The steps you need to follow to complete this recipe are as follows:

      $ ogr2ogr -f PostgreSQL -sql "SELECT ISO2, 
      NAME AS country_name FROM wborders WHERE REGION=2" -nlt 
      MULTIPOLYGON PG:"dbname='postgis_cookbook' user='me'
      password='mypassword'" -nln africa_countries 
      -lco SCHEMA=chp01 -lco GEOMETRY_NAME=the_geom wborders.shp

      postgis_cookbook=# SELECTST_AsText(the_geom) AS the_geom, bright_t31
FROM chp01.global_24h
ORDER BY bright_t31 DESC LIMIT 100;

The output of the preceding command is as follows:

      postgis_cookbook=# SELECT ST_AsText(f.the_geom) 
      AS the_geom, f.bright_t31, ac.iso2, ac.country_name
      FROM chp01.global_24h as f
      JOIN chp01.africa_countries as ac
      ON ST_Contains(ac.the_geom, ST_Transform(f.the_geom, 4326))
      ORDER BY f.bright_t31 DESCLIMIT 100;

The output of the preceding command is as follows:

You will now export the result of this query to a vector format supported by GDAL, such as GeoJSON, in the WGS 84 spatial reference using ogr2ogr:

      $ ogr2ogr -f GeoJSON -t_srs EPSG:4326 warmest_hs.geojson
      PG:"dbname='postgis_cookbook' user='me' password='mypassword'" -sql "
      SELECT f.the_geom as the_geom, f.bright_t31, 
             ac.iso2, ac.country_name
      FROM chp01.global_24h as f JOIN chp01.africa_countries as ac
      ON ST_Contains(ac.the_geom, ST_Transform(f.the_geom, 4326))
      ORDER BY f.bright_t31 DESC LIMIT 100"

      $ ogr2ogr -t_srs EPSG:4326 -f CSV -lco GEOMETRY=AS_XY 
      -lco SEPARATOR=TAB warmest_hs.csv PG:"dbname='postgis_cookbook' 
       user='me' password='mypassword'" -sql "
       SELECT f.the_geom, f.bright_t31,
         ac.iso2, ac.country_name 
       FROM chp01.global_24h as f JOIN chp01.africa_countries as ac 
       ON ST_Contains(ac.the_geom, ST_Transform(f.the_geom, 4326)) 
       ORDER BY f.bright_t31 DESC  LIMIT 100"

GDAL is an open source library that comes together with several command-line utilities, which let the user translate and process raster and vector geodatasets into a plethora of formats. In the case of vector datasets, there is a GDAL sublibrary for managing vector datasets named OGR (therefore, when talking about vector datasets in the context of GDAL, we can also use the expression OGR dataset).

When you are working with an OGR dataset, two of the most popular OGR commands are ogrinfo, which lists many kinds of information from an OGR dataset, and ogr2ogr, which converts the OGR dataset from one format to another.

It is possible to retrieve a list of the supported OGR vector formats using the -formats option on any OGR commands, for example, with ogr2ogr:

$ ogr2ogr --formats

The output of the preceding command is as follows:

Note that some formats are read-only, while others are read/write.

PostGIS is one of the supported read/write OGR formats, so it is possible to use the OGR API or any OGR commands (such as ogrinfo and ogr2ogr) to manipulate its datasets.

The ogr2ogr command has many options and parameters; in this recipe, you have seen some of the most notable ones such as -f to define the output format, -t_srs to reproject/transform the dataset, and -sql to define an (eventually spatial) query in the input OGR dataset.

When using ogrinfo and ogr2ogr together with the desired option and parameters, you have to define the datasets. When specifying a PostGIS dataset, you need a connection string that is defined as follows:

PG:"dbname='postgis_cookbook' user='me' password='mypassword'"

You can find more information about the ogrinfo and ogr2ogr commands on the GDAL website available at http://www.gdal.org.

If you need more information about the PostGIS driver, you should check its related documentation page available at http://www.gdal.org/drv_pg.html.