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# 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.

# 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.sqltable- 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->Openfrom the menu and browse to thenyc_census_sociodata.sqlfile- Press the "Run Query" button
- If you press the "Refresh" button in PgAdmin, the list of tables should now include at
nyc_census_sociodatatable

## 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:

-- 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

-- 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();

## Answer an Interesting Question

Answer an interesting question! "List top 10 New York neighborhoods ordered by the proportion of people who have graduate degrees."

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

# 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**.

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

The simple method is to ensure that each tract only falls in

onesummary 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:

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!

# 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:

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:

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.

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:

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.

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