giddy.markov.LISA_Markov

LISA_Markov.spillover(self, quadrant=1, neighbors_on=False)

Detect spillover locations for diffusion in LISA Markov.

Parameters

quadrantint

which quadrant in the scatterplot should form the core of a cluster.

neighbors_onbinary

If false, then only the 1st order neighbors of a core location are included in the cluster. If true, neighbors of cluster core 1st order neighbors are included in the cluster.

Returns

resultsdictionary

two keys - values pairs: ‘components’ - array (n, t) values are integer ids (starting at 1) indicating which component/cluster observation i in period t belonged to. ‘spillover’ - array (n, t-1) binary values indicating if the location was a spill-over location that became a new member of a previously existing cluster.

Examples

>>> import libpysal
>>> from giddy.markov import LISA_Markov
>>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
>>> years = list(range(1929, 2010))
>>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
>>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
>>> np.random.seed(10)
>>> lm_random = LISA_Markov(pci, w, permutations=99)
>>> r = lm_random.spillover()
>>> (r['components'][:, 12] > 0).sum()
17
>>> (r['components'][:, 13]>0).sum()
23
>>> (r['spill_over'][:,12]>0).sum()
6

Including neighbors of core neighbors >>> rn = lm_random.spillover(neighbors_on=True) >>> (rn[‘components’][:, 12] > 0).sum() 26 >>> (rn[“components”][:, 13] > 0).sum() 34 >>> (rn[“spill_over”][:, 12] > 0).sum() 8