def south(df=False):
"""
Sets up the data for the US southern counties example.
Returns
-------
dictionary or (dictionary, dataframe), where the dictionary is keyed on:
X : Data from southern counties, columns "GI89", "BLK90", "HR90"
Y : Outcome variate, "DNL90"
Z : upper-level variate, the state average "FH90"
W : queen weights matrix between counties
M : queen matrix between states
membership : membership vector relating counties to their states
and the dataframe contains the raw dataset
"""
import geopandas
data = geopandas.read_file(pysal.examples.get_path('south.shp'))
data = data[data.STATE_NAME != 'District of Columbia']
X = data[['GI89', 'BLK90', 'HR90']].values
N = X.shape[0]
Z = data.groupby('STATE_NAME')['FH90'].mean()
Z = Z.values.reshape(-1, 1)
J = Z.shape[0]
Y = data.DNL90.values.reshape(-1, 1)
W2 = pysal.queen_from_shapefile(pysal.examples.get_path('us48.shp'),
idVariable='STATE_NAME')
W2 = pysal.w_subset(
W2,
ids=data.STATE_NAME.unique().tolist()) #only keep what's in the data
W1 = pysal.queen_from_shapefile(pysal.examples.get_path('south.shp'),
idVariable='FIPS')
W1 = pysal.w_subset(
W1, ids=data.FIPS.tolist()) #again, only keep what's in the data
W1.transform = 'r'
W2.transform = 'r'
membership = data.STATE_NAME.apply(lambda x: W2.id_order.index(x)).values
d = {'X': X, 'Y': Y, 'Z': Z, 'W': W1, 'M': W2, 'membership': membership}
if df:
return d, data
else:
return d
def w_local_cluster(w):
"""
Local clustering coefficients for each unit as a node in a graph. [ws]_
Parameters
----------
w : W
spatial weights object
Returns
-------
c : array (w.n,1)
local clustering coefficients
Notes
-----
The local clustering coefficient :math:`c_i` quantifies how close the
neighbors of observation :math:`i` are to being a clique:
.. math::
c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i
where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =
|N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the
weights between pairs in :math:`N_i`.
References
----------
.. [ws] Watts, D.J. and S.H. Strogatz (1988) "Collective dynamics of 'small-world' networks". Nature, 393: 440-442.
Examples
--------
>>> w = pysal.lat2W(3,3, rook=False)
>>> w_local_cluster(w)
array([[ 1. ],
[ 0.6 ],
[ 1. ],
[ 0.6 ],
[ 0.42857143],
[ 0.6 ],
[ 1. ],
[ 0.6 ],
[ 1. ]])
"""
c = np.zeros((w.n, 1), float)
w.transformation = 'b'
for i, id in enumerate(w.id_order):
ki = max(w.cardinalities[id], 1) # deal with islands
Ni = w.neighbors[id]
wi = pysal.w_subset(w, Ni).full()[0]
c[i] = wi.sum() / (ki * (ki - 1))
return c
def w_local_cluster(w):
"""
Local clustering coefficients for each unit as a node in a graph. [ws]_
Parameters
----------
w : W
spatial weights object
Returns
-------
c : array (w.n,1)
local clustering coefficients
Notes
-----
The local clustering coefficient :math:`c_i` quantifies how close the
neighbors of observation :math:`i` are to being a clique:
.. math::
c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i
where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =
|N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the
weights between pairs in :math:`N_i`.
References
----------
.. [ws] Watts, D.J. and S.H. Strogatz (1988) "Collective dynamics of 'small-world' networks". Nature, 393: 440-442.
Examples
--------
>>> w = pysal.lat2W(3,3, rook=False)
>>> w_local_cluster(w)
array([[ 1. ],
[ 0.6 ],
[ 1. ],
[ 0.6 ],
[ 0.42857143],
[ 0.6 ],
[ 1. ],
[ 0.6 ],
[ 1. ]])
"""
c = np.zeros((w.n, 1), float)
w.transformation = 'b'
for i, id in enumerate(w.id_order):
ki = max(w.cardinalities[id], 1) # deal with islands
Ni = w.neighbors[id]
wi = pysal.w_subset(w, Ni).full()[0]
c[i] = wi.sum() / (ki * (ki - 1))
return c
def _update_topo(self):
atopo = ps.w_subset(self.w, self.agent_xyids, \
silent_island_warning=True).neighbors
xyid2agent_id = {xyid: agent_id for agent_id, xyid in \
enumerate(self.agent_xyids)}
self.atopo = {
xyid2agent_id[i]: [xyid2agent_id[j] for j in atopo[i]]
for i in atopo
} # Values are agent order in self.agents!!!
def _update_topo(self):
atopo = ps.w_subset(self.w, self.agent_xyids, \
silent_island_warning=True).neighbors
xyid2agent_id = {xyid: agent_id for agent_id, xyid in \
enumerate(self.agent_xyids)}
self.atopo = {xyid2agent_id[i]: [xyid2agent_id[j] for j in atopo[i]] for i in atopo}# Values are agent order in self.agents!!!