Project 7 — Environmental Risk Analysis¶

Goal¶

"Which neighborhoods in [your area] face the highest combined environmental risk — flood, wildfire, heat — adjusted for social vulnerability?"

This project marries physical hazard layers with social vulnerability indices (SVI) to identify the most at-risk communities.

Data needed¶

Tools used¶

• Spatial Analyst: Reclassify, Weighted Overlay, Zonal Statistics
• Spatial Join, Tabulate Intersection
• Raster Calculator for thermal band → land surface temperature

Workflow¶

Step 1 — Set up¶

1. Project: env_risk_<area>.
2. Project to State Plane.
3. Standardize all rasters to 30 m, snapped to the same raster.

Step 2 — Build the hazard score (per tract)¶

For each hazard, compute a per-tract score 1–5.

Flood score¶

1. Polygon to Raster on FEMA flood zones (assign higher value to AE/A/VE).
2. Tabulate Intersection → percent of each tract in flood zones.
3. Bin: 0% → 1, ≤5% → 2, ≤15% → 3, ≤30% → 4, >30% → 5.

Add field FLOOD_SCORE.

Wildfire score¶

1. Use the wildfire risk raster.
2. Zonal Statistics as Table — mean wildfire risk per tract.
3. Reclassify the mean into 5 bins (quintile or NRI thresholds).

Add field FIRE_SCORE.

Urban heat score¶

1. From Landsat 8 thermal band (Band 10), compute Land Surface Temperature.
2. Zonal Statistics — mean LST per tract.
3. Reclassify: cooler tracts = lower score.

Add field HEAT_SCORE.

Step 3 — Combine into HAZARD_SCORE¶

def hazard(flood, fire, heat):
    f = flood or 0
    w = fire or 0
    h = heat or 0
    return (f * 0.35 + w * 0.35 + h * 0.30)

Add field HAZARD_SCORE = hazard(!FLOOD_SCORE!, !FIRE_SCORE!, !HEAT_SCORE!).

Adjust weights for your area (e.g., heavier flood weight in coastal regions).

Step 4 — Bring in social vulnerability¶

CDC SVI is already a 0–1 score per tract. Higher = more vulnerable.

1. Join SVI to tracts on FIPS.
2. Standardize: bin into 5 quintiles, SVI_BIN 1–5.

Step 5 — Combine into RISK_SCORE¶

The classic equation:

Risk = Hazard × Vulnerability

Or weighted:

def risk(hazard, svi_bin):
    h = hazard or 0
    s = svi_bin or 1
    return h * 0.6 + s * 0.4

Add RISK_SCORE.

Step 6 — Categorize¶

def cat(risk):
    if risk is None: return "Unknown"
    if risk >= 4.0: return "Very High"
    if risk >= 3.0: return "High"
    if risk >= 2.0: return "Moderate"
    return "Low"

Add RISK_CATEGORY.

Step 7 — Map¶

1. Symbology: Unique Values on RISK_CATEGORY with a sequential reds/yellows palette.
2. Overlay each individual hazard as a small inset (3 thumbnails).
3. Highlight top 10 highest-risk tracts.
4. Build an ArcGIS Online dashboard:
   • The risk map
   • A bar chart of top tracts
   • Clickable filters by hazard
   • A KPI: "X% of metro population in High or Very High risk."

Step 8 — Story Map¶

A 5-section narrative:

1. Why combined risk matters (vs single-hazard)
2. The data and methodology
3. Each individual hazard
4. Combined risk
5. Recommendations / who acts

Skills learned¶

• Multi-hazard integration
• Zonal statistics across multiple rasters
• SVI / CDC index integration
• Hazard × vulnerability framing (industry standard)
• Cross-source ETL

Portfolio value¶

This is the climate-resilience and emergency-management portfolio piece. Cities, counties, FEMA, NGOs, and consulting firms work on this exact framing.

Stretch goals¶

• Add air quality (PurpleAir, EPA AQS) as a fourth hazard.
• Compare future climate scenarios with downloaded NCA or CMIP6 data.
• Cross-reference with HUD CDBG-DR funding to identify under-funded high-risk tracts.
• Build a model in ModelBuilder so the whole analysis runs with a single click.

🎓 You've reached the most advanced project in the roadmap. After shipping any 3 of these 7, you have a portfolio strong enough to apply for entry-level GIS roles.

→ Now: GIS Career Roadmap.