mapclassify.greedy

mapclassify.greedy(gdf, strategy='balanced', balance='count', min_colors=4, sw='queen', min_distance=None, silence_warnings=True, interchange=False)

Color GeoDataFrame using various strategies of greedy (topological) colouring.

Attempts to color a GeoDataFrame using as few colors as possible, where no neighbours can have same color as the feature itself. Offers various strategies ported from QGIS or implemented within NetworkX for greedy graph coloring.

greedy will return pandas.Series representing assigned color codes.

Parameters:

gdfGeoDataFrame

GeoDataFrame

strategystr (default ‘balanced’)

Determine coloring strategy. Options are 'balanced' for algorithm based on QGIS Topological coloring. It is aiming for a visual balance, defined by the balance parameter. Other options are those supported by networkx.greedy_color:

• 'largest_first'

• 'random_sequential'

• 'smallest_last'

• 'independent_set'

• 'connected_sequential_bfs'

• 'connected_sequential_dfs'

• 'connected_sequential' (alias for the previous strategy)

• 'saturation_largest_first'

• 'DSATUR' (alias for the previous strategy)

For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html

balancestr (default ‘count’)

If strategy is 'balanced', determine the method of color balancing.

• 'count' attempts to balance the number of features per each color.

• 'area' attempts to balance the area covered by each color.

• 'centroid' attempts to balance the distance between colors based on the distance between centroids.

• 'distance' attempts to balance the distance between colors based on the distance between geometries. Slower than 'centroid', but more precise.

Both 'centroid' and 'distance' are significantly slower than other especially for larger GeoDataFrames. Apart from 'count', all require CRS to be projected (not in degrees) to ensure metric values are correct.

min_colors: int (default 4)

If strategy is 'balanced', define the minimal number of colors to be used.

sw‘queen’, ‘rook’ or libpysal.weights.W (default ‘queen’)

If min_distance is None, one can pass 'libpysal.weights.W' object denoting neighbors or let greedy generate one based on 'queen' or 'rook' contiguity.

min_distancefloat (default None)

Set minimal distance between colors. If min_distance is not None, slower algorithm for generating spatial weghts is used based on intersection between geometries. 'min_distance' is then used as a tolerance of intersection.

silence_warningsbool (default True)

Silence libpysal warnings when creating spatial weights.

interchangebool (default False)

Use the color interchange algorithm (applicable for NetworkX strategies). For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html

Returns:

colorpandas.Series

pandas.Series representing assinged color codes.

Examples

>>> from mapclassify import greedy
>>> import geopandas
>>> world = geopandas.read_file(
...     "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
... )
>>> africa = world.loc[world.CONTINENT == "Africa"].copy()
>>> africa = africa.to_crs("ESRI:102022").reset_index(drop=True)

Default:

>>> africa["greedy_colors"] = greedy(africa)
>>> africa["greedy_colors"].head()
0    1
1    0
2    0
3    1
4    4
Name: greedy_colors, dtype: int64

Balanced by area:

>>> africa["balanced_area"] = greedy(africa, strategy="balanced", balance="area")
>>> africa["balanced_area"].head()
0    1
1    2
2    0
3    1
4    3
Name: balanced_area, dtype: int64

Using rook adjacency:

>>> africa["rook_adjacency"] = greedy(africa, sw="rook")
>>> africa["rook_adjacency"].tail()
46    3
47    0
48    2
49    3
50    1
Name: rook_adjacency, dtype: int64

Adding minimal distance between colors:

>>> africa["min_distance"] = greedy(africa, min_distance=1000000)
>>> africa["min_distance"].head()
0    1
1    9
2    0
3    7
4    4
Name: min_distance, dtype: int64

Using different coloring strategy:

>>> africa["smallest_last"] = greedy(africa, strategy="smallest_last")
>>> africa["smallest_last"].head()
0    3
1    1
2    1
3    3
4    1
Name: smallest_last, dtype: int64