diff --git a/src/pg/sql/11_markov.sql b/src/pg/sql/11_markov.sql
new file mode 100644
index 0000000..63a46ac
--- /dev/null
+++ b/src/pg/sql/11_markov.sql
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+-- Spatial Markov
+
+-- input table format:
+-- id | geom | date_1 | date_2 | date_3
+--  1 | Pt1  | 12.3   | 13.1   | 14.2
+--  2 | Pt2  | 11.0   | 13.2   | 12.5
+--  ...
+-- Sample Function call:
+-- SELECT cdb_spatial_markov('SELECT * FROM real_estate', 
+--                           Array['date_1', 'date_2', 'date_3'])
+
+
+CREATE OR REPLACE FUNCTION
+  cdb_spatial_markov (
+      subquery TEXT,
+      time_cols text[],
+      num_time_per_bin int DEFAULT 1,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs int DEFAULT 5)
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.space_time_predictions import spatial_markov
+  # TODO: use named parameters or a dictionary
+  return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
+$$ LANGUAGE plpythonu;
+
+-- input table format: identical to above but in a predictable format
+-- Sample function call:
+-- SELECT cdb_spatial_markov('SELECT * FROM real_estate',
+--                           'date_1')
+
+
+CREATE OR REPLACE FUNCTION
+  cdb_spatial_markov (
+      subquery TEXT,
+      time_col_min text,
+      time_col_max text,
+      date_format text, -- '_YYYY_MM_DD'
+      num_time_per_bin INT DEFAULT 1,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs int DEFAULT 5)
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
+$$ LANGUAGE plpythonu;
+
+-- input table format:
+-- id | geom | date  | measurement
+--  1 | Pt1  | 12/3  | 13.2
+--  2 | Pt2  | 11/5  | 11.3
+--  3 | Pt1  | 11/13 | 12.9
+--  4 | Pt3  | 12/19 | 10.1
+-- ...
+
+CREATE OR REPLACE FUNCTION
+  cdb_spatial_markov (
+      subquery TEXT,
+      time_col text,
+      num_time_per_bin INT DEFAULT 1,
+  	  permutations INT DEFAULT 99,
+  	  geom_column TEXT DEFAULT 'the_geom',
+  	  id_col TEXT DEFAULT 'cartodb_id',
+      w_type TEXT DEFAULT 'knn',
+      num_ngbrs int DEFAULT 5)
+RETURNS TABLE (moran FLOAT, quads TEXT, significance FLOAT, ids INT)
+AS $$
+  plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
+  from crankshaft.clustering import moran_local
+  # TODO: use named parameters or a dictionary
+  return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
+$$ LANGUAGE plpythonu;