# swemaps
Maps of Sweden in [GeoParquet](https://github.com/opengeospatial/geoparquet) for easy usage.
The parquets have been created from files published by [Statistics Sweden](https://www.scb.se/hitta-statistik/regional-statistik-och-kartor/regionala-indelningar/) and [The Swedish Agency for Economic and Regional Growth](https://tillvaxtverket.se/tillvaxtverket/statistikochanalys/statistikomregionalutveckling/regionalaindelningar/faregioner.1799.html). Maps include counties, municipalities and FA regions. The original geometries have been transformed from SWEREF 99 TM to WGS 84 for better out of the box compatibility with different tools. The column names have also been somewhat sanitized (e.g. `KnKod` -> `kommun_kod`).
The package gets you the file path so that you can load it with your prefered tool, for example PyArrow or GeoPandas. An extra helper function is included to quickly convert a PyArrow table to GeoJSON.
Made for Python with inspiration from [swemaps2](https://github.com/filipwastberg/swemaps2).
## Municipalities and counties
Municipalities | Counties
:-------------------------:|:-------------------------:
 | 
### PyArrow example with Plotly
```python
>>> import pyarrow.parquet as pq
>>> import swemaps
# This loads the map for the specified type
>>> kommuner = pq.read_table(swemaps.get_path("kommun"))
>>> kommuner.column_names
['kommun_kod', 'kommun', 'geometry']
# This helper function returns GeoJSON from a PyArrow table
>>> geojson = swemaps.pyarrow_to_geojson(kommuner)
# Here's a dataframe with municipalities and some random values that we can plot
>>> df.head()
shape: (5, 2)
┌──────────┬───────┐
│ Kommun ┆ Value │
│ --- ┆ --- │
│ str ┆ i64 │
╞══════════╪═══════╡
│ Ale ┆ 544 │
│ Alingsås ┆ 749 │
│ Alvesta ┆ 771 │
│ Aneby ┆ 241 │
│ Arboga ┆ 763 │
└──────────┴───────┘
>>> fig = px.choropleth(
df,
geojson=geojson,
color="Value",
locations="Kommun",
featureidkey="properties.kommun",
projection="mercator",
color_continuous_scale="Viridis",
fitbounds="locations",
basemap_visible=False,
)
```
You could also subset the map of municipalities for a specific county or a group of counties. Since the geometry is loaded as a PyArrow table the filter operation is straightforward.
```python
>>> import pyarrow.compute as pc
>>> kommuner.schema
kommun_kod: string
kommun: string
geometry: binary
-- schema metadata --
geo: '{"version":"1.0.0","primary_column":"geometry","columns":{"geometry' + 1478
# County code for Skåne is 12
>>> kommuner = kommuner.filter(pc.starts_with(pc.field("kommun_kod"), "12"))
>>> geojson = swemaps.pyarrow_to_geojson(kommuner)
```
You could also use list comprehension on the GeoJSON to filter it.
```python
>>> geojson["features"] = [
feature
for feature in geojson["features"]
if feature["properties"]["kommun_kod"].startswith("12")
]
```
Anyway, now we can plot Skåne.
```python
>>> skane = px.choropleth(
df,
geojson=geojson,
color="Value",
locations="Kommun",
featureidkey="properties.kommun",
projection="mercator",
color_continuous_scale="Viridis",
fitbounds="locations",
basemap_visible=False,
title="Skåne municipalities"
)
fig.show()
```
## GeoPandas example
You can load the GeoParquet into a GeoDataFrame as well.
```python
>>> import geopandas as gpd
>>> gdf = gpd.GeoDataFrame.read_parquet(swemaps.get_path("lan"))
>>> gdf.head()
lan_kod lan geometry
0 01 Stockholms MULTIPOLYGON (((17.24034 59.24219, 17.28475 59...
1 03 Uppsala POLYGON ((17.36606 59.61224, 17.35475 59.60292...
2 04 Södermanlands POLYGON ((15.95815 58.96497, 15.86130 58.99856...
3 05 Östergötlands POLYGON ((14.93369 58.13112, 14.89472 58.08986...
4 06 Jönköpings POLYGON ((14.98311 57.93450, 15.00458 57.89598...
# And with matplotlib installed as well we can have quick look
>>> gdf.plot()
```
Raw data
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"description": "# swemaps\n\nMaps of Sweden in [GeoParquet](https://github.com/opengeospatial/geoparquet) for easy usage. \n\nThe parquets have been created from files published by [Statistics Sweden](https://www.scb.se/hitta-statistik/regional-statistik-och-kartor/regionala-indelningar/) and [The Swedish Agency for Economic and Regional Growth](https://tillvaxtverket.se/tillvaxtverket/statistikochanalys/statistikomregionalutveckling/regionalaindelningar/faregioner.1799.html). Maps include counties, municipalities and FA regions. The original geometries have been transformed from SWEREF 99 TM to WGS 84 for better out of the box compatibility with different tools. The column names have also been somewhat sanitized (e.g. `KnKod` -> `kommun_kod`).\n\nThe package gets you the file path so that you can load it with your prefered tool, for example PyArrow or GeoPandas. An extra helper function is included to quickly convert a PyArrow table to GeoJSON.\n\nMade for Python with inspiration from [swemaps2](https://github.com/filipwastberg/swemaps2). \n\n## Municipalities and counties\n\nMunicipalities | Counties\n:-------------------------:|:-------------------------:\n | \n\n### PyArrow example with Plotly\n\n```python\n>>> import pyarrow.parquet as pq\n>>> import swemaps\n\n# This loads the map for the specified type\n>>> kommuner = pq.read_table(swemaps.get_path(\"kommun\"))\n\n>>> kommuner.column_names\n['kommun_kod', 'kommun', 'geometry']\n\n# This helper function returns GeoJSON from a PyArrow table\n>>> geojson = swemaps.pyarrow_to_geojson(kommuner)\n\n# Here's a dataframe with municipalities and some random values that we can plot\n>>> df.head()\nshape: (5, 2)\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Kommun \u2506 Value \u2502\n\u2502 --- \u2506 --- \u2502\n\u2502 str \u2506 i64 \u2502\n\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n\u2502 Ale \u2506 544 \u2502\n\u2502 Alings\u00e5s \u2506 749 \u2502\n\u2502 Alvesta \u2506 771 \u2502\n\u2502 Aneby \u2506 241 \u2502\n\u2502 Arboga \u2506 763 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\n>>> fig = px.choropleth(\n df,\n geojson=geojson,\n color=\"Value\",\n locations=\"Kommun\",\n featureidkey=\"properties.kommun\",\n projection=\"mercator\",\n color_continuous_scale=\"Viridis\",\n fitbounds=\"locations\",\n basemap_visible=False,\n )\n\n```\n\nYou could also subset the map of municipalities for a specific county or a group of counties. Since the geometry is loaded as a PyArrow table the filter operation is straightforward.\n\n```python\n>>> import pyarrow.compute as pc\n\n>>> kommuner.schema \n\nkommun_kod: string\nkommun: string\ngeometry: binary\n-- schema metadata --\ngeo: '{\"version\":\"1.0.0\",\"primary_column\":\"geometry\",\"columns\":{\"geometry' + 1478\n\n# County code for Sk\u00e5ne is 12\n>>> kommuner = kommuner.filter(pc.starts_with(pc.field(\"kommun_kod\"), \"12\"))\n\n>>> geojson = swemaps.pyarrow_to_geojson(kommuner)\n```\n\nYou could also use list comprehension on the GeoJSON to filter it.\n\n```python\n>>> geojson[\"features\"] = [\n feature\n for feature in geojson[\"features\"]\n if feature[\"properties\"][\"kommun_kod\"].startswith(\"12\")\n ]\n```\n\nAnyway, now we can plot Sk\u00e5ne.\n```python\n>>> skane = px.choropleth(\n df,\n geojson=geojson,\n color=\"Value\",\n locations=\"Kommun\",\n featureidkey=\"properties.kommun\",\n projection=\"mercator\",\n color_continuous_scale=\"Viridis\",\n fitbounds=\"locations\",\n basemap_visible=False,\n title=\"Sk\u00e5ne municipalities\"\n )\n\nfig.show()\n```\n\n\n\n## GeoPandas example\n\nYou can load the GeoParquet into a GeoDataFrame as well.\n\n```python\n>>> import geopandas as gpd\n\n>>> gdf = gpd.GeoDataFrame.read_parquet(swemaps.get_path(\"lan\"))\n\n>>> gdf.head()\n\nlan_kod lan geometry\n0 01 Stockholms MULTIPOLYGON (((17.24034 59.24219, 17.28475 59...\n1 03 Uppsala POLYGON ((17.36606 59.61224, 17.35475 59.60292...\n2 04 S\u00f6dermanlands POLYGON ((15.95815 58.96497, 15.86130 58.99856...\n3 05 \u00d6sterg\u00f6tlands POLYGON ((14.93369 58.13112, 14.89472 58.08986...\n4 06 J\u00f6nk\u00f6pings POLYGON ((14.98311 57.93450, 15.00458 57.89598...\n\n# And with matplotlib installed as well we can have quick look\n>>> gdf.plot()\n```\n\n\n",
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