Vanuatu

Authors: David Newhouse, Andres Chamorro

This notebook presents the results of a poverty maping exercise done for Vanuatu, at the council level. The main objective is to assess whether small area estimation with geospatial features can be a feasible alternative to the traditional method that relies on a census.

Data Sources

• National Sustainable Development Baseline Survey (2019-2020)

• Population Grid (World Pop 2020)

• Geospatial Features (list below)

	Description	Source
0	Population and demographic structure	Worldpop unconstrained
1	Elevation, DEM, 30 meters	COPERNICUS 2010
2	Distance to any road	Open Street Map
3	Land cover shares	ESA Worldcover 10m 2020
4	Built-up area	World Settlement Footprint 2019
5	Nighttime ligths 2020 composite	VIIRS
6	Electrification rate	High Resolution Energy Access 2019
7	Building heights	WSF 3D 2019
8	Count of buildings	Open Street Map
9	Count of cell towers	Open Cell ID
10	Precipitation	CHIRPS
11	Temperature	TerraClimate (4km)
12	Drougth Index	TerraClimate (4km)
13	NDVI	MODIS (250m)

Methodology

Fist, we create a uniform grid to aggregate population and the various geospatial statistics. We use the H3 hexabin at level 6, for which each grid cell is roughly equivalent to 36 sq. km.

The map below shows the grid with a subset of statistics.

Show code cell source Hide code cell source

grid = gpd.read_file(join(out_dir, "h3_res7.geojson"))
stats = pd.read_csv(join(out_dir, "david", "grid_constrained.csv"))
stats.cropland = stats.cropland*100

grid = grid.merge(stats, on='hex_id', how='inner')
m = grid.explore(
    column='pop_sum',
    tooltip="pop_sum",
    cmap="YlGnBu",
    scheme='naturalbreaks',
    legend_kwds=dict(colorbar=True, caption='Population', interval=True),
    name="Population, World Pop"
    # tiles="Stamen Terrain"
    )

m = grid.explore(
    m = m,
    column='cropland',
    tooltip="cropland",
    cmap="Greens",
    scheme='naturalbreaks',
    legend_kwds=dict(colorbar=True, caption='% Cropland', interval=True, fmt="{:.0%}"),
    name="Cropland"
    )

m = grid.explore(
    m = m,
    column='viirs_sum',
    tooltip="viirs_sum",
    cmap="viridis",
    scheme='naturalbreaks',
    legend_kwds=dict(colorbar=True, caption='Sum of Lights', interval=True),
    name="Nighttime Ligths"
    )

m = grid.explore(
    m = m,
    column='building_heights_mean',
    tooltip="building_heights_mean",
    cmap="Reds",
    scheme='naturalbreaks',
    legend_kwds=dict(colorbar=True, caption='Average Building Height', interval=True),
    name="Building Heights"
    )

flm.LayerControl('topright', collapsed = False).add_to(m)
m

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