Site analyses can benefit greatly from using “drive-time” isochrones to define the study area. Drive time isochrones are often significantly different from simple buffer areas which disregard natural barriers such as rivers or slow roads.

Of course, creating drive time isochrones requires more input data and more compute-intensive algorithms than a simple buffer analysis. It is necessary to create a routable network graph with adequate weights to be used by the routing algorithm.

One of the most popular routing  applications in the open source world is pgRouting for PostGIS enabled databases. I’ve already shown how to create drive time isochrones for one network node based on pgRouting and QGIS.  Today, I’ll show how to create drive time isochrones for a set of points – in this case all airports in Finland.

The first step is to find the closest network node to every airport:

ALTER TABLE airport
   ADD COLUMN nearest_node integer;

CREATE TABLE temp AS
   SELECT a.gid, b.id, min(a.dist)
   FROM
     (SELECT airport.gid, 
             min(distance(airport.the_geom, node.the_geom)) AS dist
      FROM airport, node
      GROUP BY airport.gid) AS a,
     (SELECT airport.gid, node.id, 
             distance(airport.the_geom, node.the_geom) AS dist
      FROM airport, node) AS b
   WHERE a.dist = b. dist
         AND a.gid = b.gid
   GROUP BY a.gid, b.id;

UPDATE airport
SET nearest_node = 
   (SELECT id 
    FROM temp
    WHERE temp.gid = airport.gid);

Then, we can calculate drive times between network nodes and “airport nodes”. I am still looking for the most efficient way to perform this calculation. The trivial solution I used for this example was to calculate all drive time values separately for each airport node (as described in “Creating Catchment Areas with pgRouting and QGIS”).
I create the table with the first node:

CREATE TABLE catchment_airport AS
SELECT 
    id,
    the_geom,
    (SELECT sum(cost) FROM (
	   SELECT * FROM shortest_path('
	   SELECT gid AS id,
		  start_id::int4 AS source,
		  end_id::int4 AS target,
		  traveltime::float8 AS cost
	   FROM network',
	   5657,
	   id,
	   false,
	   false)) AS foo ) AS cost
FROM node;

Every following node is done with an INSERT:

INSERT INTO catchment_airport (
SELECT 
    id,
    the_geom,
    (SELECT sum(cost) FROM (
	   SELECT * FROM shortest_path('
	   SELECT gid AS id,
		  start_id::int4 AS source,
		  end_id::int4 AS target,
		  traveltime::float8 AS cost
	   FROM network',
	   123, -- put a new index here!
	   id,
	   false,
	   false)) AS foo ) AS cost
FROM node);

Afterwards, I combined the values to find the minimum drive time for each network node:

CREATE table catchment_airport_final AS
SELECT id, the_geom, min (cost) AS cost
FROM catchment_airport
GROUP By id, the_geom;

The resulting point layer was imported into QGIS. Using TIN interpolation (from Interpolation plugin), you can calculate a continuous cost surface. And Contour function (from GDALTools) finally yields drive time isochrones.

Drive time isochrones around airports in northern Finland - spatial data © National Land Survey of Finland 2011

Based on this analysis, it is possible to determine how many inhabitants live within one hour driving distance from an airport or how many people have to drive longer than e.g. ninety minutes to reach any airport.