{
"cells": [
{
"cell_type": "markdown",
"id": "d49833f7",
"metadata": {},
"source": [
"# Extract population data for Brighton & Hove from Worldpop\n",
"\n",
"**What is this?**\n",
"\n",
"This notebook demonstrates how to process WorldPop population data. Here, we will convert the data from a 100 meter raster grid into a Point layer in which the centroid of the raster cell is used as a location for the point."
]
},
{
"cell_type": "markdown",
"id": "d06f3a93",
"metadata": {},
"source": [
"## Download the data\n",
"\n",
"Download the raster dataset for UK from: https://hub.worldpop.org/geodata/summary?id=49903"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1e96e490",
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import rioxarray as rx\n",
"import geopandas as gpd\n",
"from shapely.geometry import box"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "0fc1d687",
"metadata": {},
"outputs": [],
"source": [
"# Read raster data (i.e. the one downloaded from Worldpop hub) - Notice you need to download the data for this to work!\n",
"fp = \"data/Brighton/gbr_ppp_2020_constrained.tif\"\n",
"data = rx.open_rasterio(fp).squeeze(\"band\", drop=True).to_dataset(name=\"population\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cba8c79a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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