Public
Edited
Oct 2, 2024
9 stars
// loading geoblaze is as simple as this
geoblaze = require("geoblaze@1.1.0");
Use Case 1: Calculating the Population of Boston
Demographic analysis is a perfect example of how cloud optimization can speed up analysis. Gridded demographic data is often provided in large files that cover a country, large area, or the whole globe! In our case, we are going to be using a Global Human Settlement Layer COG of the whole globe and looking at calculating the population (sum of pixels) within a small hub, the city of Boston.
bostonBoundary = fetch("https://geoblaze.s3.amazonaws.com/City_of_Boston_Boundary.geojson").then(response => response.json())
GHS_POP_E2015_GLOBE_R2019A_4326_9ss_V1_0 = geoblaze.parse("https://georaster-layer-for-leaflet.s3.amazonaws.com/GHS_POP_E2015_GLOBE_R2019A_4326_9ss_V1_0.tif")
pop = geoblaze.sum(GHS_POP_E2015_GLOBE_R2019A_4326_9ss_V1_0, bostonBoundary)
how did this work?
We used HTTP Range Requests (also known as partial read requests) to get pixels from the area we want.
GeoRasterLayer = require("georaster-layer-for-leaflet")
geoblaze.stats(GHS_POP_E2015_GLOBE_R2019A_4326_9ss_V1_0, bostonBoundary)
Use Case 2: Summary Pixel Statistics
Sometimes you will want to compute statistics on a select area within an image. For example, this is an image from Planet covering Rosenberg, Texas near Houston after Hurricane Harvey. In this example, we will compute statistics for the downtown area of Rosenberg.
// load the Cloud Optimized GeoTIFF
hurricane_harvey_cog = geoblaze.parse("https://storage.googleapis.com/pdd-stac/disasters/hurricane-harvey/0831/20170831_172754_101c_3b_Visual.tif")
hurricane_harvey_cog.projection
This Cloud Optimized GeoTIFF uses a Universal Transverse Mercator (UTM) Projection! GeoBlaze supports analysis on COGs of all standard projections. It does this by re-projecting the given AOI geometry to the projection system of the COG.
aoi = ({ "type": "FeatureCollection", "features": [ { "type": "Feature", "properties": {}, "geometry": { "type": "Polygon", "coordinates": [ [ [ -95.82026481628418, 29.554494451851024 ], [ -95.79082489013672, 29.554494451851024 ], [ -95.79082489013672, 29.560467315000032 ], [ -95.82026481628418, 29.560467315000032 ], [ -95.82026481628418, 29.554494451851024 ] ] ] } } ] })
{
const container = DOM.element('div', { style: `width:${width}px; height:350px` })
const map = L.map(container, { scrollWheelZoom: false });
const aoiLayer = L.geoJson(aoi).addTo(map);
L.tileLayer('http://{s}.tile.osm.org/{z}/{x}/{y}.png', { attribution: '© <a href="http://osm.org/copyright">OpenStreetMap</a> contributors' }).addTo(map);
const lyr = new GeoRasterLayer({ georaster: hurricane_harvey_cog, resolution: 256 }).addTo(map);
yield container;
map.fitBounds(aoiLayer.getBounds());
map.setZoom(map.getZoom() - 1)
}
geoblaze.stats(hurricane_harvey_cog, { srs: 4326, geometry: aoi })
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.
