def clustering(distribution, areal_units, classes=None):
""" Return the clustering coefficient for the different classes
Assume that the class `c` is overrepresented in `N_u` areal units, and that
we obtain `N_c` clusters after aggregating the neighbourhings units.
Parameter
---------
distribution: nested dictionaries
Number of people per class, per areal unit as given in the raw data
(ungrouped). The dictionary must have the following formatting:
> {areal_id: {class_id: number}}
areal_units: dictionnary
Dictionnary of areal unit ids with shapely polygon object representing
the unit's geometry as values.
classes: dictionary of lists
When the original categories need to be aggregated into different
classes.
> {class: [categories belonging to this class]}
This can be arbitrarily imposed, or computed with uncover_classes
function of this package.
Returns
-------
clustering: dictionary
Dictionary of classes names with clustering values.
"""
# Regroup into classes if specified. Otherwise return categories indicated
# in the data
if not classes:
classes = return_categories(distribution)
## Get the number of neighbourhoods
neigh = mb.neighbourhoods(distribution, areal_units, classes)
num_neigh = {cl: len(neigh[cl]) for cl in classes}
num_units = {
cl: len([a for ne in neigh[cl] for a in ne])
for cl in classes
}
## Compute clustering values
clustering = {}
for cl in classes:
if num_units[cl] == 0:
clustering[cl] = float('nan')
elif num_units[cl] == 1:
clustering[cl] = 1
else:
clustering[cl] = _single_clustering(num_units[cl], num_neigh[cl])
clustering[cl] = ((num_neigh[cl] - num_units[cl]) /
(1 - num_units[cl]))
return clustering
def test_neighbourhoods_clust(self):
""" Test the extraction of neighbourhoods """
city = clustered_city()
units = grid()
neigh = mb.neighbourhoods(city, units)
neigh_answer = {"A": [[0, 1, 3, 6]], "B": [[2, 4, 5, 7, 8]]}
assert len(neigh["A"]) == len(neigh_answer["A"])
assert len(neigh["B"]) == len(neigh_answer["B"])
def clustering(distribution, areal_units, classes=None):
""" Return the clustering coefficient for the different classes
Assume that the class `c` is overrepresented in `N_u` areal units, and that
we obtain `N_c` clusters after aggregating the neighbourhings units.
Parameter
---------
distribution: nested dictionaries
Number of people per class, per areal unit as given in the raw data
(ungrouped). The dictionary must have the following formatting:
> {areal_id: {class_id: number}}
areal_units: dictionnary
Dictionnary of areal unit ids with shapely polygon object representing
the unit's geometry as values.
classes: dictionary of lists
When the original categories need to be aggregated into different
classes.
> {class: [categories belonging to this class]}
This can be arbitrarily imposed, or computed with uncover_classes
function of this package.
Returns
-------
clustering: dictionary
Dictionary of classes names with clustering values.
"""
# Regroup into classes if specified. Otherwise return categories indicated
# in the data
if not classes:
classes = return_categories(distribution)
## Get the number of neighbourhoods
neigh = mb.neighbourhoods(distribution, areal_units, classes)
num_neigh = {cl: len(neigh[cl]) for cl in classes}
num_units = {cl: len([a for ne in neigh[cl] for a in ne])
for cl in classes}
## Compute clustering values
clustering = {}
for cl in classes:
if num_units[cl] == 0:
clustering[cl] = float('nan')
elif num_units[cl] == 1:
clustering[cl] = 1
else:
clustering[cl] = _single_clustering(num_units[cl],
num_neigh[cl])
clustering[cl] = ((num_neigh[cl] - num_units[cl]) /
(1 - num_units[cl]))
return clustering
def test_neighbourhoods_check(self):
""" Test the extraction of neighbourhoods """
city = checkerboard_city()
units = grid()
neigh = mb.neighbourhoods(city, units)
neigh_answer = {"A":[[0],[2],[4],[6],[8]],
"B":[[1],[3],[5],[7]]}
assert len(neigh["A"]) == len(neigh_answer["A"])
assert len(neigh["B"]) == len(neigh_answer["B"])
def test_neighbourhoods_clust(self):
""" Test the extraction of neighbourhoods """
city = clustered_city()
units = grid()
neigh = mb.neighbourhoods(city, units)
neigh_answer = {"A":[[0,1,3,6]],
"B":[[2,4,5,7,8]]}
assert len(neigh["A"]) == len(neigh_answer["A"])
assert len(neigh["B"]) == len(neigh_answer["B"])
def test_neighbourhoods_check(self):
""" Test the extraction of neighbourhoods """
city = checkerboard_city()
units = grid()
neigh = mb.neighbourhoods(city, units)
neigh_answer = {
"A": [[0], [2], [4], [6], [8]],
"B": [[1], [3], [5], [7]]
}
assert len(neigh["A"]) == len(neigh_answer["A"])
assert len(neigh["B"]) == len(neigh_answer["B"])
