adds docs

src/pg/sql/22_spatial_lag.sql

@@ -0,0 +1,16 @@
+-- Spatial Lag with kNN neighbors (internal function)
+CREATE OR REPLACE FUNCTION
+  CDB_SpatialLag(
+      subquery TEXT,
+      column_name TEXT,
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs INT DEFAULT 5,
+      geom_col TEXT DEFAULT 'the_geom',
+      id_col TEXT DEFAULT 'cartodb_id')
+RETURNS TABLE (spatial_lag NUMERIC, rowid INT)
+AS $$
+  from crankshaft.spatial_lag import SpatialLag
+  s_lag = SpatialLag()
+  return s_lag.spatial_lag(subquery, column_name, w_type,
+                           num_ngbrs, geom_col, id_col)
+$$ LANGUAGE plpythonu;

src/pg/test/expected/20_spatial_lag_test.out

@@ -0,0 +1,47 @@
+\pset format unaligned
+\set ECHO all
+\i test/fixtures/spatial_lag_file.sql
+SET client_min_messages TO WARNING;
+\set ECHO none
+rowid|spatial_lag
+26|1.5558245520965668
+36|0.86458398182170748
+47|0.95270659529918711
+48|1.2321749304579666
+52|0.7067108549416905
+63|1.7648393915166776
+70|8.5933781209139841
+74|8.585129430302981
+81|1.2762005195217829
+96|8.5862165805895536
+97|4.4808366319910027
+112|11.952508899443769
+113|9.8633331700241413
+116|11.950331957611654
+117|4.6317289221482971
+119|1.5003998060451806
+125|4.623201011124598
+140|0.81320866139607006
+143|1.1652798506659032
+145|1.0029589151893603
+146|0.73142355952732685
+148|0.89698376389437184
+151|0.89265718517960235
+153|4.4672874115845964
+154|0.79363641536354224
+160|4.4995909659653517
+174|0.99031984840512266
+201|0.9850340860853366
+203|0.95995789366679918
+205|0.44649384203794357
+210|0.84287567979703126
+216|0.45638121464530523
+221|0.83657469267602325
+222|0.32468328149670383
+226|0.96856924566947733
+234|0.98029560059066778
+236|0.36577884387846982
+239|0.46532563118851461
+265|0.55308241153547621
+274|0.37779583501369646
+(40 rows)

src/pg/test/fixtures/spatial_lag_file.sql

@@ -0,0 +1,45 @@
+SET client_min_messages TO WARNING;
+\set ECHO none
+-- test table (Manhattan census tracts with bachelors' degree and above data)
+CREATE TABLE spatial_lag_file (cartodb_id integer, the_geom geometry, value float);
+INSERT INTO spatial_lag_file VALUES
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src/pg/test/sql/20_spatial_lag_test.sql

@@ -0,0 +1,11 @@
+\pset format unaligned
+\set ECHO all
+\i test/fixtures/spatial_lag_file.sql
+
+-- Spatial Lag test
+
+SELECT m.rowid, m.spatial_lag
+  FROM spatial_lag_file
+  JOIN cdb_crankshaft.CDB_SpatialLag('SELECT * FROM spatial_lag_file', 'value', 'knn',5, 'the_geom','cartodb_id') m
+    ON spatial_lag_file.cartodb_id = m.rowid
+  ORDER BY spatial_lag_file.cartodb_id;

src/py/crankshaft/crankshaft/analysis_data_provider.py

@@ -26,39 +26,63 @@ def verify_data(func):
 
 class AnalysisDataProvider(object):
     @verify_data
-    def get_getis(self, w_type, params):
+    def get_weight_and_attrs(self, w_type, params):
-        """fetch data for getis ord's g"""
+        """fetch data for moran's i, getis, and spark markov analyses
-        query = pu.construct_neighbor_query(w_type, params)
+        This method returns a feature id, a list of its neighbors ids, and the
-        return plpy.execute(query)
+        attribute(s) of the feature.
 
-    @verify_data
+        Args:
-    def get_markov(self, w_type, params):
+            w_type (str): Type of weight. One of ``knn`` (default) or
-        """fetch data for spatial markov"""
+              ``queen``.
-        query = pu.construct_neighbor_query(w_type, params)
+            params (:obj:`dict`): Parameters for data retrieval. The keys are
-        return plpy.execute(query)
+              defined below, with the descriptions of their values.
-
+              - `id_col` (str): Name of database index. Defaults to
-    @verify_data
+                `cartodb_id`
-    def get_moran(self, w_type, params):
+              - `geom_col` (str): Geometry column. Defaults to `the_geom`.
-        """fetch data for moran's i analyses"""
+              - `subquery` (str): Query to get access to data
+              - `num_ngbrs` (int, optional): Number of neighbors if using kNN
+              - `time_cols` (list of str, optional): If using with spatial
+                markov, this is a list of columns for the analysis. They should
+                be ordered in time.
+              - `numerator` (str, optional): The numerator in Moran's I local
+                rate
+              - `denominator` (str, optional): Used in conjunction with
+                `numerator`.
+        """
+        if params.get('w_type') == 'queen':
+            geom_type = plpy.execute('''
+                SELECT DISTINCT ST_GeometryType("{geom_col}") as g
+                FROM ({subquery}) as _w
+                WHERE "{geom_col}" is not null;
+            '''.format(
+                geom_col=params.get('geom_col'),
+                subquery=prams.get('subquery')
+            ))
+            if geom_type[0]['g'] not in ('ST_Polygon', 'ST_MultiPolygon'):
+                raise plpy.error(
+                    'Polygon geometries are needed when using `queen` weights '
+                    'with this analysis. {} was found instead.'.format(
+                        geom_type[0]['g']))
         query = pu.construct_neighbor_query(w_type, params)
         return plpy.execute(query)
 
     @verify_data
     def get_nonspatial_kmeans(self, params):
         """
-            Fetch data for non-spatial k-means.
+        Fetch data for non-spatial k-means.
 
-            Inputs - a dict (params) with the following keys:
+        Args:
-                colnames: a (text) list of column names (e.g.,
+            params (:obj:`dict`) - A :obj:`dict` with the following keys:
-                          `['andy', 'cookie']`)
+              - colnames: a (text) list of column names (e.g.,
-                id_col: the name of the id column (e.g., `'cartodb_id'`)
+                        `['andy', 'cookie']`)
-                subquery: the subquery for exposing the data (e.g.,
+              - id_col: the name of the id column (e.g., `'cartodb_id'`)
-                          SELECT * FROM favorite_things)
+              - subquery: the subquery for exposing the data (e.g.,
-            Output:
+                        SELECT * FROM favorite_things)
-                A SQL query for packaging the data for consumption within
+        Returns:
-                `KMeans().nonspatial`. Format will be a list of length one,
+            `plpy.respone`: A response from the database. The data has been
-                with the first element a dict with keys ('rowid', 'attr1',
+            packaged consumption within `KMeans().nonspatial`. Format will be a
-                'attr2', ...)
+            list of length one, with the first element a dict with keys
+            ('rowid', 'attr1', 'attr2', ...)
         """
         agg_cols = ', '.join([
             'array_agg({0}) As arr_col{1}'.format(val, idx+1)

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

@@ -37,7 +37,7 @@ class Getis(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_getis(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
         attr_vals = pu.get_attributes(result)
 
         # build PySAL weight object

src/py/crankshaft/crankshaft/clustering/moran.py

@@ -36,7 +36,7 @@ class Moran(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_moran(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         # collect attributes
         attr_vals = pu.get_attributes(result)
@@ -66,7 +66,7 @@ class Moran(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_moran(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         attr_vals = pu.get_attributes(result)
         weight = pu.get_weight(result, w_type, num_ngbrs)
@@ -93,7 +93,7 @@ class Moran(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_moran(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         # collect attributes
         numer = pu.get_attributes(result, 1)
@@ -123,7 +123,7 @@ class Moran(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_moran(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         # collect attributes
         numer = pu.get_attributes(result, 1)
@@ -154,7 +154,7 @@ class Moran(object):
                               ("subquery", subquery),
                               ("num_ngbrs", num_ngbrs)])
 
-        result = self.data_provider.get_moran(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         # collect attributes
         attr1_vals = pu.get_attributes(result, 1)

src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -9,14 +9,16 @@ import pysal as ps
 
 def construct_neighbor_query(w_type, query_vals):
     """Return query (a string) used for finding neighbors
-        @param w_type text: type of neighbors to calculate ('knn' or 'queen')
+
-        @param query_vals dict: values used to construct the query
+        Args:
+            w_type (:obj:`str`): type of neighbors to calculate. One of 'knn'
+              or 'queen')
+            query_vals (:obj:`dict`): values used to construct the query
     """
 
-    if w_type.lower() == 'knn':
+    if w_type.lower() == 'queen':
-        return knn(query_vals)
-    else:
         return queen(query_vals)
+    return knn(query_vals)
 
 
 # Build weight object

src/py/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -61,7 +61,7 @@ class Markov(object):
                   "subquery": subquery,
                   "num_ngbrs": num_ngbrs}
 
-        result = self.data_provider.get_markov(w_type, params)
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
 
         # build weight
         weights = pu.get_weight(result, w_type)

src/py/crankshaft/crankshaft/spatial_lag/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from for spatial lag"""
+from spatial_lag import SpatialLag

src/py/crankshaft/crankshaft/spatial_lag/spatial_lag.py

@@ -0,0 +1,46 @@
+"""
+Spatial Lag (using local kNN neighbors identifying spatial lag for a feature)
+"""
+
+from collections import OrderedDict
+import pysal as ps
+
+# crankshaft module
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.pysal_utils as pu
+
+# High level interface ---------------------------------------
+
+
+class SpatialLag(object):
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def spatial_lag(self, subquery, attr,
+                    w_type, num_ngbrs, geom_col, id_col):
+        """
+        Querying spatial lags for kNN neighbors
+        """
+
+        # geometries with attributes that are null are ignored
+        # resulting in a collection of not as near neighbors
+
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_weight_and_attrs(w_type, params)
+
+        attr_vals = pu.get_attributes(result)
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate spatial_lag values
+
+        spatial_lag = ps.weights.spatial_lag.lag_spatial(weight, attr_vals)
+
+        return zip(spatial_lag, weight.id_order)

src/py/crankshaft/test/test_clustering_getis.py

@@ -41,7 +41,7 @@ class FakeDataProvider(AnalysisDataProvider):
     def __init__(self, mock_data):
         self.mock_result = mock_data
 
-    def get_getis(self, w_type, param):
+    def get_weight_and_attrs(self, w_type, param):
         return self.mock_result
 
 

src/py/crankshaft/test/test_clustering_moran.py

@@ -14,7 +14,7 @@ class FakeDataProvider(AnalysisDataProvider):
     def __init__(self, mock_data):
         self.mock_result = mock_data
 
-    def get_moran(self, w_type, params):
+    def get_weight_and_attrs(self, w_type, params):
         return self.mock_result
 
 

src/py/crankshaft/test/test_space_time_dynamics.py

@@ -17,7 +17,7 @@ class FakeDataProvider(AnalysisDataProvider):
     def __init__(self, data):
         self.mock_result = data
 
-    def get_markov(self, w_type, params):
+    def get_weight_and_attrs(self, w_type, params):
         return self.mock_result
 
 

src/py/crankshaft/test/test_spatial_lag.py

@@ -0,0 +1,51 @@
+import unittest
+import numpy as np
+
+from helper import fixture_file
+from crankshaft.spatial_lag import SpatialLag
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+from collections import OrderedDict
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    """Data provider for existing parsed data"""
+    def __init__(self, mock_data):
+        self.mock_result = mock_data
+
+    def get_weight_and_attrs(self, w_type, params):  # pylint: disable=unused-argument
+        """mock get_weight_and_attrs"""
+        return self.mock_result
+
+
+class SpatialLagTest(unittest.TestCase):
+    """Testing class for Spatial Lag function"""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "mehak",
+                       "subquery": "SELECT * FROM m_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 10}
+        self.neighbors_data = json.loads(
+            open(fixture_file('lag_data.json')).read())
+        self.lag_result = json.loads(
+            open(fixture_file('lag_result.json')).read())
+
+    def test_local_stat(self):
+        """Test Spatial Lag function"""
+        data = [OrderedDict([('id', d['id']),
+                             ('attr1', d['value']),
+                             ('neighbors', d['neighbors'])])
+                for d in self.neighbors_data]
+
+        spatial = SpatialLag(FakeDataProvider(data))
+        result = spatial.spatial_lag('subquery', 'value',
+                                     'knn', 5, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        zipped_values = zip(result, self.lag_result)
+
+        for ([res_lag, _], [_, exp_lag]) in zipped_values:
+            self.assertEqual(res_lag, exp_lag)