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geopandas.datasets is deprecated #3043

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May 7, 2023
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geopandas.datasets is deprecated #3043

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29 changes: 15 additions & 14 deletions doc/user_guide/marks/geoshape.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -12,16 +12,17 @@ A ``geoshape`` mark can contain any :ref:`standard mark properties <mark-propert

Basic Map
^^^^^^^^^
Altair can work with many different geographical data formats, including geojson and topojson files. Often, the most convenient input format to use is a ``GeoDataFrame``. Here we load the Natural Earth dataset and create a basic map using ``mark_geoshape``:
Altair can work with many different geographical data formats, including geojson and topojson files. Often, the most convenient input format to use is a ``GeoDataFrame``. Here we load the Natural Earth 110m Cultural Vectors dataset and create a basic map using ``mark_geoshape``:

.. altair-plot::

import altair as alt
from vega_datasets import data
import geopandas as gpd

fp = gpd.datasets.get_path('naturalearth_lowres')
gdf_ne = gpd.read_file(fp) # shapefile
url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
gdf_ne = gpd.read_file(url) # zipped shapefile
gdf_ne = gdf_ne[["NAME", "CONTINENT", "POP_EST", 'geometry']]

alt.Chart(gdf_ne).mark_geoshape()

Expand Down Expand Up @@ -51,7 +52,7 @@ The following examples applies these approaches to focus on continental Africa:

.. altair-plot::

gdf_sel = gdf_ne[gdf_ne.continent == 'Africa']
gdf_sel = gdf_ne.query("CONTINENT == 'Africa'")

alt.Chart(gdf_sel).mark_geoshape()

Expand All @@ -60,7 +61,7 @@ The following examples applies these approaches to focus on continental Africa:
.. altair-plot::

alt.Chart(gdf_ne).mark_geoshape().transform_filter(
alt.datum.continent == 'Africa'
alt.datum.CONTINENT == 'Africa'
)

3. Specify ``scale`` (zoom level) and ``translate`` (panning) within the ``project`` method:
Expand All @@ -76,10 +77,10 @@ The following examples applies these approaches to focus on continental Africa:

.. altair-plot::

extent_roi = gdf_ne.query("continent == 'Africa'")
extent_roi = gdf_ne.query("CONTINENT == 'Africa'")
xmin, ymin, xmax, ymax = extent_roi.total_bounds

# fit object should be an array of GeoJSON-like features
# fit object should be a GeoJSON-like Feature or FeatureCollection
extent_roi_feature = {
"type": "Feature",
"geometry": {"type": "Polygon",
Expand Down Expand Up @@ -112,12 +113,12 @@ In the following example the input geometry is not projected and is instead rend

Mapping Polygons
^^^^^^^^^^^^^^^^
The following example maps the visual property of the ``name`` column using the ``color`` encoding.
The following example maps the visual property of the ``NAME`` column using the ``color`` encoding.

.. altair-plot::

alt.Chart(gdf_sel).mark_geoshape().encode(
color='name:N'
color='NAME:N'
)

Since each country is represented by a (multi)polygon, we can separate the ``stroke`` and ``fill`` definitions as such:
Expand All @@ -128,7 +129,7 @@ Since each country is represented by a (multi)polygon, we can separate the ``str
stroke='white',
strokeWidth=1.5
).encode(
fill='name:N'
fill='NAME:N'
)

Mapping Lines
Expand Down Expand Up @@ -166,7 +167,7 @@ Using this approach one can also style Polygons as if they are Linestrings:
filled=False,
strokeWidth=1.5
).encode(
stroke='name:N'
stroke='NAME:N'
)

Mapping Points
Expand Down Expand Up @@ -194,7 +195,7 @@ Caveat: To use the ``size`` encoding for the Points you will need to use the ``m
gdf_centroid["lat"] = gdf_centroid.geometry.y

alt.Chart(gdf_centroid).mark_circle().encode(
longitude="lon:Q", latitude="lat:Q", size="pop_est:Q"
longitude="lon:Q", latitude="lat:Q", size="POP_EST:Q"
)

Altair also contains expressions related to geographical features. We can for example define the ``centroids`` using a ``geoCentroid`` expression:
Expand All @@ -214,7 +215,7 @@ Altair also contains expressions related to geographical features. We can for ex
).encode(
longitude='centroid[0]:Q',
latitude='centroid[1]:Q',
size="pop_est:Q"
size="POP_EST:Q"
)

(basemap + bubbles).project(
Expand All @@ -229,7 +230,7 @@ An alternative to showing the population sizes as bubbles, is to create a "Choro
.. altair-plot::

alt.Chart(gdf_sel).mark_geoshape().encode(
color='pop_est'
color='POP_EST'
)

When we create choropleth maps, we need to be careful, because although the color changes according to the value of the column we are interested in, the size is tied to the area of each country and we might miss interesting values in small countries just because we can't easily see them on the map (e.g. if we were to visualize population density).
Expand Down