Denmark Map Plots

Name: denmark-map
Rating: 76
Author: josca42

Create choropleth maps using GeoParquet boundary files at /geo/.

Available Files

File	Level	Count	dim_kode source
`kommuner.parquet`	Kommuner	99	kode → dim.nuts.kode
`regioner.parquet`	Regioner	5	kode → dim.nuts.kode
`landsdele.parquet`	Landsdele	11	nuts3 → dim.nuts.kode
`politikredse.parquet`	Politikredse	12	kode → dim.politikredse.kode
`retskredse.parquet`	Retskredse	24	kode
`sogne.parquet`	Sogne	2,097	kode
`postnumre.parquet`	Postnumre	1,089	nr (postal code)
`storkredse.parquet`	Storkredse	10	nummer
`valgkredse.parquet`	Valgkredse	92	kode
`afstemningsomraader.parquet`	Afstemningsområder	1,315	dagi_id

Schema

Each file is a GeoParquet with:

•Index: dim_kode (int) — for kommuner/regioner/landsdele this matches dim.nuts.kode; other layers use their native ID (see table above)
•navn: Region/kommune name (str)
•geometry: Polygon boundaries in WGS84 (EPSG:4326)

Merge data with geo boundaries

python

geo = gpd.read_parquet("/geo/kommuner.parquet")
df = df[df["omrade"] != 0]  # drop whole-country aggregate
merged = geo.merge(df, left_index=True, right_on="omrade")

Plot map

Use px.choropleth_map. Pass merged.geometry as geojson and merged.index as locations — plotly matches each feature by position.

python

fig = px.choropleth_map(
    merged,
    geojson=merged.geometry,
    locations=merged.index,
    color="value_column",
    hover_name="navn",
    map_style="carto-positron",
    center={"lat": 56.0, "lon": 10.5},
    zoom=5.8,
)
fig.update_layout(margin=dict(l=0, r=0, t=40, b=0), height=550)

Denmark center: lat=56.0, lon=10.5, zoom 5.8 fits the mainland and islands. Bornholm (far east) may appear small — zoom 5.0 if it matters.