esda.Smaup

class esda.Smaup(n, k, rho)[source]

S-maup: Statistical Test to Measure the Sensitivity to the Modifiable Areal Unit Problem

Parameters
nint

number of spatial units

kint

number of regions

rhofloat

rho value (level of spatial autocorrelation) ranges from -1 to 1

Notes

Technical details and derivations can be found in [].

Examples

>>> import libpysal
>>> import numpy as np
>>> from esda.moran import Moran
>>> from esda.smaup import Smaup
>>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("stl_hom.txt"))
>>> y = np.array(f.by_col['HR8893'])
>>> rho = Moran(y,  w).I
>>> n = len(y)
>>> k = int(n/2)
>>> s = Smaup(n,k,rho)
>>> s.smaup
0.15221341690376405
>>> s.critical_01
0.38970613333333337
>>> s.critical_05
0.3557221333333333
>>> s.critical_1
0.3157950666666666
>>> s.summary
'Pseudo p-value > 0.10 (H0 is not rejected)'

SIDS example replicating OpenGeoda

>>> w = libpysal.io.open(libpysal.examples.get_path("sids2.gal")).read()
>>> f = libpysal.io.open(libpysal.examples.get_path("sids2.dbf"))
>>> SIDR = np.array(f.by_col("SIDR74"))
>>> from esda.moran import Moran
>>> rho = Moran(SIDR,  w).I
>>> n = len(y)
>>> k = int(n/2)
>>> s = Smaup(n,k,rho)
>>> s.smaup
0.15176796553181948
>>> s.critical_01
0.38970613333333337
>>> s.critical_05
0.3557221333333333
>>> s.critical_1
0.3157950666666666
>>> s.summary
'Pseudo p-value > 0.10 (H0 is not rejected)'
Attributes
nint

number of spatial units

kint

number of regions

rhofloat

rho value (level of spatial autocorrelation) ranges from -1 to 1

smaupfloat

: S-maup statistic (M)

critical_01float

: critical value at 0.99 confidence level

critical_05float

: critical value at 0.95 confidence level

critical_1float

: critical value at 0.90 confidence level

summarystr

: message with interpretation of results

__init__(n, k, rho)[source]

Methods

__init__(n, k, rho)