source: trunk/workshop-foss4g/joins_advanced.rst @ 6

.. _joins_advanced:

Section 19: More Spatial Joins
==============================

In the last section we saw the :command:`ST_Centroid(geometry)` and :command:`ST_Union([geometry])` functions, and some simple examples. In this section we will do some more elaborate things with them.

.. _creatingtractstable:

Creating a Census Tracts Table
------------------------------

In the workshop ``\data\`` directory, is a file that includes attribute data, but no geometry, ``nyc_census_sociodata.sql``. The table includes interesting socioeconomic data about New York: commute times, incomes, and education attainment. There is just one problem. The data are summarized by "census tract" and we have no census tract spatial data! 

In this section we will

 * Load the ``nyc_census_sociodata.sql`` table
 * Create a spatial table for census tracts 
 * Join the attribute data to the spatial data
 * Carry out some analysis using our new data
 
Loading nyc_census_sociodata.sql
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 #. Open the SQL query window in PgAdmin
 #. Select **File->Open** from the menu and browse to the ``nyc_census_sociodata.sql`` file
 #. Press the "Run Query" button
 #. If you press the "Refresh" button in PgAdmin, the list of tables should now include at ``nyc_census_sociodata`` table
 
Creating a Census Tracts Table
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
As we saw in the previous section, we can build up higher level geometries from the census block by summarizing on substrings of the ``blkid`` key. In order to get census tracts, we need to summarize grouping on the first 11 characters of the ``blkid``.
 
  ::

    360610001009000 = 36 061 00100 9000

    36     = State of New York 
    061    = New York County (Manhattan)
    000100 = Census Tract
    9      = Census Block Group
    000    = Census Block

Create the new table using the :command:`ST_Union` aggregate:
 
.. code-block:: sql
   
   -- Make the tracts table
   CREATE TABLE nyc_census_tract_geoms AS
   SELECT 
     ST_Union(the_geom) AS the_geom, 
     SubStr(blkid,1,11) AS tractid
   FROM nyc_census_blocks
   GROUP BY tractid;
     
   -- Index the tractid
   CREATE INDEX nyc_census_tract_geoms_tractid_idx ON nyc_census_tract_geoms (tractid);
     
   -- Update the geometry_columns table
   SELECT Populate_Geometry_Columns();

Join the Attributes to the Spatial Data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Join the table of tract geometries to the table of tract attributes with a standard attribute join
  
.. code-block:: sql
  
  -- Make the tracts table
  CREATE TABLE nyc_census_tracts AS
  SELECT 
    g.the_geom,
    a.*
  FROM nyc_census_tract_geoms g
  JOIN nyc_census_sociodata a
  ON g.tractid = a.tractid;
    
  -- Index the geometries
  CREATE INDEX nyc_census_tract_gidx ON nyc_census_tracts USING GIST (the_geom);
    
  -- Update the geometry_columns table
  SELECT Populate_Geometry_Columns();

.. _interestingquestion:

Answer an Interesting Question
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     
Answer an interesting question! "List top 10 New York neighborhoods ordered by the proportion of people who have graduate degrees."
  
.. code-block:: sql
  
  SELECT 
    Round(100.0 * Sum(t.edu_graduate_dipl) / Sum(t.edu_total), 1) AS graduate_pct, 
    n.name, n.boroname 
  FROM nyc_neighborhoods n 
  JOIN nyc_census_tracts t 
  ON ST_Intersects(n.the_geom, t.the_geom) 
  WHERE t.edu_total > 0
  GROUP BY n.name, n.boroname
  ORDER BY graduate_pct DESC
  LIMIT 10;

We sum up the statistics we are interested, then divide them together at the end. In order to avoid divide-by-zero errors, we don't bother bringing in tracts that have a population count of zero.

::
  
   graduate_pct |       name        | boroname  
  --------------+-------------------+-----------
           40.4 | Carnegie Hill     | Manhattan
           40.2 | Flatbush          | Brooklyn
           34.8 | Battery Park      | Manhattan
           33.9 | North Sutton Area | Manhattan
           33.4 | Upper West Side   | Manhattan
           33.3 | Upper East Side   | Manhattan
           32.0 | Tribeca           | Manhattan
           31.8 | Greenwich Village | Manhattan
           29.8 | West Village      | Manhattan
           29.7 | Central Park      | Manhattan
    
  
.. _polypolyjoins:

Polygon/Polygon Joins
---------------------

In our interesting query (in :ref:`interestingquestion`) we used the :command:`ST_Intersects(geometry_a, geometry_b)` function to determine which census tract polygons to include in each neighborhood summary. Which leads to the question: what if a tract falls on the border between two neighborhoods? It will intersect both, and so will be included in the summary statistics for **both**.

.. image:: ./screenshots/centroid_neighborhood.png

To avoid this kind of double counting there are two methods:

 * The simple method is to ensure that each tract only falls in **one** summary area (using :command:`ST_Centroid(geometry)`)
 * The complex method is to divide crossing tracts at the borders (using :command:`ST_Intersection(geometry,geometry)`)
 
Here is an example of using the simple method to avoid double counting in our graduate education query:

.. code-block:: sql

  SELECT 
    Round(100.0 * Sum(t.edu_graduate_dipl) / Sum(t.edu_total), 1) AS graduate_pct, 
    n.name, n.boroname 
  FROM nyc_neighborhoods n 
  JOIN nyc_census_tracts t 
  ON ST_Contains(n.the_geom, ST_Centroid(t.the_geom)) 
  WHERE t.edu_total > 0
  GROUP BY n.name, n.boroname
  ORDER BY graduate_pct DESC
  LIMIT 10;
  
Note that the query takes longer to run now, because the :command:`ST_Centroid` function  has to be run on every census tract.

::

   graduate_pct |       name        | boroname  
  --------------+-------------------+-----------
           49.2 | Carnegie Hill     | Manhattan
           39.5 | Battery Park      | Manhattan
           34.3 | Upper East Side   | Manhattan
           33.6 | Upper West Side   | Manhattan
           32.5 | Greenwich Village | Manhattan
           32.2 | Tribeca           | Manhattan
           31.3 | North Sutton Area | Manhattan
           30.8 | West Village      | Manhattan
           30.1 | Downtown          | Brooklyn
           28.4 | Cobble Hill       | Brooklyn
  
Avoiding double counting changes the results! 


.. _largeradiusjoins:

Large Radius Distance Joins
---------------------------

A query that is fun to ask is "How do the commute times of people near (within 500 meters) subway stations differ from those of people far away from subway stations?"

However, the question runs into some problems of double counting: many people will be within 500 meters of multiple subway stations. Compare the population of New York:

.. code-block:: sql

  SELECT Sum(popn_total)
  FROM nyc_census_blocks;
  
::

  8008278
  
With the population of the people in New York within 500 meters of a subway station:

.. code-block:: sql

  SELECT Sum(popn_total)
  FROM nyc_census_blocks census
  JOIN nyc_subway_stations subway
  ON ST_DWithin(census.the_geom, subway.the_geom, 500);
  
::

  10556898

There's more people close to the subway than there are people! Clearly, our simple SQL is making a big double-counting error. You can see the problem looking at the picture of the buffered subways.

.. image:: ./screenshots/subways_buffered.png

The solution is to ensure that we have only distinct census blocks before passing them into the summarization portion of the query. We can do that by breaking our query up into a subquery that finds the distinct blocks, wrapped in a summarization query that returns our answer:

.. code-block:: sql

  SELECT Sum(popn_total)
  FROM (
    SELECT DISTINCT ON (blkid) popn_total
    FROM nyc_census_blocks census
    JOIN nyc_subway_stations subway
    ON ST_DWithin(census.the_geom, subway.the_geom, 500)
  ) AS distinct_blocks;
  
::

  4953599

That's better! So a bit over half the population of New York is within 500m (about a 5-7 minute walk) of the subway.