Category Archives: MDS

Second example of MDS, using travelling time between cities as distance


A while ago, I wrote a post on Multidimensional Scaling in R and Gephi, using an example based on geographical distances between some Dutch cities. As I mention in that post, the example is a bit silly, because reproducing the geographical distances between the cities in an MDS configuration has little added value. I therefore decided to write a new post, in which I make use of the same set of cities, but use a different kind of distance between them. The distance that I used is travelling time when using public transport (e.g., train, bus, tram, etc.). As in the first post on this topic, I use R and Gephi in the demonstration. For a brief introduction to MDS, please see my first post on this topic, referenced above (I also mention several academic references there).

The travelling distances

As I mentioned in the introduction, I use travelling times (using public transport) between a set of cities in the Netherlands for this example. Indeed, these distances are not absolute, and they will depend on the time of day that you are travelling, possible disturbances on the way, and so on. To determine the travelling times, I made use of the website, which is the website that you can use to plan journeys with public transport in the Netherlands. For each travel, I assumed that we would want to arrive at our destination at around 09:00 on Monday October 24th, 2016. Indeed, if I would have picked another time of day, or another day in the week, the results of this exercise could have been different.

When searching for possible journeys, the website will typically give you four options that will bring you to your destination around the requested time (see the screenshot below), and it will (among other things) indicate the time it will take to reach the destination.

Screenshot of

I always picked the option that would bring us to the destination quickest. I also assumed that travelling from city A to B would take the same amount of time to travel from city B to A. I tested this assumption for a few cities, and it does not actually hold for all cities, although the differences are small enough to be considered negligible. The picture below shows the distance matrix that I constructed this way, where the distances are travelling time in minutes when using public transport.

Travelling Distances

Running the MDS analysis in R

I performed the MDS analysis in R (see my first post for a more detailed discussion of how this works). As I mentioned in my earlier post on MDS, I embedded R in emacs, using emacs speaks statistics (just in case you are wondering). I saved the matrix shown above as a csv-file, using semicolons as the column delimiters. I then started a session of R, and loaded the vegan package, which is the package that contains the MDS algorithm that I want to use (see the screenshot below for a full log of the commands that I used). I imported the csv-file, loaded it into a data frame, and ran the metaMDS algorithm, requesting a solution with 2 dimensions. As the log below shows, I got a solution with a stress value of about 0.0917, which indicates a very good fit. I plotted the space created by the solution, which is also shown in the screenshot below.

Running the MDS in R

Visualising the result

To make a nicer visualisation, I exported the coordinates of the MDS configuration to a new csv-file. For this, I made two vectors (dim1 and dim2), and put these in a data frame as two columns (see the screenshot below for the commands I used). I then wrote the resulting data frame to the disk, with the write.table() command.

Exporting the coordinates

The file that I created can be seen in the screenshot below. Before I imported this file in Gephi, I made some small changes. The original file has three columns: 1 column with the city names, and 2 columns with coordinates on 2 dimensions. I renamed the first column to Id, then copied the column entirely to a new column that I called Label. These are columns that are typically used in a Gephi nodes list, which is exactly how I am going to import the data. I did not change anything in the other two columns.

Adjusted coordinate matrix

I started up Gephi, and I imported the data from the data laboratory (see my first post for details). In this step it is especially important to import the coordinate data as double values. To reproduce the layout in Gephi, I used the MDS Layout that I wrote for Gephi some time ago. A visual inspection of the original plot that I made with R already showed me that the MDS configuration places the cities in a similar layout as the layout that we normally use in geographical maps, with the exception that the x-axis (dimension 1) corresponds to the north-south axis on geographical maps, and the y-axis (dimension 2) corresponds to the west-east axis on geographical maps. For a more intuitive map, we want this the other way around. In the MDS Layout menu, I therefore selected dimension 2 as my x-axis, and dimension 1 as my y-axis. After running the algorithm, I get the layout as shown below (I only re-positioned the labels in Inkscape).

Gephi Layout

Interpreting the result

So what do we have here? In the 2-dimensional configuration that we have created, the distances between the cities are proportional to the time it takes to travel between these cities, using public transport. In the previous example, we used geographical distances between the cities as distances, which meant that we could plot the resulting configuration on a blank map of the Netherlands, and thereby create a quite accurate geographical map. That was possible because that configuration (with geographical distances), and the blank map we pasted it on both model ‘geographical space’. However, our new configuration, which is based on travelling times as distances, models a different kind of space. Let us call this space the ‘travelling time space’ for now.

In principle these two spaces are not really comparable. However, I thought it would still be interesting to plot both of them on a blank map of the Netherlands. Therefore, I took the configuration that I produced in my previous example (again, based on geographical distances), and copied the coordinates of that configuration in the file of the new configuration (based on travelling time). Thus, I now have a file with 4 coordinate columns, 2 of which hold the coordinates of the cities in our space of geographical distances, and 2 of which hold the coordinates of the cities in our space of travelling times (I switched some of the dimensions around to make sure that the configurations are oriented in an intuitive way). The resulting file can be seen below.

Both configurations - data file

I imported this file into Gephi, and I plotted and exported the two configurations separately. I then used Inkscape to integrate the two plots, making two different versions of the integration. In one version I used the city of Utrecht as my reference point (making sure that the position of Utrecht overlaps in both plots), because it is a central and major public transport hub in the Netherlands. In the other version I used Rotterdam as my reference point, because this lead to a result in which the cities in the two configurations lie much closer to each other (also, because I have lived in Rotterdam for quite some time, I am a bit biased, and think that Rotterdam is the best reference point that you can have in the Netherlands). I gave the cities that serve as reference points a green colour to emphasise their special role in the plots, I gave the cities in the geographical space a blue colour, and I gave the cities in the travel time space an orange colour. I then pasted the results on top of a blank map of the Netherlands, and made sure that the position of the cities in the geographical space (blue) are more or less correct. It should be noted that integrating the two spaces is, in principle, not possible, but by doing so we can make some simple comparisons of the relative distances between the cities in the two types of spaces. Using the blank map of the Netherlands mostly makes this exercise less boring, but (as we will see below) may also help us explain some observations. The resulting plots can be seen below.

Combined map A

Combined map B

What is immediately obvious is that in the space of travelling times the relative distances between the cities are different from their relative distances in the geographical space. Some cities seem to be closer to each other (e.g., Utrecht and Zwolle, Leeuwarden and Groningen, Rotterdam and Breda), while other cities are further away from each other (Utrecht and Maastricht, Utrecht and Rotterdam, Zwolle and Den Helder, Rotterdam and Middelburg). The interpretations I give below should really be taken with a grain of salt, because they are all based on intuitions, rough guesses, and pairwise comparisons of city’s positions, where it is probably much more helpful to consider the configuration as a whole (but I don’t want to break my brain; I am going to need it for a bit longer).

In both configurations, Den Helder and Middelburg are both further away from all the other cities in the travel time space than in the geographical space. Intuitively, I think this makes sense, because due to their geographical locations, these two cities seem relatively hard to reach by public transport (or even in general). For example, there is a natural barrier between Den Helder on the one hand, and Leeuwarden, Groningen and Zwolle on the other hand. It is possible to reach Den Helder from Leeuwarden more or less directly by bus (I suspect this bus crosses the “Afsluitdijk”, indicated on the map by the line between the land mass of Den Helder, and the land mass of Leeuwarden and Groningen), but it is not possible to make that journey by train (you would have to travel via Utrecht). The estuary in which Middelburg is located can also be understood to act as a kind of natural barrier. According to a map of Dutch train tracks, there is only one train track that leads to Middelburg, coming (more or less) from the direction of Breda.

Other observations are a bit harder to explain. For example, Utrecht seems to have moved quite a bit to the northeast (this can be seen most easily in the plot where Rotterdam is the anchoring point). It might be that this is because Utrecht is less affected by the natural barrier formed by the “IJsselmeer”, and is therefore relatively close to both Den Helder and Groningen and Leeuwarden, but that does not explain, for example, why the distance between Utrecht and Rotterdam has increased. Here, it may have to do something with the exact route taken by trains that run between these two cities, and the number of stops they have to make on the way. However, it may be necessary to consider the distances between a multitude of cities to fully understand these observations. For example, Rotterdam and Breda are pulled towards each other because there is a high speed train service between these cities that does not run between most other cities. At the same time, Breda and Utrecht are probably pushed away from each other because they are relatively poorly (or indirectly) connected. Thus, these can be understood as pulling (Rotterdam-Breda) and pushing (Utrecht-Breda) forces that act against each other, while also interacting with other pulling and pushing forces, finally leading to a rather complex picture. As I mentioned before, offering a good interpretation of these distances, and especially of the differences between the geographical and the travelling time distances, is a bit tricky.

Closing statements

This was another simple example of performing MDS in R and Gephi, and I hope you enjoyed it. As promised in the first post, I will try to write posts with additional examples in the future, probably focusing more on “social distances”, thereby moving further away from the more intuitive types of distances such as geographical distance and travelling time. I hope these future examples will make clear that the concepts of space and distance can be generalised in ways that we would not consider in our daily lives, and that they can show us things that interesting and relevant from a social scientific point of view (for example, some of Bourdieu’s most famous work makes heavy use of forms of ‘social distance’). For now, I say goodbye.

Simple example of Multidimensional Scaling with R and Gephi


During my PhD project I developed a fascination for geometric approaches to exploratory data analysis, such as Multidimensional Scaling (MDS) and Correspondence Analysis. These methods have some very interesting applications. For example, they can help you to strongly reduce the dimensionality of datasets, capturing the most essential patterns in your data. I especially like the ways in which these methods can help you to visually explore concepts that can be captured in terms of ‘distances,’ such as (dis)similarities between people, social groups, events, and etcetera. I thought it would be nice to write a few posts in which I offer basic examples of the methods in use (without going into the technical details), using only open source software. In this post, I focus specifically on MDS, and I offer a basic demonstration of the method, using R and Gephi, two amazing pieces of software.

A very brief introduction into MDS

Let me first say that I am not going to give an in-depth methodological introduction into MDS. I like to keep things at an intuitive level for this post (and I am likely to do so in future posts on this topic). There are plenty of books that offer a great technical introduction into MDS, including Kruskal and Wish (1978) (I love those little green books on Quantitative Applications in the Social Sciences) and Gatrell (1983) (oh, the smell of old books). I should also say that there is more than one type of MDS. For example, there is the distinction between metric and non-metric MDS. I am not going into all these nuances either, but I can at least share with you that, in the example below, I am applying non-metric MDS.

Basically, what MDS can do for you is to take a matrix in which the distances between a set of entities are recorded (i.e., a distance matrix), and try to create a low-dimensional spatial configuration in which the same entities are located in a way that the distances between them are proportional to the distances in the original distance matrix. This may sound trivial, but keep in mind that some types of distances may be very difficult to accurately reproduce in a low-dimensional space. For example, I have experimented with using MDS to visualize the (dis)similarities between social events, based on the actors and issues (e.g, problem and solution definitions) that the events have in common. I usually ended up needing at least 6 dimensions to accurately express these (dis)similarities. Unfortunately, it is impossible to visualize any space with more than three dimensions in one picture, and trying to do so is a serious threat to your mental health.

At this point, some of you might already wonder how we can decide the number of dimensions that we need to accurately represent the contents of a given distance matrix. MDS comes with a measure called ‘stress,’ which you can interpret as a measure of fit between the spatial configuration produced, and the original distance matrix. The higher the stress, the worse the fit. In other words: The higher the stress, the less confidence you should have that the spatial configuration you produced accurately represents the distances in your original distance matrix. Kruskal and Wish (1978) offer an insightful discussion on stress, and on different rules of thumb on how to interpret stress values. A very rough rule of thumb is that stress values below 0.10 usually indicate a good fit, and that values above 0.20 are unacceptable (but don’t cite me on this!).

Our example of today

The example that I will be using is a bit silly, but it serves quite well to show how powerful MDS can be at creating spaces that accurately reflect the distances that you feed into the analysis. For this example, I have taken 12 cities in the Netherlands, and I looked up the distances between all of them. To find the distances, I used this website (thank you, Google). See the distance matrix that I produced below (some of the larger pictures in this post may be easier to read if you click on them).


There is no particular reason why I picked these cities, except for the fact that they are spread out quite nicely throughout the Netherlands. I already know that it will be very easy to represent these distances in a 2-dimensional space, because that is what we do all the time with geographical maps. So the results will not be very surprising, but I still think the results are cool enough.

Let’s get to work!

So, let’s do this! The first thing I did is to save the matrix shown above as a comma-delimited file (.csv extension), because it is easy to import such files into R. If you don’t know how to do this, go to ‘save as’ in Excel, and find comma-delimited in the file options. Comma-delimited files are basically text-files that use commas, or other symbols, to separate columns. I always use semicolons ‘;’ as separators, but you can use whatever you like.

Next, we start up R. My R-session may look at bit weird to you, because I embedded R in Emacs, using Emacs Speaks Statistics, and that is just because I am a nerd. You can of course use the standard GUI for R if you like. R comes with built-in functions that can do MDS, but I like to work with a package called ‘vegan.’ I already had the package installed, but if you don’t, then you can install the package by typing install.packages(“vegan”) into the R-console. After that, you can activate the package by typing library(vegan).

After starting R, I imported the csv-file as a matrix object (see picture further below for a log of all the commands that I used). I called the matrix geoMatrix. After importing the file, I immediately turned the matrix into a so-called dist object (called geoDist), which is something that R uses to store distances. This is also the object that I fed into the MDS analysis.

The ‘vegan’ package comes with a function called metaMDS(). The function takes a lot of arguments, but most of them have default values, and for our example these default values will work just fine. There are two arguments that I would like to draw your attention to: The first argument to the function should be the distance object that you want to use as the input for the MDS analysis. There is another argument k, which indicates the number of dimensions that you want the spatial configuration to have. The default value for k is 2, which is fine for us. However, if you find that with this default value you end up with high stress values, then you might want to consider increasing the value of k. You would then, for example, use the function with the following arguments: metaMDS(geoDist, k = 3).

I use the function with the geoDist object as the only argument, and I tell R to assign the results of the function to an object called geoMDS. The function will start an iterative process in which it attempts to come up with increasingly accurate spatial configurations, and it will spit out the stress value that it ends up with at each run. By default, this function will make up to 20 attempts to come up with a decent configuration (this is also something you can change by changing the arguments given to the function), but in our case the function already reaches a very low stress value at its second run (see log below).

I immediately plotted the resulting configuration, using the standard plot() function. To assign labels to the points in the visualized configuration, I used the function text(). See the screen shot below for a log of all the commands I used (including the arguments given to the different functions) on the right, and the plot of the resulting configuration on the left (if you look below the visualization, you’ll see that I was checking out a discussion on calculating stress in R, just before doing the analysis).


After a visual inspection of the results, I decided that the distances seem pretty close the actual distances you will find on a map. However, the layout of the cities is a bit counterintuitive. Also, the orientation of the space is different from what we are used to with geographical maps, so I decided to import the data into Gephi, and change the layout there, without changing the distances.

Importing the data into Gephi

To do this, I first had to export the coordinates of all the cities in the new space that the MDS analysis created for them. These coordinates are stored somewhere in the geoMDS object. This object has a rather complicated structure that I do not want to get into, and I do not expect you to immediately understand the trick that I use to extract the coordinates from the object. However, let me tell you what I did: I created two new vectors (one for each dimension). The first vector holds all the coordinates in dimension 1, and the second vector holds all the coordinates in dimension 2. In the geoMDS-object, these coordinates are stored in what can be understood as a ‘sub-object’ of geoMDS, which is  called ‘points,’ and which is basically a two-column matrix (1 column for each dimension). All coordinates in dimension 1 are stored in the first column, and all coordinates in dimension 2 are stored in the second column. So, if you understand the basics of R, you may also understand that we can access the coordinates in each dimension with the commands geoMDS$points[,1] (for dimension 1) and geoMDS$points[,2] (for dimension 2). After putting the coordinates in two separate vectors, I assembled the vectors into a data.frame. Then I wrote the data.frame to the disk, using the write.table() function. I wrote the results to a file that I named “CoordinatesGeo.csv.” See the screen shot below to see the commands that I used.


Before we can import the resulting file in Gephi, it needs some minor changes. Without changes “CoordinatesGeo.csv” is a table with three colums: One unnamed column with the names of the cities, and two named columns with the coordinates that we created with our MDS analysis. I turned this table into a Gephi nodes list by naming the column with city names “Id”, and by copying this column to a second column that I renamed “Labels” (see screen shot below).


Now we start up Gephi. In Gephi, we start a new project, and we go to the data laboratory. There, we use the ‘import spreadsheet’ function (see below).


This will open a new dialog, where we can indicate how we want to import our data. One thing is very important here, and that is that we want to import the coordinates as numerical data. I usually import numerical data as doubles (see below).


If the settings are like shown in the screen shot above, then you should be okay. We can go back to the overview in Gephi, and the nodes will be randomly distributed in the plot screen. Gephi doesn’t come with a built-in layout algorithm for nodes that have coordinates, but you can use a plugin that I created some time ago, called MDS Layout. You should have this plugin installed before you can proceed with the next steps.

I selected the MDS Layout from the layout menu, and the plugin automatically recognizes the variables ‘dim1’ and ‘dim2’ as appropriate dimensions. The layout that is created if you run the plugin is the same as what we saw when we plotted the results in Gephi (see below).


I wanted the cities to be oriented the same way as in commonly used geographical maps, and for that I used Gephi’s built-in layout algorithm ‘Clockwise rotate’ (interestingly, this algorithm rotates the nodes in the counter-clockwise direction). I set the algorithm to rotate the layout by 90 degrees. After doing that, I immediately saw that the layout of the cities created by the MDS is like a mirror image of the layout that you’ll find on a geographical map (Maastricht is in the West, while it should be in the East, and Middelburg is in the East, while it should be in the West). This is something that we cannot fix in Gephi, but we can easily fix it in Excel.

I reopened the nodes list we created earlier, and after inspecting the coordinates I saw that the coordinates in dimension 2 are the coordinates that are mirrored. To fix this, I created a new column with coordinates for dimension 2. The values in this column are the values of the original column multiplied by -1 (see below).


I imported the adjusted nodes list into Gephi, using the same approach as before. I used the MDS Layout again to create my initial layout. In this case too, I had to use the “Clockwise rotate” layout to rotate the layout by 90 degrees. I also increased the size of the nodes a bit and gave them a color, just for aesthetic reasons. See the result below. If you know the map of the Netherlands, then you’ll see that this is already more like it!


As an ultimate test (okay, this is a bit of an exaggeration) I decided to plot the points on top of a blank map of the Netherlands. I found such a map easily enough through Google Images. I exported the visualization from Gephi as an SVG-file, such that I could adjust the positions of the labels (the city names) in Inkscape (another wonderful piece of open source software). After adjusting the labels, I exported the result as a PNG image. I then opened the blank map of the Netherlands, as well as the PNG file with the cities in of the Netherlands in Gimp (yes, yes, open source) and pasted the cities on the blank map. It took some rescaling and additional rotating to get the layout right, but ladies and gentlemen, the result looks very, very nice.


Just compare the results to an actual map of the Netherlands, and you’ll see that the MDS analysis did a very good job at creating a spatial configuration that accurately represents the distances that we fed into it. The main difference between the layout of the cities in the MDS space, and the layout of the cities in ‘actual’ geographic space is that the layout of the cities on one dimension was mirrored. It reminds me of those crappy Sci-Fi stories in which mirrors are portals to alternative dimensions, but as I mentioned before: I am a nerd.

Like I said, this is a bit of a silly example, but I plan to come up with more interesting ones in the future, where I will explore, for example, the application of MDS to ‘social stuff.’

Hope you enjoyed this!

BTW: Some time ago I created a plugin for Gephi that does MDS, using the path distances between the nodes of a graph as its input. The plugin produces coordinates that can be used to create layouts with the MDS layout algorithm. See here for more details.