy

Overview

WAVE (Workforce Analytics & Vulnerability Engine) is a tract-level predictive policy platform designed to prevent economic displacement caused by climate shocks in Santa Barbara County.

Disaster response traditionally focuses on physical infrastructure. WAVE focuses on human infrastructure. We model how floods, storm surges, and wildfires disrupt workforce stability, quantify which communities are most vulnerable to labor displacement, and simulate targeted funding strategies that accelerate recovery.

Our system enables policymakers to act before temporary disruption becomes permanent economic leakage.

The Problem: Economic Leakage After Climate Shocks

Climate events do not only damage buildings. They destabilize local labor markets.

Workers in climate-sensitive industries—especially in marginalized communities—face structural barriers such as:

Poverty
Limited English proficiency
Flood exposure
Industry concentration risk

These barriers increase what we model as social friction, turning short-term income loss into permanent relocation.

Today, local governments lack tract-level tools to:

Quantify workforce displacement risk
Measure recovery inequality across communities
Simulate intervention strategies before displacement occurs

WAVE provides that missing decision-support layer.

Our Solution

WAVE is a predictive resilience engine that integrates environmental exposure, workforce mobility, and social vulnerability into a unified mathematical system.

The platform produces:

Tract-level vulnerability scores (0–100)
Displacement probability estimates
Labor-state transition modeling
Recovery trajectory simulations
Policy optimization recommendations

Instead of reacting after workers leave, WAVE predicts and prevents displacement.

Technical Architecture

1. Data Fusion Engine

We ingest and align high-resolution datasets:

Live Data Technologies API — real workforce career histories and transitions
NOAA Station 9411340 — tide levels and environmental shock signals
EPA EJScreen — 11 environmental justice indicators
FEMA Flood Zones — structural exposure data
U.S. Census Tracts — demographic overlays

All data are harmonized spatially (tract-level) and temporally (shock windows).

RapidFire AI Classification (Sponsor Tool)

Using RapidFire AI, we built a parallelized job-title classification pipeline to convert unstructured workforce data into structured climate exposure metrics.

Our system:

Shards thousands of job titles across concurrent threads

Uses LLM-based prompt ensembles with consensus voting

Classifies roles into coastal-dependent, climate-resilient, or transitional sectors

This transforms raw career histories into tract-level climate sensitivity indicators, enabling real-time exposure quantification across industries.

RapidFire allows us to scale classification efficiently while maintaining ensemble robustness and consistency.

3. Predictive Displacement Modeling

We trained an XGBoost classifier to estimate worker flight probability under shock conditions.

Key features include:

Career velocity
Industry volatility
Coastal job dependency
EJ burden indicators
Flood zone exposure

Predictions are aggregated to produce tract-level displacement risk scores.

4. Markov Labor-State Modeling

We implemented a 4x4 Markov transition matrix modeling movement between:

Coastal employment
Inland employment
Unemployment
Transitional states

Shock severity dynamically modifies transition probabilities to simulate workforce migration patterns.

5. The Mathematical Core

We model labor recovery using a logistic growth ordinary differential equation:

$$ \frac{dL}{dt} = rL \left( 1 - \frac{L}{K} \right) - \beta(EJ) $$

Where:

( L ) = labor force participation level
( r ) = intrinsic recovery rate
( K ) = sustainable employment carrying capacity
( \beta(EJ) ) = friction coefficient derived from EJScreen percentiles

The term ( \beta(EJ) ) formalizes systemic inequality as measurable friction. Higher environmental justice burden increases recovery drag and slows labor stabilization.

This mathematically demonstrates that recovery speed is a function of equity.