adding more thorough docs
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Andy Eschbacher
parent
dd32d45422
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0d050a2206
@ -1,23 +1,26 @@
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"""
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Moran's I geostatistics (global clustering & outliers presence)
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Functionality relies PySAL: http://pysal.readthedocs.io/en/latest/
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"""
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# TODO: Fill in local neighbors which have null/NoneType values with the
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# average of the their neighborhood
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import pysal as ps
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from collections import OrderedDict
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from crankshaft.analysis_data_provider import AnalysisDataProvider
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import pysal as ps
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# crankshaft module
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import crankshaft.pysal_utils as pu
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from crankshaft.analysis_data_provider import AnalysisDataProvider
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# High level interface ---------------------------------------
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class Moran(object):
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"""Class for calculation of Moran's I statistics (global, local, and local
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rate"""
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rate)
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Parameters:
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data_provider (:obj:`AnalysisDataProvider`): Class for fetching data. See
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the `crankshaft.analysis_data_provider` module for more information.
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"""
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def __init__(self, data_provider=None):
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if data_provider is None:
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self.data_provider = AnalysisDataProvider()
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@ -30,7 +33,26 @@ class Moran(object):
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Moran's I (global)
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Implementation building neighbors with a PostGIS database and Moran's I
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core clusters with PySAL.
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Andy Eschbacher
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Args:
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subquery (str): Query to give access to the data needed. This query
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must give access to ``attr_name``, ``geom_col``, and ``id_col``.
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attr_name (str): Column name of data to analyze
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w_type (str): Type of spatial weight. Must be one of `knn`
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or `queen`. See `PySAL documentation
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<http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
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for more information.
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num_ngbrs (int): If using `knn` for ``w_type``, this
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specifies the number of neighbors to be used to define the spatial
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neighborhoods.
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permutations (int): Number of permutations for performing
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conditional randomization to find the p-value. Higher numbers
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takes a longer time for getting results.
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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"""
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params = OrderedDict([("id_col", id_col),
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("attr1", attr_name),
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@ -55,8 +77,26 @@ class Moran(object):
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def local_stat(self, subquery, attr,
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I implementation for PL/Python
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Andy Eschbacher
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Moran's I (local)
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Args:
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subquery (str): Query to give access to the data needed. This query
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must give access to ``attr_name``, ``geom_col``, and ``id_col``.
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attr (str): Column name of data to analyze
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w_type (str): Type of spatial weight. Must be one of `knn`
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or `queen`. See `PySAL documentation
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<http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
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for more information.
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num_ngbrs (int): If using `knn` for ``w_type``, this
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specifies the number of neighbors to be used to define the spatial
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neighborhoods.
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permutations (int): Number of permutations for performing
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conditional randomization to find the p-value. Higher numbers
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takes a longer time for getting results.
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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"""
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# geometries with attributes that are null are ignored
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@ -90,7 +130,26 @@ class Moran(object):
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I Rate (global)
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Andy Eschbacher
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Args:
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subquery (str): Query to give access to the data needed. This query
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must give access to ``attr_name``, ``geom_col``, and ``id_col``.
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numerator (str): Column name of numerator to analyze
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denominator (str): Column name of the denominator
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w_type (str): Type of spatial weight. Must be one of `knn`
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or `queen`. See `PySAL documentation
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<http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
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for more information.
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num_ngbrs (int): If using `knn` for ``w_type``, this
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specifies the number of neighbors to be used to define the spatial
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neighborhoods.
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permutations (int): Number of permutations for performing
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conditional randomization to find the p-value. Higher numbers
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takes a longer time for getting results.
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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"""
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params = OrderedDict([("id_col", id_col),
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("attr1", numerator),
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@ -117,7 +176,26 @@ class Moran(object):
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w_type, num_ngbrs, permutations, geom_col, id_col):
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"""
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Moran's I Local Rate
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Andy Eschbacher
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Args:
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subquery (str): Query to give access to the data needed. This query
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must give access to ``attr_name``, ``geom_col``, and ``id_col``.
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numerator (str): Column name of numerator to analyze
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denominator (str): Column name of the denominator
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w_type (str): Type of spatial weight. Must be one of `knn`
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or `queen`. See `PySAL documentation
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<http://pysal.readthedocs.io/en/latest/users/tutorials/weights.html>`__
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for more information.
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num_ngbrs (int): If using `knn` for ``w_type``, this
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specifies the number of neighbors to be used to define the spatial
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neighborhoods.
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permutations (int): Number of permutations for performing
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conditional randomization to find the p-value. Higher numbers
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takes a longer time for getting results.
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geom_col (str): Name of the geometry column in the dataset for
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finding the spatial neighborhoods.
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id_col (str): Row index for each value. Usually the database index.
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"""
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# geometries with values that are null are ignored
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# resulting in a collection of not as near neighbors
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@ -188,9 +266,9 @@ def map_quads(coord):
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"""
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Map a quadrant number to Moran's I designation
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HH=1, LH=2, LL=3, HL=4
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Input:
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@param coord (int): quadrant of a specific measurement
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Output:
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Args:
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coord (int): quadrant of a specific measurement
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Returns:
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classification (one of 'HH', 'LH', 'LL', or 'HL')
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"""
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if coord == 1:
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@ -206,11 +284,12 @@ def map_quads(coord):
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def quad_position(quads):
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"""
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Produce Moran's I classification based of n
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Input:
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@param quads ndarray: an array of quads classified by
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Map all quads
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Args:
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quads (:obj:`numpy.ndarray`): an array of quads classified by
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1-4 (PySAL default)
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Output:
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@param list: an array of quads classied by 'HH', 'LL', etc.
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Returns:
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list: an array of quads classied by 'HH', 'LL', etc.
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"""
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return [map_quads(q) for q in quads]
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