Tag Archives: Routing

The function with the glorious name “find_node_by_nearest_link_within_distance” is part of pgRouting and can be found in matching.sql.

“This function finds nearest node as a source or target of the nearest link”
That means that we can use this function e.g. to find the best road network node for a given address.

The function returns an object of type link_point:

CREATE TYPE link_point AS (id integer, name varchar);

To access only the id value of the nearest node, you can use:

SELECT id(foo.x) 
   SELECT find_node_by_nearest_link_within_distance(
	'POINT(14.111 47.911)',
	'nw_table')::link_point as x
) AS foo

Alpha shapes are generalizations of the convex hull [1]. Convex hulls are well known and widely implemented in GIS systems. Alpha shapes are different in that they capture the shape of a point set. You can watch a great demo of how alpha shapes work on François Bélair’s website “Everything You Always Wanted to Know About Alpha Shapes But Were Afraid to Ask” I borrowed the following pictures from that site:

Alpha shapes for different values of alpha. The left one equals the convex hull of the point set.

Alpha shapes for different values of alpha. The left one equals the convex hull of the point set.

pgRouting comes with an implementation of alpha shapes. There is an alpha shape function: alphashape(sql text) and a convenience wrapper: points_as_polygon(query character varying). The weird thing is that you don’t get to set an alpha value. The only thing supplied to the function is a set of points. Let’s see what kind of results it produces!

Starting point for this experiment is a 10 km catchment zone around node #2699 in my osm road network. Travel costs to nodes are calculated using driving_distance() function. (You can find more information on using this function in Catchment Areas with pgRouting driving_distance().)

CREATE TABLE home_catchment10km AS
   FROM osm_nodes
   (SELECT * FROM driving_distance('
      SELECT gid AS id,
          meters AS cost
      FROM osm_roads',
      false)) AS route
   ON = route.vertex_id

After costs are calculated, we can create some alpha shapes. The following queries create the table and insert an alpha shape for all points with a cost of less than 1500:

CREATE TABLE home_isodist (id serial, max_cost double precision);
SELECT AddGeometryColumn('home_isodist','the_geom',4326,'POLYGON',2);

INSERT INTO home_isodist (max_cost, the_geom) (
SELECT 1500, ST_SetSRID(the_geom,4326)
    'SELECT id, ST_X(the_geom) AS x, ST_Y(the_geom) AS y FROM home_catchment10km where cost < 1500'));

In previous posts, I’ve created catchment areas by first interpolating a cost raster and creating contours from there. Now, let’s see how the two different approaches compare!

The following picture shows resulting catchment areas for 500, 1000, 1500, and 2000 meters around a central node. Colored areas show the form of pgRouting alpha shape results. Black contours show the results of the interpolation method:

Comparison of pgRouting alpha shapes and interpolation method

At first glance, results look similar enough. Alpha shape results look like a generalized version of interpolation results. I guess that it would be possible to get even closer if the alpha value could be set to a smaller value. The function should then produce a finer, more detailed polygon.

For a general overview about which areas of a network are reachable within certain costs, pgRouting alpha shapes function seems a viable alternative to the interpolation method presented in previous posts. However, the alpha value used by pgRouting seems too big to produce detailed catchment areas.


This is something I have been wanting to do for a long time: map which areas of Vienna have fast access to a certain kind of infrastructure. Now, I finally found time and data to perform this analysis. Data used is OSM road data (Cloudmade shapefile) for Austria and metro station coordinates for Vienna by Max Kossatz and Robert Harm.

Before importing the OSM roads into PostGIS, I cut out my area of interest and created a clean topology using GRASS v.clean.break. Once loaded into the database, assign_vertex_id() function does the rest and the network is ready for routing and distance calculations.
For the metro stations, I calculated the nearest network node using George MacKerron’s Nearest Neighbor function.

Catchments were calculated using driving_distance() function. It returns distance to a given metro station for all network nodes (up to a maximum distance). The result can be interpolated to show e.g. which areas are at most 1 km away from any metro station.

1 km catchments around metro stations in Vienna

Close-up look at the 1 km catchment zone border

Once set up, performing this analysis is reasonably fast. Instead of metro stations, any other infrastructure coverage can be analyzed easily. I could imagine this being really useful when looking for a new flat: “Find me an area close to work, a metro station and a highschool.”

The next great thing would be to have all data for calculation of transit travel times too. Yes, I’m looking at you Wiener Linien!

In a previous post, I’ve described how to create catchment areas with pgRouting shortest_path() function. The solution described there calculates costs from the starting node (aka vertex) to all other nodes in the network. Depending on the network size, this can take a long time. Especially, if you are only interested in relatively small catchment areas (e.g. 50 km around a node in a network covering 10,000 km) there is a lot of unnecessary calculation going on. This is where you might want to use driving_distance() instead.

Driving_distance() offers a parameter for maximum distance/cost and will stop calculations when the costs exceed this limit. But let’s start at the beginning: installing the necessary functions.


If you have followed my guide to installing pgRouting, you already have some routing functions installed – but not driving_distance(). Weirdly, the necessary SQL scripts are not shipped with the .zip file available on pgRouting’s download page. You need:


Both are available through the project repository at Github. Get them and execute them in your pgRouting-enabled database. Now, you should be ready.

Calculating driving distances

To calculate driving distances, we need a query very similar to shortest_path():

sql text,
source_id integer,
distance float8,
directed boolean,
has_reverse_cost boolean)
RETURNS SETOF path_result

The only new value is “distance”. That’s the maximum distance/cost you want to be contained in the result set. “distance” has to be specified in the same units as the cost attribute (which is specified in the “sql” text parameter).

Note: In my opinion, the name “(driving) distance” is misleading. While you can use distance as a cost attribute, you’re not limited to distances. You can use any cost attribute you like, e.g. travel time, fuel consumption, co2 emissions, …

The actual query for a catchment area of 100 km around node # 2000 looks like this:

SELECT * FROM driving_distance('
      SELECT gid AS id,
          start_id::int4 AS source,
          end_id::int4 AS target,
          shape_leng::float8 AS cost
      FROM network',

Interpreting the result

These are the first lines of the result set:


The cost attribute contains the total cost of travel from the starting node to the vertex_id node.
We will only be using vertex_id and cost. The use of edge_id is a mystery to me.

Visualizing the result

The easiest way to visualize driving_distance() results is using RT Sql Layer plugin. We need to join the results of driving_distance() with the table containing node geometries:

   FROM node
   (SELECT * FROM driving_distance('
      SELECT gid AS id,
          start_id::int4 AS source,
          end_id::int4 AS target,
          shape_leng::float8 AS cost
      FROM network',
      false)) AS route
   ON = route.vertex_id

If you color the nodes based on the cost attribute, it will look something like this:

result of pgRouting driving_distance() visualized in QGIS

Please read the new instructions for pgRouting 2.0.

The aim of this post is to describe the steps necessary to calculate routes with pgRouting. In the end, we’ll visualize the results in QGIS.

This guide assumes that you have the following installed and running:

  • Postgres with PostGIS and pgAdmin
  • QGIS with PostGIS Manager and RT Sql Layer plugins

Installing pgRouting

pgRouting can be downloaded from

Building from source is covered by pgRouting documentation. If you’re using Windows, download the binaries and copy the .dlls into PostGIS’ lib folder, e.g. C:\Program Files (x86)\PostgreSQL\8.4\lib.

Start pgAdmin and create a new database based on your PostGIS template. (I called mine ‘routing_template’.) Open a Query dialog, load and execute the three .sql files located in your pgRouting download (routing_core.sql, routing_core_wrappers.sql, routing_topology.sql). Congratulations, you now have a pgRouting-enabled database.

Creating a routable road network

The following description is based on the free road network published by National Land Survey of Finland (NLS) (Update January 2013: Sorry, this dataset has been removed). All you get is one Shapefile containing line geometries, a road type attribute and further attributes unrelated to routing.

First step is to load roads.shp into PostGIS. This is easy using PostGIS Manager – Data – Load Data from Shapefile.

pgRouting requires each road entry to have a start and an end node id. If your dataset already contains this information, you can skip this step. Otherwise we will create the node ids now. (Update: pgRouting also offers a special function called assign_vertex_id that will create start and end node ids for your network table. It will not create a node table though.)

Next, we create start and end point geometries. I used a view:

   SELECT *, startpoint(the_geom), endpoint(the_geom)
   FROM road;

Now, we create a table containing all the unique network nodes (start and end points) and we’ll also give them an id:

   SELECT row_number() OVER (ORDER BY foo.p)::integer AS id, 
          foo.p AS the_geom
   FROM (         
      SELECT DISTINCT road_ext.startpoint AS p FROM road_ext
      SELECT DISTINCT road_ext.endpoint AS p FROM road_ext
   ) foo
   GROUP BY foo.p;

Finally, we can combine our road_ext view and node table to create the routable network table:

   SELECT a.*, as start_id, as end_id
   FROM road_ext AS a
      JOIN node AS b ON a.startpoint = b.the_geom
      JOIN node AS c ON a.endpoint = c.the_geom;

(This can take a while.)

I recommend adding a spatial index to the resulting table.

Calculating shortest routes

Let’s try pgRouting’s Shortest Path Dijkstra method. The following query returns the route from node #1 to node #5110:

SELECT * FROM shortest_path('
   SELECT gid AS id, 
          start_id::int4 AS source, 
          end_id::int4 AS target, 
          shape_leng::float8 AS cost
   FROM network',

Final step: Visualization

With RT Sql Layer plugin, we can visualize the results of a query. The results will be loaded as a new layer. The query has to contain both geometry and a unique id. Therefore, we’ll join the results of the previous query with the network table containing the necessary geometries.

   FROM network
   (SELECT * FROM shortest_path('
      SELECT gid AS id, 
          start_id::int4 AS source, 
          end_id::int4 AS target, 
          shape_leng::float8 AS cost
      FROM network',
      false)) AS route
   network.gid = route.edge_id;

In my case, this is how the result looks like:

Route from node #1 to node #5110

For further pgRouting-related posts check my list of pgRouting posts.

pgRouting has become even more powerful: A DARP (Dial-a-Ride-Problem) solver is now available in the “darp branch” of the pgRouting repository.

The Dial-a-Ride Problem (DARP) solver tries to minimize transportation cost while satisfying customer service level constraints (time windows violation, waiting and travelling times) and fleet constraints (number of cars and capacity, as well as depot location).

Documentation can be found at

Today, Alexander Bruy announced a new QGIS plugin called RoadGraph. It is a C++ plugin that calculates the shortest path between two points on any polyline layer (e.g. Openstreetmap shapefiles).

More information can be found at GIS-Lab.

Binary files are available for Windows and Linux:

Read on: “Travelling through Brazil with Quantum GIS”

%d bloggers like this: