adds hotspot/coldspot function

src/py/crankshaft/crankshaft/clustering/__init__.py

@@ -1,3 +1,4 @@
 """Import all functions from for clustering"""
 from moran import *
 from kmeans import *
+from getis import *

src/py/crankshaft/crankshaft/clustering/getis.py

@@ -0,0 +1,51 @@
+"""
+Moran's I geostatistics (global clustering & outliers presence)
+"""
+
+# TODO: Fill in local neighbors which have null/NoneType values with the
+#       average of the their neighborhood
+
+import pysal as ps
+import plpy
+from collections import OrderedDict
+
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
+# High level interface ---------------------------------------
+
+def getis_ord(subquery, attr,
+              w_type, num_ngbrs, permutations, geom_col, id_col):
+    """
+    Getis-Ord's G
+    Implementation building neighbors with a PostGIS database and Getis-Ord's G
+     hotspot/coldspot analysis with PySAL.
+    Andy Eschbacher
+    """
+
+    # geometries with attributes that are null are ignored
+    # resulting in a collection of not as near neighbors if kNN is chosen
+
+    qvals = OrderedDict([("id_col", id_col),
+                         ("attr1", attr),
+                         ("geom_col", geom_col),
+                         ("subquery", subquery),
+                         ("num_ngbrs", num_ngbrs)])
+
+    query = pu.construct_neighbor_query(w_type, qvals)
+
+    try:
+        result = plpy.execute(query)
+        # if there are no neighbors, exit
+        if len(result) == 0:
+            return pu.empty_zipped_array(3)
+    except plpy.SPIError, e:
+        plpy.error('Query failed: %s' % e)
+
+    attr_vals = pu.get_attributes(result)
+    weight = pu.get_weight(result, w_type, num_ngbrs)
+
+    # calculate LISA values
+    getis = ps.esda.getisord(attr_vals, weight, star=True)
+
+    return zip(getis.z_sim, getis.p_sim, weight.id_order)