Dot Density Plots

[1]:

import geopandas as gpd
import mapped
mapped.__version__, gpd.__version__

[1]:

('19.12.1', '0.6.2')

[2]:

gdf = gpd.read_file(gpd.datasets.get_path('nybb'))

gdf['Population'] = [
    479_458,
    2_358_582,
    2_648_771,
    1_664_727,
    1_471_160,
]

gdf

[2]:
BoroCode BoroName Shape_Leng Shape_Area geometry Population
0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON (((970217.022 145643.332, 970227.... 479458
1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON (((1029606.077 156073.814, 102957... 2358582
2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON (((1021176.479 151374.797, 102100... 2648771
3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON (((981219.056 188655.316, 980940.... 1664727
4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON (((1012821.806 229228.265, 101278... 1471160

The dotdensity method is patched into the GeoDataFrame class. It lets you generate a new GeoSeries of points in each polygon in proportion to some attribute in the original GeoDataFrame.

[3]:

dots = gdf.dotdensity('Population', units_per_point=1e4)

[4]:

dots.head()

[4]:

0  0    POINT (918444.793 122684.940)
   1    POINT (946389.317 169427.663)
   2    POINT (951486.097 172255.074)
   3    POINT (929640.480 147704.559)
   4    POINT (929587.612 150638.626)
Name: geometry, dtype: geometry

[5]:

dots.plot(basemap="Stamen.TonerLite", figsize=(12,12));
_images/example-dotdensity_6_0.png
GeoDataFrame.dotdensity(values, units_per_point=1, seed=None)

Create a GeoSeries of random points in polygons.

Parameters:
  • gdf (GeoDataFrame) – The areas in which to create points
  • values (str or Series) – The [possibly scaled] number of points to create in each area
  • units_per_point (numeric, optional) – The rate to scale the values in point generation.
  • seed (int, optional) – A random seed
Returns:
Return type:

GeoSeries