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Since I’ve started working, transport and movement data have been at the core of many of my projects. The spatial nature of movement data makes it interesting for GIScience but typical GIS tools are not a particularly good match.

Dealing with the temporal dynamics of geographic processes is one of the grand challenges for Geographic Information Science. Geographic Information Systems (GIS) and related spatial analysis methods are quite adept at handling spatial dimensions of patterns and processes, but the temporal and coupled space-time attributes of phenomena are difficult to represent and examine with contemporary GIS. (Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University)

It’s still a hot topic right now, as the variety of related publications and events illustrates. For example, just this month, there is an Animove two-week professional training course (18–30 September 2016, Max-Planck Institute for Ornithology, Lake Konstanz) as well as the GIScience 2016 Workshop on Analysis of Movement Data (27 September 2016, Montreal, Canada).

Space-time cubes and animations are classics when it comes to visualizing movement data in GIS. They can be used for some visual analysis but have their limitations, particularly when it comes to working with and trying to understand lots of data. Visualization and analysis of spatio-temporal data in GIS is further complicated by the fact that the temporal information is not standardized in most GIS data formats. (Some notable exceptions of formats that do support time by design are GPX and NetCDF but those aren’t really first-class citizens in current desktop GIS.)

Most commonly, movement data is modeled as points (x,y, and optionally z) with a timestamp, object or tracker id, and potential additional info, such as speed, status, heading, and so on. With this data model, even simple questions like “Find all tracks that start in area A and end in area B” can become a real pain in “vanilla” desktop GIS. Even if the points come with a sequence number, which makes it easy to identify the start point, getting the end point is tricky without some custom code or queries. That’s why I have been storing the points in databases in order to at least have the powers of SQL to deal with the data. Even so, most queries were still painfully complex and performance unsatisfactory.

So I reached out to the Twitterverse asking for pointers towards moving objects database extensions for PostGIS and @bitnerd, @pwramsey, @hruske, and others replied. Amongst other useful tips, they pointed me towards the new temporal support, which ships with PostGIS 2.2. It includes the following neat functions:

  • ST_IsValidTrajectory — Returns true if the geometry is a valid trajectory.
  • ST_ClosestPointOfApproach — Returns the measure at which points interpolated along two lines are closest.
  • ST_DistanceCPA — Returns the distance between closest points of approach in two trajectories.
  • ST_CPAWithin — Returns true if the trajectories’ closest points of approach are within the specified distance.

Instead of  points, these functions expect trajectories that are stored as LinestringM (or LinestringZM) where M is the time dimension. This approach makes many analyses considerably easier to handle. For example, clustering trajectory start and end locations and identifying the most common connections:

animation_clusters

(data credits: GeoLife project)

Overall, it’s an interesting and promising approach but there are still some open questions I’ll have to look into, such as: Is there an efficient way to store additional info for each location along the trajectory (e.g. instantaneous speed or other status)? How well do desktop GIS play with LinestringM data and what’s the overhead of dealing with it?

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This post is a quick instruction for installing Postgres 9.2, PostGIS 2.0 and pgRouting 2.0.

  1. For Postgres, download the installer from enterprisedb.com.
  2. Run the installer. You’ll have to pick a superuser password – remember it, you’ll need it again soon.
  3. At the end of the installation process, allow the installer to start Stack Builder.
  4. In Stack Builder, select the Postgres 9.2 installation and install PostGIS from the list of available extensions.
  5. The PostGIS installation procedure will prompt you for the superuser password you picked before.
  6. I suggest letting the installer create a sample database We’ll need it later anyway.

Now for the pgRouting part:

  1. Download the pgRouting zip file for your system (32 or 64 bit) from Winnie.
  2. Unzip the file. It contains bin, lib and share folders as well as two text files.
  3. Copy these folders and files over to your Postgres installation. In my case C:\Program Files\PostgreSQL\9.2

Installation – done.

Next, fire up pgAdmin. If you allowed the PostGIS installer to create a sample database, you will find it named postgis20 or similar. Otherwise just create a new database using the PostGIS template database. You can enable pgRouting in a database using the following steps:

  1. In postgis20, execute the following query to create the pgrouting extension. This will add the pgRouting-specific functions:
    CREATE EXTENSION pgrouting;
  2. Test if everything worked fine:
    SELECT pgr_version();

    It should return "(2.0.0-dev,v2.0.0-beta,18,a3be38b,develop,1.46.1)" or similar – depending on the version you downloaded.

pgadmin_pgrouting

How about some test data? I’ll be using the public transport network of the city of Vienna provided in GeoJSON format from http://data.wien.gv.at/daten/geo?service=WFS&request=GetFeature&version=1.1.0&typeName=ogdwien:OEFFLINIENOGD&srsName=EPSG:4326&outputFormat=json:

    1. Just copy paste the url in Add Vector Layer | Protocol to load the dataset.
    2. Use DB Manager to load the layer into your database. (As you can see in the screenshot, I created a schema called network but that’s optional.)

import_publictransport

  1. To make the line vector table routable, we use pgr_createTopology. This function assumes the columsn called “source” and “target” exist so we have to create those as well:
    alter table network.publictransport add column source integer;
    alter table network.publictransport add column target integer;
    select pgr_createTopology('network.publictransport', 0.0001, 'geom', 'id');
    

    I’m quite generously using a tolerance of 0.0001 degrees to build the topology. Depending on your dataset, you might want to be more strict here.

  2. Let’s test it! Route from source #1 to target #3000 using pgr_dijkstra:
    SELECT seq, id1 AS node, id2 AS edge, cost, geom
      FROM pgr_dijkstra(
        'SELECT id, source, target, st_length(geom) as cost FROM network.publictransport',
        1, 3000, false, false
      ) as di
      JOIN network.publictransport pt
      ON di.id2 = pt.id ;

    Note how the query joins the routing results and the network table together. (I’m aware that using the link length as a cost attribute will not lead to realistic results in a public transport network but bear with me for this example.)

  3. We can immediately see the routing results using the Load as new layer option:

select_dijkstra
route

Nice work! pgRouting 2.0 has come a long way. In a post from April this year, Boston GIS even announced to add pgRouting into the Stack Builder. That’s going to make the installation even more smooth.

This is an update to my previous post “WFS to PostGIS in 3 Steps”. Thanks to Even Rouault’s comments and improvements to GDAL, it is now possible to load Latin1-encoded WFS (like the one by data.wien.gv.at) into PostGIS in just one simple step.

To use the following instructions, you’ll have to get the latest GDAL (release-1600-gdal-mapserver.zip)

You only need to run SDKShell.bat to set up the environment and ogr2ogr is ready for action:

C:\Users\Anita>cd C:\release-1600-gdal-mapserver
C:\release-1600-gdal-mapserver>SDKShell.bat
C:\release-1600-gdal-mapserver>ogr2ogr -overwrite -f PostgreSQL PG:"user=myuser password=mypassword dbname=wien_ogd" "WFS:http://data.wien.gv.at/daten/geoserver/ows?service=WFS&request=GetFeature&version=1.1.0&typeName=ogdwien:BEZIRKSGRENZEOGD&srsName=EPSG:4326"

Thanks everyone for your comments and help!

This is a quick note on how to download features from a WFS and import them into a PostGIS database. The first line downloads a zipped Shapefile from the WFS. The second one unzips it and the last one loads the data into my existing “gis_experimental” database:

wget "http://data.wien.gv.at/daten/geoserver/ows?service=WFS&request=GetFeature&version=1.1.0&typeName=ogdwien:BEZIRKSGRENZEOGD&srsName=EPSG:4326&outputFormat=shape-zip" -O BEZIRKSGRENZEOGD.zip
unzip -d /tmp BEZIRKSGRENZEOGD.zip
shp2pgsql -s 4326 -I -S -c -W Latin1 "/tmp/BEZIRKSGRENZEOGD.shp" | psql gis_experimental

Now, I’d just need a loop through the WFS Capabilities to automatically fetch all offered layers … Ideas anyone?

Thanks to Tim for his post “Batch importing shapefiles into PostGIS” which was very useful here.

Update: Many readers have pointed out that ogr2ogr is a great tool for this kind of use cases and can do the above in one line. That’s true – if it works. Unfortunately, it is picky about the supported encodings, e.g. doesn’t want to parse ISO-8859-15. In such cases, the three code lines above can be a good alternative.

This is the follow up post to “An osm2po Quickstart” which covers loading the OSM network into PostGIS and using the result with pgRouting. After parsing the OSM file, e.g.

C:\Users\Anita\temp\osm2po-4.2.30>java -jar osm2po-core-4.2.30-signed.jar prefix=at "C:\Users\Anita\Geodaten\OpenStreetMap Data\austria.osm.pbf"

you should find a folder with the name of the prefix you chose inside the osm2po folder. It contains a log file which in turn provides a command line template for importing the OSM network into PostGIS, e.g.

INFO commandline template: psql -U [username] -d [dbname] -q -f "C:\Users\Anita\temp\osm2po-4.2.30\at\at_2po_4pgr.sql"

Using this template, we can easily import the .sql file into an exiting database. My pgRouting-enabled database is called wien_ogd.

C:\Users\Anita\temp\osm2po-4.2.30\at>psql -U [username] -d wien_ogd -q -f C:\Users\Anita\temp\osm2po-4.2.30\at\at_2po_4pgr.sql

Now, the data is ready for usage in QGIS:

The osm2po table in QGIS

Using “pgRouting Layer” plugin, it’s now straightforward to calculate shortest paths. I had to apply some changes to the plugin code, so please get the latest version from Github.

A shortest path in osm2po network

Using osm2po turned out to be far less painful than I expected and I hope you’ll find this post useful too.

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
SELECT *
   FROM osm_nodes
   JOIN
   (SELECT * FROM driving_distance('
      SELECT gid AS id,
          source,
          target,
          meters AS cost
      FROM osm_roads',
      2699,
      10000,
      false,
      false)) AS route
   ON
   osm_nodes.id = 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)
FROM 
  points_as_polygon(
    '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.

[1] http://biogeometry.duke.edu/software/alphashapes/

Today, I’ve been experimenting with data from OpenFlights.org. They offer airport, airline and route data for download. The first idea that came to mind was to connect airports on a shared route by lines. This kind of visualization just looks much nicer if the connections are curved instead of simple straight lines.

Luckily, that’s pretty easy to do using PostGIS. After loading airport positions and route data, we can create the connection lines like this (based on [postgis-users] Great circle as a linestring):

UPDATE experimental.airroutes
SET the_geom = 
(SELECT ST_Transform(ST_Segmentize(ST_MakeLine(
       ST_Transform(a.the_geom, 953027),
       ST_Transform(b.the_geom, 953027)
     ), 100000 ), 4326 ) 
 FROM experimental.airports a, experimental.airports b
 WHERE a.id = airroutes.source_id  
   AND b.id = airroutes.dest_id
);

The CRS used in the query is not available in PostGIS by default. You can add it like this (source: spatialreference.org):

INSERT into spatial_ref_sys (srid, auth_name, auth_srid, proj4text, srtext) values ( 953027, 'esri', 53027, '+proj=eqdc +lat_0=0 +lon_0=0 +lat_1=60 +lat_2=60 +x_0=0 +y_0=0 +a=6371000 +b=6371000 +units=m +no_defs ', 'PROJCS["Sphere_Equidistant_Conic",GEOGCS["GCS_Sphere",DATUM["Not_specified_based_on_Authalic_Sphere",SPHEROID["Sphere",6371000,0]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Equidistant_Conic"],PARAMETER["False_Easting",0],PARAMETER["False_Northing",0],PARAMETER["Central_Meridian",0],PARAMETER["Standard_Parallel_1",60],PARAMETER["Standard_Parallel_2",60],PARAMETER["Latitude_Of_Origin",0],UNIT["Meter",1],AUTHORITY["EPSG","53027"]]');

This is an example visualization (done in QGIS) showing only flight routes starting from Vienna International Airport:

Flight routes from Vienna International

Connections crossing the date line are currently more problematic. Lines would have to be split, otherwise this is what you’ll get:

Date line trouble

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