Applying GIS, Data Vis. and Deep Learning to environmental science and nuclear emergency responses
Public
Edited
Jul 26, 2024
2 forks
// Adjust it manually based on histogram
// activityBins = [1, 10, 100, 1000, 10000] // FNDPP
activityBins = [1, 20, 1000, 10000] // FNDPP
// Adjust it based on known events, behaviours, ...
timePeriod = [1960, 2010, 2011, 2012, 2013, 2015, 2017, 2021] // FNDPP
// timePeriod = [1960, 1970, 1980, 1990, 2000, 2010, 2021] // North sea area
dataWithinRadius
data = dataRaw.map((d) => ({
area: d["areaname"],
nuclide: d["nusymbol"],
lon: d["longitude"],
lat: d["latitude"],
time: d["samplingyear"],
activity: d["activity"],
unit: d["unit"],
depth: d["sampdepth"]
}))
dataNuclide = data.filter((d) => d.nuclide == nuclide)
dataDepth = dataNuclide.filter((d) => d.depth <= depth)
minYear = d3.min(dataWithinRadius.map((d) => d.time))
maxYear = d3.max(dataWithinRadius.map((d) => d.time))
timeDomain = timePeriod.slice(1, -1)
timeRange = d3.pairs(timePeriod).map((d) => `${d[0]} - ${d[1]}`)
timeScale = d3.scaleThreshold().domain(timeDomain).range(timeRange)
timeScale(2011)
world = (
await fetch("https://cdn.jsdelivr.net/npm/world-atlas@2/land-50m.json")
).json()
land = topojson.feature(world, world.objects.land)
circle = d3
.geoCircle()
.center(center)
.radius((kms / (6371 * Math.PI * 2)) * 360)
.precision(10)()
// dataWithinRadiusTurf = dataNuclide.filter((d) =>
// turf.booleanWithin(turf.point([d.lon, d.lat]), circle)
// )
// isInCircle = geoContainsCache(circle)
// dataWithinRadius = dataNuclide.filter((d) =>
// // d3.geoContains(circle, [d.lon, d.lat])
// isInCircle([d.lon, d.lat])
// )
dataWithinRadius = dataDepth.filter(
(d) => d3.geoDistance([d.lon, d.lat], center) < kms / 6371
)
// import { geoContainsCache } from "@fil/another-hex-map"
// source: https://observablehq.com/@mkfreeman/plot-tooltip
import { Plot } from "@mkfreeman/plot-tooltip"
// simple = import("simple-statistics@7")
// turf = require("@turf/turf@6")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
