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DOC: Add examples to build_distance_band #705

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Apr 14, 2024
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DOC: Add examples to build_distance_band #705

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DOC: add examples to Graph.build_disatnce_band
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Update libpysal/graph/base.py
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85 changes: 85 additions & 0 deletions libpysal/graph/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -463,6 +463,91 @@ def build_distance_band(
-------
Graph
libpysal.graph.Graph encoding distance band weights

Examples
-------
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>>> import geopandas as gpd
>>> from geodatasets import get_path
>>> nybb = gpd.read_file(get_path('nybb')).set_index("BoroName")
>>> nybb
BoroCode ... geometry
BoroName ...
Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227....
Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957...
Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100...
Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940....
Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278...
[5 rows x 4 columns]

Note that the function requires point geometry as the input.
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The treshold distance is in units of the dataframe.
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>>> distance_band = graph.Graph.build_distance_band(nybb.centroid, 45000)
>>> distance_band.adjacency
focal neighbor
Staten Island Staten Island 0
Queens Brooklyn 1
Brooklyn Queens 1
Manhattan Bronx 1
Bronx Manhattan 1
Name: weight, dtype: int64

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>>> distance_band = graph.Graph.build_distance_band(nybb.centroid, 110000)
>>> distance_band.adjacency
focal neighbor
Staten Island Queens 1
Brooklyn 1
Manhattan 1
Queens Staten Island 1
Brooklyn 1
Manhattan 1
Bronx 1
Brooklyn Staten Island 1
Queens 1
Manhattan 1
Bronx 1
Manhattan Staten Island 1
Queens 1
Brooklyn 1
Bronx 1
Bronx Queens 1
Brooklyn 1
Manhattan 1
Name: weight, dtype: int64

Instead of binary weights you can use inverse distance.

>>> distance_band = graph.Graph.build_distance_band(
... nybb.centroid,
... 45000,
... binary=False,
... )
>>> distance_band.adjacency
focal neighbor
Staten Island Staten Island 0.000000
Queens Brooklyn 0.000024
Brooklyn Queens 0.000024
Manhattan Bronx 0.000026
Bronx Manhattan 0.000026
Name: weight, dtype: float64

You can specify the kernel function.
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>>> distance_band = graph.Graph.build_distance_band(
... nybb.centroid,
... 45000,
... binary=False,
... kernel='bisquare',
... bandwidth=60000,
... )
>>> distance_band.adjacency
focal neighbor
Staten Island Staten Island 0.000000
Queens Brooklyn 0.232079
Brooklyn Queens 0.232079
Manhattan Bronx 0.309825
Bronx Manhattan 0.309825
Name: weight, dtype: float64
"""
ids = _evaluate_index(data)

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