esda.G¶
- class esda.G(y, w, permutations=999)[source]¶
Global G Autocorrelation Statistic
- Parameters:
- w
W
DistanceBand W spatial weights based on distance band
Notes
Moments are based on normality assumption.
For technical details see [GO10] and [OG10].
Examples
>>> import libpysal
>>> import numpy
>>> numpy.random.seed(10)
Preparing a point data set
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
Creating a weights object from points
>>> w = libpysal.weights.DistanceBand(points,threshold=15)
>>> w.transform = "B"
Preparing a variable
>>> y = numpy.array([2, 3, 3.2, 5, 8, 7])
Applying Getis and Ord G test
>>> from esda.getisord import G
>>> g = G(y,w)
Examining the results
>>> round(g.G, 3)
0.557
>>> round(g.p_norm, 3)
0.173
- Attributes:
- w
W
DistanceBand W spatial weights based on distance band
vector of G values for permutated samples
null: spatial randomness
alternative: the observed G is extreme it is either
extremely high or extremely low
Methods
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Function to compute a G statistic on a dataframe |
- classmethod by_col(df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **stat_kws)[source]¶
Function to compute a G statistic on a dataframe
- Parameters:
is searched for in the dataframe’s metadata
return a series contaning the results of the computation. If
operating inplace, the derived columns will be named ‘column_g’
the G statistic’s documentation for available p-values
documentation for the G statistic.
the
relevant
columns
attached.