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Today’s post is a follow-up of Movement data in GIS #3: visualizing massive trajectory datasets. In that post, I summarized a concept for trajectory generalization. Now, I have published the scripts and sample data in my QGIS-Processing-tools repository on Github.

To add the trajectory generalization scripts to your Processing toolbox, you can use the Add scripts from files tool:

It is worth noting, that Add scripts from files fails to correctly import potential help files for the scripts but that’s not an issue this time around, since I haven’t gotten around to actually write help files yet.

The scripts are used in the following order:

  1. Extract characteristic trajectory points
  2. Group points in space
  3. Compute flows between cells from trajectories

The sample project contains input data, as well as output layers of the individual tools. The only required input is a layer of trajectories, where trajectories have to be LINESTRINGM (note the M!) features:

Trajectory sample based on data provided by the GeoLife project

In Extract characteristic trajectory points, distance parameters are specified in meters, stop duration in seconds, and angles in degrees. The characteristic points contain start and end locations, as well as turns and stop locations:

The characteristic points are then clustered. In this tool, the distance has to be specified in layer units, which are degrees in case of the sample data.

Finally, we can compute flows between cells defined by these clusters:

Flow lines scaled by flow strength and cell centers scaled by counts

If you use these tools on your own data, I’d be happy so see what you come up with!


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If you follow this blog, you’ll probably remember that I published a QGIS style for flow maps a while ago. The example showed domestic migration between the nine Austrian states, a rather small dataset. Even so, it required some manual tweaking to make the flow map readable. Even with only 72 edges, the map quickly gets messy:

Raw migration flows between Austrian states, line width scaled by flow strength

One popular approach in the data viz community to deal with this problem is edge bundling. The idea is to reduce visual clutter by generate bundles of similar edges. 

Surprisingly, edge bundling is not available in desktop GIS. Existing implementations in the visual analytics field often run on GPUs because edge bundling is computationally expensive. Nonetheless, we have set out to implement force-directed edge bundling for the QGIS Processing toolbox [0]. The resulting scripts are available at https://github.com/dts-ait/qgis-edge-bundling.

The main procedure consists of two tools: bundle edges and summarize. Bundle edges takes the raw straight lines, and incrementally adds intermediate nodes (called control points) and shifts them according to computed spring and electrostatic forces. If the input are 72 lines, the output again are 72 lines but each line geometry has been bent so that similar lines overlap and form a bundle.

After this edge bundling step, most common implementations compute a line heatmap, that is, for each map pixel, determine the number of lines passing through the pixel. But QGIS does not support line heatmaps and this approach also has issues distinguishing lines that run in opposite directions. We have therefore implemented a summarize tool that computes the local strength of the generated bundles.

Continuing our previous example, if the input are 72 lines, summarize breaks each line into its individual segments and determines the number of segments from other lines that are part of the same bundle. If a weight field is specified, each line is not just counted once but according to its weight value. The resulting bundle strength can be used to create a line layer style with data-defined line width:

Bundled migration flows

To avoid overlaps of flows in opposing directions, we define a line offset. Finally, summarize also adds a sequence number to the line segments. This sequence number is used to assign a line color on the gradient that indicates flow direction.

I already mentioned that edge bundling is computationally expensive. One reason is that we need to perform pairwise comparison of edges to determine if they are similar and should be bundled. This comparison results in a compatibility matrix and depending on the defined compatibility threshold, different bundles can be generated.

The following U.S. dataset contains around 4000 lines and bundling it takes a considerable amount of time.

One approach to speed up computations is to first use a quick clustering algorithm and then perform edge bundling on each cluster individually. If done correctly, clustering significantly reduces the size of each compatibility matrix.

In this example, we divided the edges into six clusters before bundling them. If you compare this result to the visualization at the top of this post (which did not use clustering), you’ll see some differences here and there but, overall, the results are quite similar:

Looking at these examples, you’ll probably spot a couple of issues. There are many additional ideas for potential improvements from existing literature which we have not implemented yet. If you are interested in improving these tools, please go ahead! The code and more examples are available on Github.

For more details, leave your email in a comment below and I’ll gladly send you the pre-print of our paper.

[0] Graser, A., Schmidt, J., Roth, F., & Brändle, N. (accepted) Untangling Origin-Destination Flows in Geographic Information Systems. Information Visualization – Special Issue on Visual Movement Analytics.


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This is a guest post by Chris Kohler .

Introduction:

This guide provides step-by-step instructions to produce drive-time isochrones using a single vector shapefile. The method described here involves building a routing network using a single vector shapefile of your roads data within a Virtual Box. Furthermore, the network is built by creating start and end nodes (source and target nodes) on each road segment. We will use Postgresql, with PostGIS and Pgrouting extensions, as our database. Please consider this type of routing to be fair, regarding accuracy, as the routing algorithms are based off the nodes locations and not specific addresses. I am currently working on an improved workflow to have site address points serve as nodes to optimize results. One of the many benefits of this workflow is no financial cost to produce (outside collecting your roads data). I will provide instructions for creating, and using your virtual machine within this guide.

Steps:–Getting Virtual Box(begin)–

Intro 1. Download/Install Oracle VM(https://www.virtualbox.org/wiki/Downloads)

Intro 2. Start the download/install OSGeo-Live 11(https://live.osgeo.org/en/overview/overview.html).

Pictures used in this workflow will show 10.5, though version 11 can be applied similarly. Make sure you download the version: osgeo-live-11-amd64.iso. If you have trouble finding it, here is the direct link to the download (https://sourceforge.net/projects/osgeo-live/files/10.5/osgeo-live-10.5-amd64.iso/download)
Intro 3. Ready for virtual machine creation: We will utilize the downloaded OSGeo-Live 11 suite with a virtual machine we create to begin our workflow. The steps to create your virtual machine are listed below. Also, here are steps from an earlier workshop with additional details with setting up your virtual machine with osgeo live(http://workshop.pgrouting.org/2.2.10/en/chapters/installation.html).

1.  Create Virutal Machine: In this step we begin creating the virtual machine housing our database.

Open Oracle VM VirtualBox Manager and select “New” located at the top left of the window.

VBstep1

Then fill out name, operating system, memory, etc. to create your first VM.

vbstep1.2

2. Add IDE Controller:  The purpose of this step is to create a placeholder for the osgeo 11 suite to be implemented. In the virtual box main window, right-click your newly-created vm and open the settings.

vbstep2

In the settings window, on the left side select the storage tab.

Find “adds new storage controller button located at the bottom of the tab. Be careful of other buttons labeled “adds new storage attachment”! Select “adds new storage controller button and a drop-down menu will appear. From the top of the drop-down select “Add IDE Controller”.

vbstep2.2

vbstep2.3

You will see a new item appear in the center of the window under the “Storage Tree”.

3.  Add Optical Drive: The osgeo 11 suite will be implemented into the virtual machine via an optical drive. Highlight the new controller IDE you created and select “add optical drive”.

vbstep3

A new window will pop-up and select “Choose Disk”.

vbstep3.2

Locate your downloaded file “osgeo-live 11 amd64.iso” and click open. A new object should appear in the middle window under your new controller displaying “osgeo-live-11.0-amd64.iso”.

vbstep3.3

Finally your virtual machine is ready for use.
Start your new Virtual Box, then wait and follow the onscreen prompts to begin using your virtual machine.

vbstep3.4

–Getting Virtual Box(end)—

4. Creating the routing database, and both extensions (postgis, pgrouting): The database we create and both extensions we add will provide the functions capable of producing isochrones.

To begin, start by opening the command line tool (hold control+left-alt+T) then log in to postgresql by typing “psql -U user;” into the command line and then press Enter. For the purpose of clear instruction I will refer to database name in this guide as “routing”, feel free to choose your own database name. Please input the command, seen in the figure below, to create the database:

CREATE DATABASE routing;

You can use “\c routing” to connect to the database after creation.

step4

The next step after creating and connecting to your new database is to create both extensions. I find it easier to take two-birds-with-one-stone typing “psql -U user routing;” this will simultaneously log you into postgresql and your routing database.

When your logged into your database, apply the commands below to add both extensions

CREATE EXTENSION postgis;
CREATE EXTENSION pgrouting;

step4.2

step4.3

5. Load shapefile to database: In this next step, the shapefile of your roads data must be placed into your virtual machine and further into your database.

My method is using email to send myself the roads shapefile then download and copy it from within my virtual machines web browser. From the desktop of your Virtual Machine, open the folder named “Databases” and select the application “shape2pgsql”.

step5

Follow the UI of shp2pgsql to connect to your routing database you created in Step 4.

step5.2

Next, select “Add File” and find your roads shapefile (in this guide we will call our shapefile “roads_table”) you want to use for your isochrones and click Open.

step5.3

Finally, click “Import” to place your shapefile into your routing database.

6. Add source & target columns: The purpose of this step is to create columns which will serve as placeholders for our nodes data we create later.

There are multiple ways to add these columns into the roads_table. The most important part of this step is which table you choose to edit, the names of the columns you create, and the format of the columns. Take time to ensure the source & target columns are integer format. Below are the commands used in your command line for these functions.

ALTER TABLE roads_table ADD COLUMN "source" integer;
ALTER TABLE roads_table ADD COLUMN "target" integer;

step6

step6.2

7. Create topology: Next, we will use a function to attach a node to each end of every road segment in the roads_table. The function in this step will create these nodes. These newly-created nodes will be stored in the source and target columns we created earlier in step 6.

As well as creating nodes, this function will also create a new table which will contain all these nodes. The suffix “_vertices_pgr” is added to the name of your shapefile to create this new table. For example, using our guide’s shapefile name , “roads_table”, the nodes table will be named accordingly: roads_table_vertices_pgr. However, we will not use the new table created from this function (roads_table_vertices_pgr). Below is the function, and a second simplified version, to be used in the command line for populating our source and target columns, in other words creating our network topology. Note the input format, the “geom” column in my case was called “the_geom” within my shapefile:

pgr_createTopology('roads_table', 0.001, 'geom', 'id',
 'source', 'target', rows_where := 'true', clean := f)

step7

Here is a direct link for more information on this function: http://docs.pgrouting.org/2.3/en/src/topology/doc/pgr_createTopology.html#pgr-create-topology

Below is an example(simplified) function for my roads shapefile:

SELECT pgr_createTopology('roads_table', 0.001, 'the_geom', 'id')

8. Create a second nodes table: A second nodes table will be created for later use. This second node table will contain the node data generated from pgr_createtopology function and be named “node”. Below is the command function for this process. Fill in your appropriate source and target fields following the manner seen in the command below, as well as your shapefile name.

To begin, find the folder on the Virtual Machines desktop named “Databases” and open the program “pgAdmin lll” located within.

step8

Connect to your routing database in pgAdmin window. Then highlight your routing database, and find “SQL” tool at the top of the pgAdmin window. The tool resembles a small magnifying glass.

step8.2

We input the below function into the SQL window of pgAdmin. Feel free to refer to this link for further information: (https://anitagraser.com/2011/02/07/a-beginners-guide-to-pgrouting/)

CREATE TABLE node AS
   SELECT row_number() OVER (ORDER BY foo.p)::integer AS id,
          foo.p AS the_geom
   FROM (     
      SELECT DISTINCT roads_table.source AS p FROM roads_table
      UNION
      SELECT DISTINCT roads_table.target AS p FROM roads_table
   ) foo
   GROUP BY foo.p;

step8.3

  1.  Create a routable network: After creating the second node table from step 8,  we will combine this node table(node) with our shapefile(roads_table) into one, new, table(network) that will be used as the routing network. This table will be called “network” and will be capable of processing routing queries.  Please input this command and execute in SQL pgAdmin tool as we did in step 8. Here is a reference for more information:(https://anitagraser.com/2011/02/07/a-beginners-guide-to-pgrouting/)   

step8.2

 

CREATE TABLE network AS
   SELECT a.*, b.id as start_id, c.id as end_id
   FROM roads_table AS a
      JOIN node AS b ON a.source = b.the_geom
      JOIN node AS c ON a.target = c.the_geom;

step9.2

10. Create a “noded” view of the network:  This new view will later be used to calculate the visual isochrones in later steps. Input this command and execute in SQL pgAdmin tool.

CREATE OR REPLACE VIEW network_nodes AS 
SELECT foo.id,
 st_centroid(st_collect(foo.pt)) AS geom 
FROM ( 
  SELECT network.source AS id,
         st_geometryn (st_multi(network.geom),1) AS pt 
  FROM network
  UNION 
  SELECT network.target AS id, 
         st_boundary(st_multi(network.geom)) AS pt 
  FROM network) foo 
GROUP BY foo.id;

step10

11.​ Add column for speed:​ This step may, or may not, apply if your original shapefile contained a field of values for road speeds.

In reality a network of roads will typically contain multiple speed limits. The shapefile you choose may have a speed field, otherwise the discrimination for the following steps will not allow varying speeds to be applied to your routing network respectfully.

If values of speed exists in your shapefile we will implement these values into a new field, “traveltime“, that will show rate of travel for every road segment in our network based off their geometry. Firstly, we will need to create a column to store individual traveling speeds. The name of our column will be “traveltime” using the format: ​double precision.​ Input this command and execute in the command line tool as seen below.

ALTER TABLE network ADD COLUMN traveltime double precision;

step11

Next, we will populate the new column “traveltime” by calculating traveling speeds using an equation. This equation will take each road segments geometry(shape_leng) and divide by the rate of travel(either mph or kph). The sample command I’m using below utilizes mph as the rate while our geometry(shape_leng) units for my roads_table is in feet​. If you are using either mph or kph, input this command and execute in SQL pgAdmin tool. Below further details explain the variable “X”.

UPDATE network SET traveltime = shape_leng / X*60

step11.2

How to find X​, ​here is an example​: Using example 30 mph as rate. To find X, we convert 30 miles to feet, we know 5280 ft = 1 mile, so we multiply 30 by 5280 and this gives us 158400 ft. Our rate has been converted from 30 miles per hour to 158400 feet per hour. For a rate of 30 mph, our equation for the field “traveltime”  equates to “shape_leng / 158400*60″. To discriminate this calculations output, we will insert additional details such as “where speed = 30;”. What this additional detail does is apply our calculated output to features with a “30” value in our “speed” field. Note: your “speed” field may be named differently.

UPDATE network SET traveltime = shape_leng / 158400*60 where speed = 30;

Repeat this step for each speed value in your shapefile examples:

UPDATE network SET traveltime = shape_leng / X*60 where speed = 45;
UPDATE network SET traveltime = shape_leng / X*60 where speed = 55;

The back end is done. Great Job!

Our next step will be visualizing our data in QGIS. Open and connect QGIS to your routing database by right-clicking “PostGIS” in the Browser Panel within QGIS main window. Confirm the checkbox “Also list tables with no geometry” is checked to allow you to see the interior of your database more clearly. Fill out the name or your routing database and click “OK”.

If done correctly, from QGIS you will have access to tables and views created in your routing database. Feel free to visualize your network by drag-and-drop the network table into your QGIS Layers Panel. From here you can use the identify tool to select each road segment, and see the source and target nodes contained within that road segment. The node you choose will be used in the next step to create the views of drive-time.

12.Create views​: In this step, we create views from a function designed to determine the travel time cost. Transforming these views with tools will visualize the travel time costs as isochrones.

The command below will be how you start querying your database to create drive-time isochrones. Begin in QGIS by draging your network table into the contents. The visual will show your network as vector(lines). Simply select the road segment closest to your point of interest you would like to build your isochrone around. Then identify the road segment using the identify tool and locate the source and target fields.

step12

step12.2

Place the source or target field value in the below command where you see ​VALUE​, in all caps​.

This will serve you now as an isochrone catchment function for this workflow. Please feel free to use this command repeatedly for creating new isochrones by substituting the source value. Please input this command and execute in SQL pgAdmin tool.

*AT THE BOTTOM OF THIS WORKFLOW I PROVIDED AN EXAMPLE USING SOURCE VALUE “2022”

CREATE OR REPLACE VIEW "​view_name" AS 
SELECT di.seq, 
       di.id1, 
       di.id2, 
       di.cost, 
       pt.id, 
       pt.geom 
FROM pgr_drivingdistance('SELECT
     gid::integer AS id, 
     Source::integer AS source, 
     Target::integer AS target,                                    
     Traveltime::double precision AS cost 
       FROM network'::text, ​VALUE::bigint, 
    100000::double precision, false, false)
    di(seq, id1, id2, cost)
JOIN network_nodes pt ON di.id1 = pt.id;

step12.3

13.Visualize Isochrone: Applying tools to the view will allow us to adjust the visual aspect to a more suitable isochrone overlay.

​After creating your view, a new item in your routing database is created, using the “view_name” you chose. Drag-and-drop this item into your QGIS LayersPanel. You will see lots of small dots which represent the nodes.

In the figure below, I named my view “take1“.

step13

Each node you see contains a drive-time value, “cost”, which represents the time used to travel from the node you input in step 12’s function.

step13.2

Start by installing the QGIS plug-in Interpolation” by opening the Plugin Manager in QGIS interface.

step13.3

Next, at the top of QGIS window select “Raster” and a drop-down will appear, select “Interpolation”.

step13.4

 

A new window pops up and asks you for input.

step13.5

Select your “​view”​ as the​ vector layer​, select ​”cost​” as your ​interpolation attribute​, and then click “Add”.

step13.6

A new vector layer will show up in the bottom of the window, take care the type is Points. For output, on the other half of the window, keep the interpolation method as “TIN”, edit the ​output file​ location and name. Check the box “​Add result to project​”.

Note: decreasing the cellsize of X and Y will increase the resolution but at the cost of performance.

Click “OK” on the bottom right of the window.

step13.7

A black and white raster will appear in QGIS, also in the Layers Panel a new item was created.

step13.8

Take some time to visualize the raster by coloring and adjusting values in symbology until you are comfortable with the look.

step13.9

step13.10

14. ​Create contours of our isochrone:​ Contours can be calculated from the isochrone as well.

Find near the top of QGIS window, open the “Raster” menu drop-down and select Extraction → Contour.

step14

Fill out the appropriate interval between contour lines but leave the check box “Attribute name” unchecked. Click “OK”.

step14.2

step14.3

15.​ Zip and Share:​ Find where you saved your TIN and contours, compress them in a zip folder by highlighting them both and right-click to select “compress”. Email the compressed folder to yourself to export out of your virtual machine.

Example Isochrone catchment for this workflow:

CREATE OR REPLACE VIEW "2022" AS 
SELECT di.seq, Di.id1, Di.id2, Di.cost,                           
       Pt.id, Pt.geom 
FROM pgr_drivingdistance('SELECT gid::integer AS id,                                       
     Source::integer AS source, Target::integer AS target, 
     Traveltime::double precision AS cost FROM network'::text, 
     2022::bigint, 100000::double precision, false, false) 
   di(seq, id1, id2, cost) 
JOIN netowrk_nodes pt 
ON di.id1 = pt.id;

References: Virtual Box ORACLE VM, OSGeo-Live 11  amd64 iso, Workshop FOSS4G Bonn(​http://workshop.pgrouting.org/2.2.10/en/index.html​),

In this post, we use TimeManager to visualize the position of a moving object over time along a trajectory. This is another example of what is possible thanks to QGIS’ geometry generator feature. The result can look like this:

What makes this approach interesting is that the trajectory is stored in PostGIS as a LinestringM instead of storing individual trajectory points. So there is only one line feature loaded in QGIS:

(In part 2 of this series, we already saw how a geometry generator can be used to visualize speed along a trajectory.)

The layer is added to TimeManager using t_start and t_end attributes to define the trajectory’s temporal extent.

TimeManager exposes an animation_datetime() function which returns the current animation timestamp, that is, the timestamp that is also displayed in the TimeManager dock, as well as on the map (if we don’t explicitly disable this option).

Once TimeManager is set up, we can edit the line style to add a point marker to visualize the position of the moving object at the current animation timestamp. To do that, we interpolate the position along the trajectory segments. The first geometry generator expression splits the trajectory in its segments:

The second geometry generator expression interpolates the position on the segment that contains the current TimeManager animation time:

The WHEN statement compares the trajectory segment’s start and end times to the current TimeManager animation time. Afterwards, the line_interpolate_point function is used to draw the point marker at the correct position along the segment:

CASE 
WHEN (
m(end_point(geometry_n($geometry,@geometry_part_num)))
> second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
AND
m(start_point(geometry_n($geometry,@geometry_part_num)))
<= second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
)
THEN
line_interpolate_point( 
  geometry_n($geometry,@geometry_part_num),
  1.0 * (
    second(age(animation_datetime(),to_datetime('1970-01-01 00:00')))
	- m(start_point(geometry_n($geometry,@geometry_part_num)))
  ) / (
    m(end_point(geometry_n($geometry,@geometry_part_num)))
	- m(start_point(geometry_n($geometry,@geometry_part_num)))
  ) 
  * length(geometry_n($geometry,@geometry_part_num))
)
END

Here is the animation result for a part of the trajectory between 08:00 and 09:00:


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In the previous post, I demonstrated the aggregation support in QGIS expressions. Another popular request is to aggregate or cluster point features that are close to each other. If you have been following the QGIS project on mailing list or social media, you probably remember the successful cluster renderer crowd-funding campaign by North Road.

The point cluster renderer is implemented and can be tested in the current developer version. The renderer is highly customizable, for example, by styling the cluster symbol and adjusting the distance between points that should be in the same cluster:

Beyond this basic use case, the point cluster renderer can also be combined with categorized visualizations and clusters symbols can be colored in the corresponding category color and scaled by cluster size, as demoed in this video by the developer Nyall Dawson:

In 2012 I published a post on mapping the then newly released Tirol river dataset.

In the comments, reader Michal Zimmermann asked:

Do you think it would be possible to create a river stream which gains width along its way? I mean rivers are usually much narrower on their beginnings, then their width increases and the estuary should be the widest part, right?

For a long time, this kind of river style, also known as “tapered lines” could only be created in vector graphics software, such as Inkscape and Illustrator.

With the help of geometry generators, we can now achieve this look directly in QGIS:

Data cc-by Land Tirol

In the river dataset published by the state of Tirol, all rivers are digitized in upstream direction. For this styling to work, it is necessary that the line direction is consistent throughout the whole dataset.

We use a geometry generator symbol layer to split the river geometry into its individual segments:

 

Then we can use the information about the total number of segments (accessible via the expression variable @geometry_part_count) and the individual segment’s number (@geometry_part_num) to calculate the segment’s line width.

The stroke width expression furthermore uses the river category (GEW_GRKL) to vary the line width depending on the category:

CASE 
WHEN "GEW_GRKL" = '< 10 km2 Fluss' THEN 0.2
WHEN "GEW_GRKL" = '10 km2 Fluss' THEN 0.4
WHEN "GEW_GRKL" = '100 km2 Fluss' THEN 0.6
WHEN "GEW_GRKL" = '1.000 km2 Fluss' THEN 0.8
ELSE 1.0
END 
* ( 1- ( @geometry_part_num /  @geometry_part_count ))

If the rivers are digitized in downstream direction, you can simply remove the 1- term.

Happy mapping!

In my previous posts, I discussed classic flow maps that use arrows of different width to encode flows between regions. This post presents an alternative take on visualizing flows, without any arrows. This style is inspired by Go with the Flow by Robert Radburn and Visualisation of origins, destinations and flows with OD maps by J. Wood et al.

The starting point of this visualization is a classic OD matrix.

migration_raw_data

For my previous flow maps, I already converted this data into a more GIS-friendly format: a Geopackage with lines and information about the origin, destination and strength of the flow:

migration_attribute_table

In addition, I grabbed state polygons from Natural Earth Data.

At this point, we have 72 flow features and 9 state polygon features. An ordinary join in the layer properties won’t do the trick. We’d still be stuck with only 9 polygons.

Virtual layers to the rescue!

The QGIS virtual layers feature (Layer menu | Add Layer | Add/Edit Virtual Layer) provides database capabilities without us having to actually set up a database … *win!*

Using a classic SQL query, we can join state polygons and migration flows into a new virtual layer:

virtual_layer

The resulting virtual layer contains 72 polygon features. There are 8 copies of each state.

Now that the data is ready, we can start designing the visualization in the Print Composer.

This is probably the most manual step in this whole process: We need 9 map items, one for each mini map in the small multiples visualization. Create one and configure it to your liking, then copy and paste to create 8 more copies.

I’ve decided to arrange the map items in a way that resembles the actual geographic location of the state that is represented by the respective map, from the state of Vorarlberg (a proud QGIS sponsor by the way) in the south-west to Lower Austria in the north-east.

To configure which map item will represent the flows from which origin state, we set the map item ID to the corresponding state ID. As you can see, the map items are numbered from 1 to 9:

small_multiples_print_composer_init

Once all map items are set up, we can use the map item IDs to filter the features in each map. This can be implemented using a rule based renderer:

small_multiples_style_rules

The first rule will ensure that the each map only shows flows originating from a specific state and the second rule will select the state itself.

We configure the symbol of the first rule to visualize the flow strength. The color represents the number number of people moving to the respective district. I’ve decided to use a smooth gradient instead of predefined classes for the polygon fill colors. The following expression maps the feature’s weight value to a shade on the Viridis color ramp:

ramp_color( 'Viridis',
  scale_linear("weight",0,2000,0,1)
)

You can use any color ramp you like. If you want to use the Viridis color ramp, save the following code into an .xml file and import it using the Style Manager. (This color ramp has been provided by Richard Styron on rocksandwater.net.)

<!DOCTYPE qgis_style>
<qgis_style version="0">
  <symbols/>
    <colorramp type="gradient" name="Viridis">
      <prop k="color1" v="68,1,84,255"/>
      <prop k="color2" v="253,231,36,255"/>
      <prop k="stops" v="0.04;71,15,98,255:0.08;72,29,111,255:0.12;71,42,121,255:0.16;69,54,129,255:0.20;65,66,134,255:0.23;60,77,138,255:0.27;55,88,140,255:0.31;50,98,141,255:0.35;46,108,142,255:0.39;42,118,142,255:0.43;38,127,142,255:0.47;35,137,141,255:0.51;31,146,140,255:0.55;30,155,137,255:0.59;32,165,133,255:0.62;40,174,127,255:0.66;53,183,120,255:0.70;69,191,111,255:0.74;89,199,100,255:0.78;112,206,86,255:0.82;136,213,71,255:0.86;162,218,55,255:0.90;189,222,38,255:0.94;215,226,25,255:0.98;241,229,28,255"/>
    </colorramp>
  </colorramps>
</qgis_style>

If we go back to the Print Composer and update the map item previews, we see it all come together:

small_multiples_print_composer

Finally, we set title, legend, explanatory texts, and background color:

migration

I think it is amazing that we are able to design a visualization like this without having to create any intermediate files or having to write custom code. Whenever a value is edited in the original migration dataset, the change is immediately reflected in the small multiples.

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