Monthly Archives: March 2014

Spatial capabilities in PostgreSQL with PostGIS

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When dealing with geographic data in PostgreSQL, at some point we’re going to want to lookup rows based purely on their location relative to one another.

With smaller volumes of data, we can get away quite easily with just latitude / longitude and some maths, but once we get past a certain point we’re going to want to be able to do index lookups based on distance from a specific location; this is where the PostGIS extension is priceless.

Installing PostGIS on Debian Wheezy

Installing under Debian when PostgreSQL has been installed via the package manager is dead simple. I’m using the 2.1 version packaged for a 9.3 server from the apt.postgresql.org repository, but you should select the package that matches your server version. It’s also possible to install version 1.5 directly from Wheezy main if you don’t want to add other repositories to apt.

$ sudo apt-get install postgresql-9.3-postgis-2.1

This will install the PostGIS contrib modules into the /usr/share/postgresql/9.3/contrib directory rather than the “extension” directory used by most other PostgreSQL packages in Debian.

Some of the machines I look after have PostgreSQL installed from source for various reasons (but mainly because historically packages from Debain weren’t very timely), and these machines require a slightly more lengthy setup process. To compile PostGIS from source we need to install some dependencies, one of which is the development files for the geospatial data abstraction library and these are in the “libgdal-dev” package; however this depends on the “libpq-dev” package which will most likely interfere with our source install. There are two options here; either install the package without it’s dependencies (make a note you’ve done it to avoid future dependency problems), or roll gdal from source:

First we install the required dependencies, these are listed in the PostGIS docs and for my test machine that already has everything required to compile PostgreSQL already these are:

$ sudo apt-get install libgeos-dev libproj-dev libjson-c-dev libjson0-dev

Then install the gdal dev libraries in whichever manner suits:

$ sudo apt-get download libgdal-dev
$ sudo dpkg --force-all -i libgdal-dev_1.9.0-3.1_amd64.deb

OR

$ cd /tmp
$ wget http://download.osgeo.org/gdal/1.10.1/gdal-1.10.1.tar.gz
$ tar xvfz gdal-1.10.1.tar.gz && cd gdal-1.10.1
$ ./configure
$ make
$ sudo make install

Once this is done we can compile and install PostGIS:

$ cd /tmp
$ wget http://download.osgeo.org/postgis/source/postgis-2.1.1.tar.gz
$ tar xvfz postgis-2.1.1.tar.gz && cd postgis-2.1.1
$ ./configure
$ make				
$ sudo make install

Getting started with PostGIS

Now we’ve got PostGIS installed we just need to create the extension in the database:

$ psql -U glyn -d test -c 'CREATE EXTENSION postgis; CREATE EXTENSION postgis_topology;'

OR on and on 9.0 and lower run the sql scripts in the contrib/postgis-2.1 directory:

$ psql -U glyn -d test -f postgis.sql
$ psql -U glyn -d test -f postgis_comments.sql
$ psql -U glyn -d test -f spatial_ref_sys.sql
$ psql -U glyn -d test -f rtpostgis.sql
$ psql -U glyn -d test -f raster_comments.sql
$ psql -U glyn -d test -f topology.sql
$ psql -U glyn -d test -f topology_comments.sql

So let’s generate some data for testing; we create a table called “friends” with 90k rows which stores their locations as latitude and longitude values. Admittedly the distribution in this table won’t be that realistic, but it should suffice for testing:

[postgres]
INSERT INTO friends
SELECT b.unnest || ‘ v.’ || generate_series,
CASE WHEN random() > 0.5 THEN ‘Somewhere Else’ ELSE ‘Somewhere’ END,
1.0838637+random()*(CASE WHEN random() > 0.5 THEN -1 ELSE 1 END),
52.7389201+random()*(CASE WHEN random() > 0.5 THEN -1 ELSE 1 END)
FROM generate_series(1,10000)
CROSS JOIN (SELECT unnest(ARRAY[‘White Wonder’,’Colonel K’,’El Loco’,’Count Duckula’,’Leatherhead’,’Barron Greenback’,’Ernest Penfold’,’Professor Heinrich Von Squawkencluck’,’Flying Officer Buggles Pigeon’])) b;
[/postgres]

In the scenario where we don’t have PostGIS we can create an sql function to calculate earth distance between two points, but any relative distances will always be an unknown so can’t be indexed:

[postgres]
CREATE OR REPLACE FUNCTION earth_distance_miles(lat1 float, lat2 float, long1 float, long2 float)
RETURNS double precision
AS
‘SELECT 3963.0*acos(sin($1/57.2958)*sin($2/57.2958)+cos($1/57.2958)*cos($2/57.2958)*cos(($4/57.2958)-($3/57.2958)));’
LANGUAGE SQL IMMUTABLE;
[/postgres]

This makes listing out “friends” within a mile pretty easy:

[postgres]
EXPLAIN ANALYZE SELECT f.*,
earth_distance_miles(52.7389201, f.latitude, 1.0838637, f.longitude) AS dist_miles
FROM friends f WHERE earth_distance_miles(52.7389201, f.latitude, 1.0838637, f.longitude) <= 1
ORDER BY earth_distance_miles(52.7389201, f.latitude, 1.0838637, f.longitude);
[/postgres]

                                           QUERY PLAN
------------------------------------------------------------------------------------------------------------------------
 Sort  (cost=10988.40..11063.40 rows=30000 width=108) (actual time=160.006..160.013 rows=29 loops=1)
   Sort Key: ((3963::double precision * acos(((0.795884736186082::double precision * sin((latitude / 57.2958::double precision))) 
   	+ ((0.605448170123596::double precision * cos((latitude / 57.2958::double precision))) * cos(((longitude / 57.2958::double precision) 
   	- 0.0189169834438126::double precision)))))))
   Sort Method:  quicksort  Memory: 30kB
   ->  Seq Scan on friends f  (cost=0.00..7510.00 rows=30000 width=108) (actual time=19.993..159.930 rows=29 loops=1)
         Filter: ((3963::double precision * acos(((0.795884736186082::double precision * sin((latitude / 57.2958::double precision))) 
         	+ ((0.605448170123596::double precision * cos((latitude / 57.2958::double precision))) 
         	* cos(((longitude / 57.2958::double precision) - 0.0189169834438126::double precision)))))) <= 1::double precision)
 Total runtime: 160.069 ms

Now lets put PostGIS to work and add in an indexed geography column:

[postgres]
ALTER TABLE friends ADD COLUMN geog geography(Point,4326); — SRID 4326 for WGS84
UPDATE friends SET geog = ST_MakePoint(longitude, latitude);
CREATE INDEX ON friends USING GIST (geog);
[/postgres]

Now let’s try to list out our “friends” within a mile again, this time making use of the PostGIS ST_Distance and ST_DWithin functions:

[postgres]
EXPLAIN ANALYZE SELECT f.*,
ST_Distance(f.geog, ST_MakePoint(1.0838637, 52.7389201))/1609 AS dist_miles
FROM friends f WHERE ST_DWithin(f.geog, ST_MakePoint(1.0838637, 52.7389201), 1609)
ORDER BY ST_Distance(f.geog, ST_MakePoint(1.0838637, 52.7389201));
[/postgres]

                                           QUERY PLAN
------------------------------------------------------------------------------------------------------------------------
 Sort  (cost=37.90..37.90 rows=1 width=108) (actual time=1.756..1.760 rows=29 loops=1)
   Sort Key: (_st_distance(geog, '0101000020E610000017258D768157F13FB4ED0FEF945E4A40'::geography, 0::double precision, true))
   Sort Method:  quicksort  Memory: 32kB
   ->  Bitmap Heap Scan on friends f  (cost=2.39..37.89 rows=1 width=108) (actual time=0.908..1.684 rows=29 loops=1)
         Recheck Cond: (geog && '0101000020E610000017258D768157F13FB4ED0FEF945E4A40'::geography)
         Filter: (('0101000020E610000017258D768157F13FB4ED0FEF945E4A40'::geography && _st_expand(geog, 1609::double precision)) 
         	AND _st_dwithin(geog, '0101000020E610000017258D768157F13FB4ED0FEF945E4A40'::geography, 1609::double precision, true))
         ->  Bitmap Index Scan on friends_geog_idx  (cost=0.00..2.39 rows=16 width=0) (actual time=0.351..0.351 rows=45 loops=1)
               Index Cond: (geog && '0101000020E610000017258D768157F13FB4ED0FEF945E4A40'::geography)
 Total runtime: 1.821 ms

This shows a marked improvement from a query time of 160.069ms down to 1.821 ms. Obviously our mileage will vary depending on the quantity of data in the table, it’s distribution and just how many rows we want to retrieve.

UK Geographic postcode data, latitude longitude, Royal Mail PAF and Ordnance Survey data

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When it comes to getting accurate geographic data for UK postcodes I only know of 2 sources; Ordnance Survey OpenData which is free, or the Postcode Address File (PAF) from the Royal Mail which costs a fair whack. If detail of individual addresses is required then there’s little choice other than to purchase the PAF data, but if only postcodes and coordinates are required then the OS data is fine.

Rather than latitude and longitude, both sources supply cartesian coordinates in the form of eastings and northings. It’s not a big issue though and we don’t even have to understand the maths involved in conversion because there are a few great cartographic tools out there that will do the conversions for us; perhaps the most popular of these is the Proj4 cartographic libraries.

Conversion to WGS84 / latitude and longitude

In my case I want to pull the data along with latitude and longitude into a PostgreSQL database and I have 2 options; do the conversion before importing with the cs2cs tool or use the PostGIS module within the database to compute them afterwards or during the load from an intermediate table.

The cs2cs tool takes input from stdin or a file and outputs anything after the eastings and northings to stdout. E.g. to convert from the British national grid, we’d do something like:

$ echo '7811 340198 other data' | cs2cs -f '%.7f' +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894 +units=m +no_defs +to +proj=latlong +ellps=WGS84 +towgs84=0,0,0 +no_defs

A breakdown of the parameters passed above:

  • -f ‘%.7f’ – Format of output – 7 decimal places
  • +proj=tmerc – Transverse Mercator Projection
  • +lat_0=49 – National grid true origin latitude
  • +lon_0=-2 – National grid true origin longitude
  • +k=0.9996012717 – National Grid scale factor on central meridian
  • +x_0=400000 – Easting of true origin metres
  • +y_0=-100000 – Northing of true origin metres
  • +ellps=airy – National grid is based on airy 1830 ellipsoid
  • +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894
    – The 7 Bursa Wolf transformation parameters, used in our projection transformation to approximate the transformation from horizontal datum
  • +units=m – Units in metres
  • +no_defs – Don’t use the /usr/share/proj/proj_def.dat defaults file (they’re for US maps)
  • +to +proj=latlong – Ouptut latitude and longitude
  • +ellps=WGS84 – Ouptut WGS84 ellipsoid
  • +towgs84=0,0,0 – WGS84 datum shifts set to zero

If we have the PostGIS module loaded in PostgreSQL we could calculate our latitude and longitude similarly with

[postgres]
SELECT ST_x(ST_transform(ST_GeomFromText(‘POINT(‘||’457811 340198’||’)’,27700),4326)) AS longitude,
ST_y(ST_transform(ST_GeomFromText(‘POINT(‘||’457811 340198’||’)’,27700),4326)) AS latitude;
[/postgres]

What we’re doing here is using PostGIS to first transform from our eastings / northings into a geometric type then transforming that back into latitude / longitude. My gut feeling is that there should be a more direct way to do the conversion than the above; but I couldn’t find any. The values we pass in are SRIDs; 27700 to represent SRID 27700 for OSGB 1936 / British National Grid, and 4326 to represent SRID 4326 for WGS84. For any references on the TM75 / Irish Grid we’d use SRID 29903 instead.

OrdananceSurvey Data

Getting the data from the data from OrdananceSurvey is fairly easy; go to the OpenData website, choose the Code Point data option and fill in the form to get a download link emailed to you.

The data may be lacking a few more postcodes than the Royal Mail data, but the geographic coordinates are much more accurate. Once you’ve got the data you can use the methods above to generate the latitude and longitude.

My process for importing the data is composed of 3 steps, firstly I want the area information along with my postcodes and they’re supplied in separate worksheets in an excel document named Codelist.xls. To quickly pull this data out I use the ssconvert utility supplied with Gnumeric:

$ sudo apt-get install gnumeric --no-install-recommends
$ ssconvert -S ~/codepoint_data/Doc/Codelist.xls ~/codepoint_data/Doc/Codelist_%s.csv > /dev/null 2>&1

The tool wants to use X and will spew out warnings if you’re on a headless machine, I just pipe these to null (ignorance is bliss). Then I remove the duplicates and add in the area types with sed:

$ sed -e 's/$/,CTY/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_CTY.csv
$ sed -e 's/$/,DIS/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_DIS.csv
$ sed -e 's/$/,DIW/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_DIW.csv
$ sed -e 's/$/,LBO/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_LBO.csv
$ sed -e 's/$/,LBW/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_LBW.csv
$ sed -e 's/$/,MTD/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_MTD.csv
$ sed -e 's/$/,MTW/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_MTW.csv
$ sed -e 's/$/,UTA/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_UTA.csv
$ sed -e 's/$/,UTE/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_UTE.csv
$ sed -e 's/$/,UTW/; s/|/,/g; /\(DET\)/d' -i ~/codepoint_data/Doc/Codelist_UTW.csv

Then I use a perl script to generate the latitude and longitude:

$ sudo apt-get install proj-bin
$ sudo perl -MCPAN -e 'install Text::CSV'
$ sudo perl -MCPAN -e 'install Geo::Proj4'
	
$ os_convert.pl -i "~/codepoint_data/Data/CSV/*.csv" -o ~/codepoint_data/Data/all_areas_20140324.csv

I can now copy these directly into my database with a plpgsql script (See the link at the end of the post for the perl script and a database function to import into PostgreSQL)

Royal Mail Postcode Address File

If you’ve paid for the “postzon” data from the Royal Mail, you can go about generating the latitude and longitude in a similar way. There’s artefacts in the data from RM that shows its legacy, but they provide some (fairly convoluted) documentation here to help with deciphering it. The main gotchas being:

  • The cartesian coordinates are only accurate to 100 meters, which can be annoying as it can place you on the wrong street.
  • You may have to fiddle with the northing values before you can import them, as for 7 digit northings the leftmost digit is alphabetic (P|O=12,U|T=11,Z|Y=10) to fit them into a 5 character field (the least significant digit is always truncated).
  • The cartesian coordinates for Northern Ireland (postcodes starting BT) are for the TM75 Irish Grid system, whereas the rest are for the OSGB 1936 / British National Grid system. No fault of RM, but these need to be converted with the different SRID.
  • If you’re using the relational full file format there’s a few rules to follow regarding their schema design, one being that organisation keys for large organisations relate to address keys in their organisations table instead of organisation keys.

Like the OS data my process for importing is to post process the data with a perl script:

$ paf_convert.pl -i ~/rm_data/pzone100.c01 -o ~/rm_data/pzone100.c02

Again, I then import the data into my database with a plpgsql script (linked below).

Edit 22/05/2014: I’ve now tested an actual quarterly update, and created a separate version of the plpgsql functions using postgis to do all the latitude / longitude parts for both import and update (hence the script above isn’t required if you are using postgis)

You can find the scripts in my git repository at postgresql/geographic_data (mirrored also on github glynastill/geographic_data). I’m not totally happy with the speed of the perl scripts that do the latitude / longitude creation before loading; they could be much faster with a few minor changes. I’m sure the plpgsql functions could easily be translated for other databases without too much hassle if you really want to.