Quick start

Every snippet below loads Chile's 16 regions. Swap the URL for any dataset from the Catalog.

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LeafletMapLibrePythonQGISR

<div id="map" style="height: 480px"></div>
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
<script>
  const map = L.map("map").setView([-35, -71], 4);
  L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", {
    attribution: "&copy; OpenStreetMap contributors",
  }).addTo(map);

  const url =
    "https://raw.githubusercontent.com/andresgmg/World-GeoJSON/main/regiones.geojson";

  fetch(url)
    .then((r) => r.json())
    .then((data) => {
      const layer = L.geoJSON(data, {
        style: { weight: 1, color: "#00695c", fillOpacity: 0.15 },
        onEachFeature: (f, l) => l.bindTooltip(f.properties.Region),
      }).addTo(map);
      map.fitBounds(layer.getBounds());
    });
</script>

import maplibregl from "maplibre-gl";

const map = new maplibregl.Map({
  container: "map",
  style: "https://demotiles.maplibre.org/style.json",
  center: [-71, -35],
  zoom: 3,
});

map.on("load", () => {
  map.addSource("regions", {
    type: "geojson",
    data: "https://raw.githubusercontent.com/andresgmg/World-GeoJSON/main/regiones.geojson",
  });

  map.addLayer({
    id: "regions-fill",
    type: "fill",
    source: "regions",
    paint: { "fill-color": "#00695c", "fill-opacity": 0.15 },
  });

  map.addLayer({
    id: "regions-line",
    type: "line",
    source: "regions",
    paint: { "line-color": "#00695c", "line-width": 1 },
  });
});

import geopandas as gpd

URL = (
    "https://raw.githubusercontent.com/andresgmg/World-GeoJSON"
    "/main/regiones.geojson"
)

regions = gpd.read_file(URL)

print(len(regions))                    # 16
print(regions.crs)                     # EPSG:4326
print(regions["Region"].tolist()[:3])

# Area needs a projected CRS. Computing it in degrees is meaningless.
# EPSG:5361 (SIRGAS-Chile) is appropriate for Chile specifically.
print(regions.to_crs(5361).area / 1e6)  # km²

Without GeoPandas, the standard library is enough to inspect the file:

import json
import urllib.request

with urllib.request.urlopen(URL) as fh:
    data = json.load(fh)

for feature in data["features"]:
    print(feature["properties"]["Region"])

Do not json.load a 70 MB file casually

comunas.geojson will expand to well over a gigabyte of Python objects. Use ijson to stream it, or GeoPandas, which parses via GDAL rather than into Python dicts.

1. Layer → Add Layer → Add Vector Layer
2. Set Source Type to Protocol: HTTP(S), cloud, etc.
3. Paste the raw URL: https://raw.githubusercontent.com/andresgmg/World-GeoJSON/main/regiones.geojson
4. Click Add.

QGIS reads the CRS as EPSG:4326 automatically. To measure areas or distances, reproject to a projected CRS appropriate for your area of interest first — for Chile, EPSG:5361.

For the 70 MB communes file, download it locally rather than streaming it over HTTP; QGIS will re-request ranges repeatedly otherwise.

library(sf)

url <- paste0(
  "https://raw.githubusercontent.com/andresgmg/World-GeoJSON",
  "/main/regiones.geojson"
)

regions <- st_read(url)

nrow(regions)        # 16
st_crs(regions)      # EPSG:4326
plot(st_geometry(regions))

What you get back

A standard FeatureCollection. Chile's regions carry these properties today:

{
  "objectid": 1084,
  "cir_sena": 1,
  "codregion": 15,
  "area_km": 16866.81984442,
  "st_area_sh": 18868687743.9,
  "st_length_": 750529.550114,
  "Region": "Región de Arica y Parinacota"
}

These property names are not final

They come straight from the upstream Esri shapefile and are inconsistent — three naming styles in one object, plus export artifacts like st_area_sh. A standardised schema is defined in Property schema and will be applied as a documented breaking change. See Versioning & stability.