diff --git a/src/pg/sql/11_markov.sql b/src/pg/sql/11_markov.sql
index 63a46ac..fa7c838 100644
--- a/src/pg/sql/11_markov.sql
+++ b/src/pg/sql/11_markov.sql
@@ -6,7 +6,7 @@
 --  2 | Pt2  | 11.0   | 13.2   | 12.5
 --  ...
 -- Sample Function call:
--- SELECT cdb_spatial_markov('SELECT * FROM real_estate', 
+-- SELECT cdb_spatial_markov('SELECT * FROM real_estate',
 --                           Array['date_1', 'date_2', 'date_3'])
 
 
@@ -16,7 +16,7 @@ CREATE OR REPLACE FUNCTION
       time_cols text[],
       num_time_per_bin int DEFAULT 1,
   	  permutations INT DEFAULT 99,
-  	  geom_column TEXT DEFAULT 'the_geom',
+  	  geom_col TEXT DEFAULT 'the_geom',
   	  id_col TEXT DEFAULT 'cartodb_id',
       w_type TEXT DEFAULT 'knn',
       num_ngbrs int DEFAULT 5)
@@ -25,7 +25,7 @@ AS $$
   plpy.execute('SELECT cdb_crankshaft._cdb_crankshaft_activate_py()')
   from crankshaft.space_time_predictions import spatial_markov
   # TODO: use named parameters or a dictionary
-  return spatial_markov(subquery, time_cols, permutations, geom_column, id_col, w_type, num_ngbrs)
+  return def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, geom_col, id_col, w_type, num_ngbrs)
 $$ LANGUAGE plpythonu;
 
 -- input table format: identical to above but in a predictable format
@@ -34,48 +34,48 @@ $$ LANGUAGE plpythonu;
 --                           '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;
+-- 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;