Geopandas#

From version 0.16 onwards Datashader supports rendering GeoPandas GeoDataFrames directly rather than having to convert them to SpatialPandas first.

Here is a demonstration using the “geoda.natregimes” dataset from geodatasets, which includes data on US counties.

import colorcet as cc
import datashader as ds
import datashader.transfer_functions as tf
import geopandas
from geodatasets import get_path

First load the GeoPandas GeoDataFrame. The first time this is called will download and cache the dataset, and subsequent calls will be faster as they will use the cached dataset.

df = geopandas.read_file(get_path("geoda.natregimes"))
df.head()

Downloading file 'natregimes.zip' from 'https://geodacenter.github.io/data-and-lab//data/natregimes.zip' to '/home/runner/.cache/geodatasets'.

	REGIONS	NOSOUTH	POLY_ID	NAME	STATE_NAME	STATE_FIPS	CNTY_FIPS	FIPS	STFIPS	COFIPS	...	GI59	GI69	GI79	GI89	FH60	FH70	FH80	FH90	West	geometry
0	1.0	1.0	1	Lake of the Woods	Minnesota	27	077	27077	27	77	...	0.285235	0.372336	0.342104	0.336455	11.279621	5.4	5.663881	9.515860	0	POLYGON ((-95.34258 48.54670, -95.34081 48.715...
1	2.0	1.0	2	Ferry	Washington	53	019	53019	53	19	...	0.256158	0.360665	0.361928	0.360640	10.053476	2.6	10.079576	11.397059	1	POLYGON ((-118.85050 47.94969, -118.84732 48.4...
2	2.0	1.0	3	Stevens	Washington	53	065	53065	53	65	...	0.283999	0.394083	0.357566	0.369942	9.258437	5.6	6.812127	10.352015	1	POLYGON ((-117.43777 48.04422, -117.54113 48.0...
3	2.0	1.0	4	Okanogan	Washington	53	047	53047	53	47	...	0.258540	0.371218	0.381240	0.394519	9.039900	8.1	10.084926	12.840340	1	POLYGON ((-118.97096 47.93928, -118.97293 47.9...
4	2.0	1.0	5	Pend Oreille	Washington	53	051	53051	53	51	...	0.243263	0.365614	0.358706	0.387848	8.243930	4.1	7.557643	10.313002	1	POLYGON ((-117.43750 49.00000, -117.03098 49.0...

5 rows × 74 columns

The geometry type of this GeoDataFrame is POLYGON, and there are many columns. Columns that we will use are “DNL90” (log of population density in 1990) and “UE90” (unemployment rate in 1990).

Population#

To view 1990 population data using Datashader, first create a canvas to render to of an appropriate size.

canvas = ds.Canvas(plot_width=800, plot_height=400)

The polygons are rasterized to the canvas using the Canvas.polygons method. This takes the source dataframe and name of the geometry column, plus an aggregator. Here we aggregate using the maximum of the population density column so that if there are multiple polygons touching a particular pixel it selects the maximum population density of those polygons.

agg = canvas.polygons(df, geometry="geometry", agg=ds.max("DNL90"))

Now we shade the aggregation using Colorcet’s fire colormap using histogram equalization so that the colors are applied nonlinearly for most even use of the colormap.

im = tf.shade(agg, cmap=cc.fire, how="eq_hist")
tf.set_background(im, "black")

Counties with the lowest population density are rendered in black and dark red, and those with the highest population density are rendered in yellow and white.

Unemployment#

The “UE90” column contains unemployment percentage per county in 1990.

df["UE90"], df["UE90"].min(), df["UE90"].max()

(0        3.894790
     16.811594
     10.700794
     10.203544
     14.991023
           ...    
  10.910351
  11.002271
   4.102888
   3.353981
   5.488553
 Name: UE90, Length: 3085, dtype: float64,
0,
534082923)

We can see from the min and max values that unemployment goes from zero to just over 30%.

To rasterize this using Datashader it is recommended to use a monochromatic colormap such as colorcet.blues and apply this linearly.

agg = canvas.polygons(df, geometry="geometry", agg=ds.max("UE90"))
im = tf.shade(agg, cmap=cc.blues, how="linear")
tf.set_background(im, "white")

Lines#

Datashader can also render GeoPandas GeoDataFrames as lines rather than polygons. Use the same code as in the population example above but replace Canvas.polygons() with Canvas.line() instead.

agg = canvas.line(df, geometry="geometry", agg=ds.max("DNL90"))
im = tf.shade(agg, cmap=cc.fire, how="eq_hist")
tf.set_background(im, "black")

Lines can be rendered with antialiasing. Here is the previous example with an antialiased line width of 2 pixels.

agg = canvas.line(df, geometry="geometry", agg=ds.max("DNL90"), line_width=2)
im = tf.shade(agg, cmap=cc.fire, how="eq_hist")
tf.set_background(im, "black")

Geometry type support#

The following table shows which geometry types are supported by which Datashader Canvas functions.

Canvas function	Supported geometry types
`Canvas.line`	LineString, MultiLineString, MultiPolygon, Polygon
`Canvas.point`	MultiPoint, Point
`Canvas.polygons`	MultiPolygon, Polygon

GeoPandas or SpatialPandas?#

Datashader supports the same line, point and polygon rendering using GeoPandas and SpatialPandas, and produces the same output using either. They work in different ways such that SpatialPandas is usually faster for viewing large datasets and GeoPandas faster when zooming into a small region of a large dataset. The GeoPandas approach is more convenient if you already have your data in GeoPandas format and do not want the overhead of converting to SpatialPandas.

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