GeoPandas Support (GeoPandas and Shapely)#

GeoPandas GeoDataFrame is a tabular data structure that contains a set of shapes (geometry) per each observation.

GeoDataFrame extends pandas DataFrame and as such, aside from the geometry, can contain other data.

GeoPandas supports the following three basic classes of geometric objects (shapes):

Points / Multi-Points
Lines / Multi-Lines
Polygons / Multi-Polygons

All GeoPandas shapes are “understood” by Lets-Plot and can be plotted using various geometry layers, depending on the type of the shape.

Use:

geom_point(), geom_pie(), geom_text() with Points / Multi-Points
geom_path() with Lines / Multi-Lines
geom_polygon(), geom_map() with Polygons / Multi-Polygons
geom_rect() when used with Polygon shapes will display corresponding bounding boxes

Plot Preliminaries#

import pandas as pd

from lets_plot.geo_data import *
from lets_plot import *
LetsPlot.setup_html()

df = pd.DataFrame({
    'state': ['IL', 'IN', 'MI', 'OH', 'WI'],
    'pop_2021': [12_569_321, 6_805_663, 9_992_427, 11_714_618, 5_852_490],
})
gdf = geocode_states(names=df.state).scope('US').get_boundaries()[['state', 'geometry']]

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).

Use Cases#

Depending on the situation, for spatial data we use either parameter data or map or both.

Use either `data` or `map` Parameter#

Suppose we have the following data:

gdf

	state	geometry
0	IL	MULTIPOLYGON (((-89.13301 36.98200, -89.25478 ...
1	IN	MULTIPOLYGON (((-84.81993 39.10544, -84.89739 ...
2	MI	MULTIPOLYGON (((-90.41862 46.56636, -89.95710 ...
3	OH	MULTIPOLYGON (((-82.59342 38.42186, -82.84431 ...
4	WI	MULTIPOLYGON (((-91.21771 43.50055, -91.24454 ...

If you want to draw only shapes, then it makes no difference which parameter is used:

ggplot() + geom_map(data=gdf)

ggplot() + geom_map(map=gdf)

Use `data` Parameter#

If you want to use aesthetics, the data parameter is the only choice:

ggplot() + geom_map(aes(fill='state'), color='white', data=gdf)

Use Both: `data` and `map` Parameters#

Suppose you also have a dataframe with population data:

df

	state	pop_2021
0	IL	12569321
1	IN	6805663
2	MI	9992427
3	OH	11714618
4	WI	5852490

In this situation, in order to link aesthetics to the population numbers you will use:

data parameter for the “population” dataframe,
map parameter for the state boundaries GeoDataframe.

The 3rd parameter, map_join, will help to combine population values and state boundaries on the same chart:

ggplot() + geom_map(aes(fill='pop_2021'), color='white', data=df, map=gdf, map_join='state')

`use_crs` Parameter#

Specify EPSG code of coordinate reference system (CRS). All coordinates in GeoDataFrame will be projected to this CRS.

ggplot() + geom_map(map=gdf, use_crs="EPSG:32616")

Value “provided” tells Lets-Plot that the input GeoDataframes already contains coordinates in the desired CRS and should not be reprojected any further.

gdf_utm = gdf.to_crs("EPSG:32616")
ggplot() + geom_map(map=gdf_utm, use_crs="provided")

Interactive Maps#

Apart from the use_crs parameter, GeoDataFrames are fully supported on Lets-Plot interactive maps.

When used in combination with geom_livemap(), the value of the use_crs parameter is ignored and all coordinates are reprojected using the Mercator projection.

ggplot() + geom_livemap() + geom_map(aes(fill='state'), data=gdf, alpha=.5)

Examples#

The world map with Lets-Plot and GeoPandas:

Plotting Airbnb prices Boston:

Spatial prediction of soil pollutants with multi-output Gaussian processes:

Using geom_imshow() to draw DEM on map:

use_crs parameter:

Using exotic map projections:

An inset map of Kotlin island:

GeoPandas Support (GeoPandas and Shapely)#

Plot Preliminaries#

Use Cases#

Use either data or map Parameter#

Use data Parameter#

Use Both: data and map Parameters#