Nuclear Event Simulation

This is a quick and dirty React experiment which models the loss of life for a given area based on NASA SEDAC population data from 2020. This was produced as an exploration of data and interactivity with maps for professional purposes and should not be interpreted as nefarious in any way.

Usage

To drop a bomb hold ctrl, then click and drag. Release at the desired radius.

Build / Run:

You'll need your own MapBox token.

• sign up at MapBox
• go to the Account page
• look under Access Tokens and copy the token
• create a file in the root of this repo called .env.local

The contents of env.local should look like:

REACT_APP_TOKEN=<your token here>

Then run:

• npm install
• npm run start

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FIXME:

• distance calculation does not respect zoom level

TODO:

• animate detonations
• implement API (users, track detonations, load GeoTIFF layers from S3)
• implement realtime (websockets) multi-user interactivity
• implement weather effects modeling (radition / fallout)
• implement water system contamination modeling
• implement electrical grid disruption modeling
• implement supply chain disruption modeling (air / rail / road)
• implement agricultural output impact modeling
• implement defensive response time / distance modeling (military, police?)
• implement emergency response time / distance modeling (FEMA)

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Sources and Notes:

Libraries

• React, Redux, etc.
• Leaflet, Leaflet React for mapping
• Tuft.js for GeoJSON geometry
• GeoTIFF (geoblaze, georaster) for population data mapping
• D3, mostly for color and opacity scales

Blast Calculations

Extracted from the weapons effect simulation page on MIT Nuclear Weapons Education Project site.

Population Data

Included under ./public.

• Format GeoTIFF, WGS84 (Lat / Lng)
• Year 2020
• Resolution 2.5 min
• Source NASA SEDAC, Gridded Population of the World (GPW), v4
• URL https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download

Converting GeoTIFFs to Cloud Optimized TIFF (cog):

In almost all cases, you'll want to do this with a new GeoTIFF. These files can contain a massive amount of data. Optimization significantly improves render time across zoom levels.

docker run --rm \
  -v $PWD/public:/public osgeo/gdal:alpine-small-latest \
    gdal_translate \
      /public/gpw_v4_population_count_rev11_2020_2pt5_min.tif \
      /public/gpw_v4_population_count_rev11_2020_2pt5_min.cog.tif \
      -co TILED=yes -co COMPRESS=DEFLATE

docker run --rm \
  -v $PWD/public:/public osgeo/gdal:alpine-small-latest \
    gdaladdo -r average \
      /public/gpw_v4_population_count_rev11_2020_2pt5_min.cog.tif 2 4 8 16 32

License

MIT