def _build_data(data, tracts, year, weights_type):
df = data.loc[data.year == year].copy().dropna(how="any")
tracts = tracts.loc[tracts.geoid.isin(df.index)].copy()
weights = {"queen": Queen, "rook": Rook}
w = weights[weights_type].from_dataframe(tracts, idVariable="geoid")
# drop islands from dataset and rebuild weights
#df.drop(index=w.islands, inplace=True)
#tracts.drop(index=w.islands, inplace=True)
#w = weights[weights_type].from_dataframe(tracts, idVariable="geoid")
knnw = KNN.from_dataframe(tracts, k=1, ids=tracts.geoid.tolist())
return df, w, knnw
def setUp(self):
sids = libpysal.io.open(libpysal.examples.get_path('sids2.shp'), 'r')
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = KNN.from_array(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = libpysal.io.open(libpysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
self.sids_hb_rr5 = np.array([0.00075586, 0.,
0.0008285, 0.0018315, 0.00498891])
self.sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
0.00086896, 0.0018315, 0.00498891])
self.sids_hb_r3r5 = np.array([0.00091659, 0.,
0.00156838, 0.0018315, 0.00498891])
if not PANDAS_EXTINCT:
self.df = sids_db.to_df()
self.ename = 'SID74'
self.bname = 'BIR74'
self.enames = [self.ename, 'SID79']
self.bnames = [self.bname, 'BIR79']
self.sids79r = np.array([.000563, .001659, .001879,
.002410, .002720])
def cluster_spatial(
gdf,
n_clusters=6,
spatial_weights="rook",
method=None,
columns=None,
threshold_variable="count",
threshold=10,
time_var="year",
id_var="geoid",
scaler="std",
weights_kwargs=None,
**kwargs,
):
"""Create a *spatial* geodemographic typology by running a cluster
analysis on the metro area's neighborhood attributes and including a
contiguity constraint.
Parameters
----------
gdf : geopandas.GeoDataFrame
long-form geodataframe holding neighborhood attribute and geometry data.
n_clusters : int
the number of clusters to model. The default is 6).
spatial_weights : ['queen', 'rook'] or libpysal.weights.W object
spatial weights matrix specification`. By default, geosnap will calculate Rook
weights, but you can also pass a libpysal.weights.W object for more control
over the specification.
method : str in ['ward_spatial', 'spenc', 'skater', 'azp', 'max_p']
the clustering algorithm used to identify neighborhood types
columns : array-like
subset of columns on which to apply the clustering
threshold_variable : str
for max-p, which variable should define `p`. The default is "count",
which will grow regions until the threshold number of polygons have
been aggregated
threshold : numeric
threshold to use for max-p clustering (the default is 10).
time_var : str
which column on the dataframe defines time and or sequencing of the
long-form data. Default is "year"
id_var : str
which column on the long-form dataframe identifies the stable units
over time. In a wide-form dataset, this would be the unique index
weights_kwargs : dict
If passing a libpysal.weights.W instance to spatial_weights, these additional
keyword arguments that will be passed to the weights constructor
scaler : None or scaler class from sklearn.preprocessing
a scikit-learn preprocessing class that will be used to rescale the
data. Defaults to sklearn.preprocessing.StandardScaler
Returns
-------
gdf : geopandas.GeoDataFrame
GeoDataFrame with a column of neighborhood cluster labels
appended as a new column. If cluster method exists as a column on the DataFrame
then the column will be incremented.
models : dict of named tuples
tab-completable dictionary of named tuples keyed on the Community's time variable
(e.g. year). The tuples store model results and have attributes X, columns, labels,
instance, W, which store the input matrix, column labels, fitted model instance,
and spatial weights matrix
model_name : str
name of model to be stored in a Community
"""
specification = {
"azp": azp,
"spenc": spenc,
"ward_spatial": ward_spatial,
"skater": skater,
"max_p": max_p,
}
if method not in specification.keys():
raise ValueError(
"`method` must be one of ['ward_spatial', 'spenc', 'skater', 'azp', 'max_p']"
)
if method in gdf.columns.tolist():
model_name = method + str(
len(gdf.columns[gdf.columns.str.startswith(method)]))
else:
model_name = method
if not columns:
raise ValueError("You must provide a subset of columns as input")
if not method:
raise ValueError("You must choose a clustering algorithm to use")
if scaler == "std":
scaler = StandardScaler()
times = gdf[time_var].unique()
gdf = gdf.set_index([time_var, id_var])
# this is the dataset we'll operate on
data = gdf.copy()[columns + ["geometry"]]
contiguity_weights = {"queen": Queen, "rook": Rook}
if spatial_weights in contiguity_weights.keys():
W = contiguity_weights[spatial_weights]
else:
W = spatial_weights
models = _Map()
ws = {}
clusters = []
gdf[model_name] = np.nan
# loop over each time period, standardize the data and build a weights matrix
for time in times:
df = data.loc[time].dropna(how="any", subset=columns).reset_index()
df[time_var] = time
if scaler:
df[columns] = scaler.fit_transform(df[columns].values)
if weights_kwargs:
w0 = W.from_dataframe(df, **weights_kwargs)
else:
w0 = W.from_dataframe(df)
w1 = KNN.from_dataframe(df, k=1)
ws = [w0, w1]
if threshold_variable and threshold_variable != "count":
data[threshold_variable] = gdf[threshold_variable]
threshold_var = data.threshold_variable.values
ws[0] = attach_islands(ws[0], ws[1])
elif threshold_variable == "count":
threshold_var = np.ones(len(data.loc[time]))
ws[0] = attach_islands(ws[0], ws[1])
else:
threshold_var = None
model = specification[method](
df[columns],
w=ws[0],
n_clusters=n_clusters,
threshold_variable=threshold_var,
threshold=threshold,
**kwargs,
)
labels = model.labels_.astype(str)
clusters = pd.DataFrame({
model_name: labels,
time_var: df[time_var],
id_var: df[id_var]
})
clusters = clusters.drop_duplicates(subset=[id_var])
clusters.set_index([time_var, id_var], inplace=True)
gdf.update(clusters)
results = ModelResults(
X=df[columns].values,
columns=columns,
labels=model.labels_,
instance=model,
W=ws[0],
)
models[time] = results
gdf = gdf.reset_index()
return gdf, models, model_name
def cluster_spatial(
gdf,
n_clusters=6,
spatial_weights="rook",
method=None,
columns=None,
threshold_variable="count",
threshold=10,
time_var="year",
id_var="geoid",
return_model=False,
scaler=None,
**kwargs,
):
"""Create a *spatial* geodemographic typology by running a cluster
analysis on the metro area's neighborhood attributes and including a
contiguity constraint.
Parameters
----------
gdf : geopandas.GeoDataFrame
long-form geodataframe holding neighborhood attribute and geometry data.
n_clusters : int
the number of clusters to model. The default is 6).
weights_type : str 'queen' or 'rook'
spatial weights matrix specification` (the default is "rook").
method : str
the clustering algorithm used to identify neighborhood types
columns : list-like
subset of columns on which to apply the clustering
threshold_variable : str
for max-p, which variable should define `p`. The default is "count",
which will grow regions until the threshold number of polygons have
been aggregated
threshold : numeric
threshold to use for max-p clustering (the default is 10).
time_var: str
which column on the dataframe defines time and or sequencing of the
long-form data. Default is "year"
id_var: str
which column on the long-form dataframe identifies the stable units
over time. In a wide-form dataset, this would be the unique index
scaler: str or sklearn.preprocessing.Scaler
a scikit-learn preprocessing class that will be used to rescale the
data. Defaults to StandardScaler
Returns
-------
geopandas.GeoDataFrame with a column of neighborhood cluster labels
appended as a new column. Will overwrite columns of the same name.
"""
if not columns:
raise ValueError("You must provide a subset of columns as input")
if not method:
raise ValueError("You must choose a clustering algorithm to use")
times = gdf[time_var].unique()
gdf = gdf.set_index([time_var, id_var])
# this is the dataset we'll operate on
data = gdf.copy()[columns + ["geometry"]]
contiguity_weights = {"queen": Queen, "rook": Rook}
if spatial_weights in contiguity_weights.keys():
W = contiguity_weights[spatial_weights]
else:
W = spatial_weights
specification = {
"azp": azp,
"spenc": spenc,
"ward_spatial": ward_spatial,
"skater": skater,
"max_p": max_p,
}
# if the user doesn't specify, use the standard scalar
if not scaler:
scaler = StandardScaler()
ws = {}
clusters = []
dfs = []
# loop over each time period, standardize the data and build a weights matrix
for time in times:
df = data.loc[time].dropna(how="any", subset=columns).reset_index()
df[time_var] = time
df[columns] = scaler.fit_transform(df[columns].values)
w0 = W.from_dataframe(df)
w1 = KNN.from_dataframe(df, k=1)
ws = [w0, w1]
# the rescalar can create nans if a column has no variance, so fill with 0
df = df.fillna(0)
if threshold_variable and threshold_variable != "count":
data[threshold_variable] = gdf[threshold_variable]
threshold_var = data.threshold_variable.values
ws[0] = attach_islands(ws[0], ws[1])
elif threshold_variable == "count":
threshold_var = np.ones(len(data.loc[time]))
ws[0] = attach_islands(ws[0], ws[1])
else:
threshold_var = None
model = specification[method](
df[columns],
w=ws[0],
n_clusters=n_clusters,
threshold_variable=threshold_var,
threshold=threshold,
**kwargs,
)
labels = model.labels_.astype(str)
clusters = pd.DataFrame({
method: labels,
time_var: df[time_var],
id_var: df[id_var]
})
clusters.set_index([time_var, id_var], inplace=True)
dfs.append(gdf.loc[time].join(clusters, how="left"))
gdf = pd.concat(dfs).reset_index()
if return_model:
return gdf, model
return gdf