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Drawing boundaries of social interaction

Beyond physical borders, “invisible borders” limit interaction between specific groups of people. These “social borders” are historically and socially constructed and result from a wide range of factors such as cultural differences, absence of connecting infrastructure, or even rivalry and prejudice. Insights into such borders can help us understand the social, civic, and commercial interactions that do or do not exist, in contemporary nations. As studying the phenomena that lead to these borders is highly complex, simply defining and mapping these borders is a first step towards better governance, planning and other policy. Newly available digital records of human interaction coupled with geographic information have great potential to uncover ‘social borders’ and are therefore providing a whole new array of empirical observations for the study of this topic.


Many existing ‘social borders’ are well known as they are anchored in the history of nation-building or in particular regional developments. Take for instance Belgium, where a north-south divide between the Dutch-speaking northern part, Flanders, and the French-speaking southern part, Wallonia, has been a long-standing division line marking a barrier between different regional groups and cultural identities. Interestingly, this linguistic separation can be clearly observed from communication patterns between mobile phone customers in the country [1].

In this paper we investigate whether similar borders can be detected within other European countries like the Great Britain, France and Italy.

COMMUNITY DETECTION BASED ON COMMUNICATION PATTERNS

In this study, we use data from country-wide phone communication networks to detect and understand the outlines of communities within European nations, namely: the Great Britain, France, Italy, and Belgium [2]. This process is best understood in two steps. Firstly, one needs to know how the communication patterns are constructed based on telephone data, and second, a better insight into the way the community detection algorithm works is key to understanding how ‘communities’ are defined.

Communication patterns are constructed between different locations within a country based on the aggregated duration of all calls having their origin in the first and their destination in the second location. This process generates a weighted (by aggregated duration) and directional (from A to B, from B to A) network. Of course, this methodology cannot be applied to all locations in a country. Rather, the network is based on a number of fixed locations or zones that are used for geolocating the captured communications, such as cell phone towers or the zip code of the customer’s home.

An easy way to investigate constructed communication patterns is to have a look at those originating from a single municipality. Typically, these patterns will show a lot of interactions with neighbouring municipalities and increasingly fewer interactions with municipalities that are further away [3]. While it is possible to calculate the probability that one municipality will be contacted by another, it remains difficult to detect ‘social borders’ on a larger scale than that of the individual municipality.

Recently, community detection algorithms have overcome this issue as they are now able to detect and define user groups, locations, or regions that have a higher degree of commonality compared to other groupings. Based on the total network of all communication patterns, we used an algorithm for community detection that looks for groups of locations (and thus communities) that have dense connections between them but sparse connections with other groups (for more information see [2]).

STUDYING SPATIAL COMMUNITIES

We can observe that all calculated communities (Fig.1, coloured) are spatially homogenous and coincide quite well with existing administrative boundaries (in black). This is a surprising finding given that one cannot foresee which locations will belong to which communities, nor can one know how many different locations will form a community. The algorithmic treatment of the topology and information of a nationwide interaction network yields opportunities to delineate regions based on daily communication patterns that are strikingly similar to existing regions.

Figure1

The results for France (1A) show that the created community map follows administrative borders with striking accuracy. There are two major exceptions: the regions of Limousin and Auvergne are to most parts joined together and Rhône-Alpes is split into three. This indicates that some parts of the communication network in the south of Rhône-Alpes are better connected to the neighbouring regions to the south and south-west than to their home region.

In Great Britain (1C), the most obvious difference between the detected groupings and the administrative partitions is that Wales and, to a lesser extent, Yorkshire, seem to have been incorporated into regions dominated by the major cities of the West and East Midlands regions, respectively. We have also “found” a new region extending to the west of London.

The partitioning of Italy (1E) also generally corresponds to the official division of land, but here a number of border shifts between neighbouring regions are observed. Some notable deviations from political borders include the city of Verona being part of the Trentino-Alto Adige region, and the most eastern part of Liguria, La Spezia, merging with Tuscany. Additional regions are scattered throughout the country, with small settlements in the western part of Emilia-Romagna, and Sicily being split into three.

So, the country-wide networks of human telephone interactions create spatially cohesive regions generally consistent with the geo-partitioning of greater administrative regions. It is possible to go one step further and apply the community detection method to a second level. Applying the algorithm to the sub-network inside each of the detected regions uncovers a second level of partitions. The panels on the right in figure 1 (B, D, F) show that second-level communities possess the same general properties as the first-level partitions – all the communities are geographically cohesive.

Despite various technological advances in communications, social networks and daily interactions continue to revolve primarily around local concerns. Social interaction patterns follow a local context far from the global village prophecy. Thus, several other studies have shown that in general about 80% of all calls cover distances of no more than 50 km, or that the probability that two individuals are connected by a telephone link is inversely proportional to the square of the distance between them [3].

Furthermore, it did not come as a surprise that social networks are framed by geographical distance; the ‘natural geography’ emerges from our daily communications reinforces the preexisting political, cultural or administrative lines at every scale.

Figure2

FINDINGMAJOR DIVIDING LINES

Considering the interesting case of Belgium’s bipartition, we could ask ourselves what a bipartition in France, Italy or the Great Britain would look like. What if we asked the community detection algorithm to render only two communities? Would the algorithm be able to create a meaningful bipartition of a country based solely on our communication patterns? Might it even reflect a well-known cultural, political or administrative border? Running the bipartition algorithm, we can see an almost perfect separation of two communities according to the two well-known linguistic territories: Flanders and Wallonia. Some interesting findings can be explored, such as the inclusion of Brussels in the Flanders territory and the emergence of a small corridor south of Brussels as part of the Flanders community (see above). Only 3.5% of all communications cross the ‘linguistic border’. Clearly, the Belgian bipartition is a strongly institutionalized one and so it is interesting to investigate the application of the bipartition algorithm to other countries, as shown in figure below.

Figure3

The next strongest split is France, with just 5.7% of links straddling the West-East divide. Surprisingly enough, the interaction split is no longer linked to the old linguistic barrier of the Occitan and Oïl languages (South/North) but runs along the western borders of Normandy, Paris-Region, Centre, Limousin, and Midi-Pyrénées.

In Italy only 7.8 % of communications cross a line running roughly along the northern border of the Emilio-Romagna region and separating the industrial and commercial metropolises of Milan, Turin and the Po valley from the southern part of the country. And telecommunications data in Britain show that only about 9.5 per cent of communications cross a line about 150 km north of London.

CONCLUDING REMARKS

 Applying a community detection algorithm to extensive communication datasets of 4 countries, we showed that it has now become possible to derive spatially coherent communities that align remarkably well with existing administrative borders even though the methodology is not predetermined by geography. The technique employed here proves to be robust over different datasets from different countries and allows for multi-level investigations as well as calibration for more generalized patterns, like bi-partitioning.

The results of our investigations are compelling. They provide a rather objective, albeit generalized view of how communications create a landscape that often does and at times does not reflect existing political, economic or administrative borders. The fact that these regions are derived from digital records of individual actions offers opportunities to address long-standing questions related to identity formation or border policies in a more objective and quantitative way.

The objectivity that stems from the passive tracking of large numbers of people and activities also has its downside. It is very difficult to attribute direct interpretations of why these communities and division lines exist. Uncovering the invisible social communities and borders on a large scale and in a more objective fashion does not mean one can infer the reasons for, or the implications of, these borders.

It is one thing to acknowledge borders; it is another thing to comprehend, appreciate and integrate them in order to take more effective action and obtain more valuable insights. What can be extracted from this study, however, is a renewed appreciation for the way daily behaviour creates general structures that can be revealed, analysed and used to achieve greater insight into the human condition and contemporary social patterns.

your comments

    • 2/02/2016 – 04h54 | Arthur Perini

      Hello,
      Thanks for communicating over your studies, the results you’re presenting are quite surprising and the methodology you’ve used is really interesting. I would like to adress you some further questions over it:
      1) What are exactly the kind of datas you’ve used to anchor your algorythmic and statistical manipulations ? the expression “phone use” is quite evasive. Are the different types of contacting someone or adressing one a message (or, let say, information from us) as chating, phone calls, communication patterns using digital social medias, and automated self inforrmation delivery part of the communication networks you’re describing ?
      2) How does the statistical intergration of communities’ communication networks vaires according to the “geometrical” projection of the administrative partition of territories ? In other words, is the statistical integration of the comminities’ networks and the administrative partition equally distributed when the empirical reference changes ? (How long can the empirical partition be held on)
      3) If the protools you’re using aren’t edited as open source programs, which kind of tools would you recomend a student to use for his research ? same kind of data visualisation needed, but with cross-media (which can be condensed in one’s phone btw) analyses.
      Thanks a lot, we’re expecting further more from your researches in this area.
      Best regards,
      AP

    • 4/02/2016 – 02h41 | Maarten Vanhoof

      Hi Arthur,

      Thanks for your message and interest. Let me try and answer some of your questions:

      1) You’re correct that it is not really clear from this (popular) article as to what kind of data where exactly used. Generally, the kind of data differ from country to country and are well described in reference 2. (Just click on it in the article and you’ll get acces to the full paper, or navigate towards: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081707). Mostly, though, the connections in the graph are made up of calls and calls only. It is true that shares of communication media change over time but, as you can imagine, it is quite impossible to have access to all this kind of data.

      2) I have to admit that your question here is not entirely clear to me. I’ll try to answer it anyway, but if you feel like this is not addressing your question, please don’t hesitate to rephrase your question. To evaluate the ‘overlap’ between our partitioning and the administrative partitioning, we can use several statistical measures like Rand’s criterion and Fowlkes and Mallow’s index. There exists other but these ones were used in reference 2, so you can have a look at their meaning and interpretation there. It is a good question, however, to ask how well our methodology would perform or thus, how these indicators would behave when we have less, or different data. To this idea i can help you out with some findings that are also described in reference 2: a) adding small amounts of noise to our datasets, so adding random calls between people did not influence our partitioning; meaning that the data is rather robust on this one. However, adding mediate or a lot of noise (say something like 40 to 100 % of calls are randomly inserted), does cause the community detection algorithm to create a partitioing that is not uniform in space. b) the indicators of overlap do not at all behave similar when using different datasets. For instance in Saudi Arabia, or Ivory Coast, the overlap between boundaries and our partitioing was not at all that good compared to the West-European countries. c) to my knowledge, nobody yet tested what exactly ist the lower boundary of data you need to detect our findings. In my opinion, the problem here is twofold. First it is rather time-consuming to run the methodology over and over on the same dataset but with less information. The moment you start thinking about it, it is rather difficult to come up with an objective way of shrinking your dataset. Which calls do you delete? And how neutral is this choice?

      3) It is, indeed, a pity that neither this kind of data, nor the full protocols are open sourced. The tools we use for this kind of analysis however, are mostly open source. For the big data treatmen we use the hadoop platform (hive, pig, spark), for the implementation of the communitiy detection algorithms, you can use c++, matlab (not really open-source) or python. One of the most well known community detection algorithms (Louvain-la-Neuve) has recently been made available by Vincent Traag: http://www.traag.net/code/ as a python library, so it might be interesting to look around there for awhile. If your data is not that big of ours, i believe you can also explore community detection algorithms in open-source software of social network analysis like Gephi, but probably you’ll be limited to the visual aspect of the case there. To map similar findings as ours, we used matlab, but really any GIS software will do for that. One of the most used open-source options here would be Q-GIS.

      Hope this answers some of your questions if you have more, don’t hesitate to fire them away.
      As mentioned before, thanks for your interest!

      Bests,
      Maarten

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