PostGIS : Loading and querying data

Today I'm going to load some POIs (points of interest) in a PostGIS database and query them back. To do this you'll first need an installed version of PostgreSQL with PostGIS. I suggest to first download and install PostgreSQL and then install PostGIS with the latest version. Then you should create a spatially enabled database. I called mine pois_db.

Now we are ready to import the POIs. I first extracted 500.000 POIs from a file geodatabase into a shapefile with the ArcGIS Select tool. Then I used the PostGIS shp2pgsql tool which is located in your bin folder of your PostgreSQL installation to create a text file. This file can then be used to import the data in your database with the PostgreSQL psql tool. My commandline looked like below.

rem -s : srid (spatial reference id)
rem -c : creates a new table and populates it
rem -D : use PostgreSQL dump format
rem -i create a GiST index on the geometry column
"C:\Program Files\PostgreSQL\8.3\bin\shp2pgsql.exe" -s 4326 -c -D -i poi_500000.shp pois_db > pois.sql

More information about the usage of shp2pgsql can be found here. Note that I set the srid to 4326 which stands for WGS84. On spatialreference.org you can find a list of spatial reference ids or you can take a look at the spatial_ref_sys table in your database.
Next thing we are going to is load the pois.sql in the pois_db with the following command line.

"C:\Program Files\PostgreSQL\8.3\bin\psql" -d pois_db -f pois.sql

To query the POIs we issue the following sql command. This query checks whether the bounding box of the point intersects with the bounding box of a created rectangle polygon.

SELECT *
FROM pois
WHERE the_geom && GeomFromText('POLYGON((4.5 50.5, 5.0 50.5, 5.0 51.0, 4.5 51.0, 4.5 50.5))',4326);

This query takes in pgAdmin III with a gist index on the geometry column only 515 ms for more then 4000 found POIs. If I only ask the ids the query is down to 67 ms which is slightly slower then with Rtree but faster then with MongoDb.

I've come to the end of this post. I hope you liked it and leave a comment if you want to.

Related Posts
Pythonnet
MongoDB
Rtree

No comments: