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Protests that Spread Spatially

Learn how protests spread spatially and discover the clusters of colour observed during experiments.
Protests That Spread Spatially

In this article, we will investigate how protests spread spatially and take a closer look at the cluster of colours that we observed during the experiments.

First, we will describe the whole process that led us from Cherryville to a NetLogo simulation. In the series of various exercises, we investigated how protests may spread spatially. We assumed that people have different attitudes towards protesting (diverse thresholds) and when deciding on whether to hang a banner in their windows they observe their surroundings and on the basis of those observations they decide whether to join a protest or not. This was the process of modeling.

Then, we investigated Cherryville and some other cities, first in a close-up (pen-and-paper model), and then we took it to a more general level using a digital tool to simulate the process. With the use of simulation we studied how the process would unfold in a number of potential cities, or, we should rather say, for a number of different combinations of initial conditions.

So what can we learn from those simulations?

In general, the higher the number of the initiators and the lower the general level of thresholds, the bigger the protest. However, just as in the case of other complex processes, it’s not so simple. So, let’s sum up the not-so-obvious insights that we receive from investigating the model.

Neighborhood Matters

Two people with the same threshold (the same attitude towards the protest) may or may not take part in the protest, depending on who they are surrounded with. It’s similar to the way friends’ behaviours influenced the final decision of potential protesters in the networked version of the process. In the same city, we may have Samantha who is very eager to protest (threshold=20%) not joining in and a very reluctant Terry (threshold=80%) hanging the banner, because their neighbourhoods are different.

  • Samantha (20%, surrounded by people who are more reluctant)
  • Terry (80%, reluctant himself, surrounded by people more prone to protest)

Initiators Play a Key Role but Their Impact Depends on Their Surroundings

Even if we have an average threshold of 20% which is low, it won’t help spread the protest if there are no initiators to “start the fire” (image). On the other hand, an initiator surrounded by very reluctant neighbours will remain to be a lonely spot within a grey area. So, in order for the protest to occur both the initiators would be there and some early-goers nearby.

  • The neighbourhood on the left has inhabitants with low thresholds, but no initiator, so the protest does not start.
  • The neighbourhood on the right does have an initiator, but the protest never spreads as his/her neighbours are reluctant to protest
  • In this neighbourhood, there is an initiator surrounded by at least some eager neighbours, so the protest will start and spread.

Typical patterns – no protest, everyone protesting OR clusters of protesters

With every different combination of parameters (number of initiators, average level of thresholds) we usually obtain one of the listed patterns, either no one is protesting, the protest spreads in the whole population, or this is the one we will focus on, we get visible clusters of colour.


The results often look almost as if they were painted. There are places in the city where the flags are all around and there are places with no flags at all (please imagine walking through the city). We could think that there are active neighbourhoods (people are so active there, very low threshold) and passive neighbourhoods (very high thresholds) but this is not true! This would be placing the blame on the people not on the process and situational factors. Let’s just look at one example.

Schematic houses

In the image, you can see that the protest has spread on the right side of the village. On the left side, there are also people who are eager to join the protest (low thresholds) but a group of quite reluctant citizens in the middle stop the spread of the protest. Six houses on the left actually have a lower average threshold than the six houses on the right – but because of the group in the middle the area on the left will stay “grey” and we won’t see any flags there.


So, a map like the one above can appear in a city where thresholds are pretty random and quite evenly distributed. It does not mean that in the middle everyone is happy with the new highway plans (or any other unpopular decision). Maybe there were no initiators there? And some more reluctant citizens on the boundaries of this area just stopped the spread that came from the “purple areas”. And the “island” of protesters in the upper part of the map does not mean that they are all very unhappy or angry – this might be just an island built around three initiators that happen to live there.

It is important to have those images in mind when we try to explain why certain processes succeed in certain areas and fail in others. Instead of blaming the people and thinking they are merely either passive or active (or good, or bad), it’s worth taking the time to dig deeper and think about the presence and the placement of initiators, about their surroundings, and investigate if there are any places where the spread is blocked (not intentionally).

One more remark to reflect upon: in all the experiments we used the example of a protest. But just as in the case of our first simple example with Appleton and Berryville, we can think about how this model of a social process is more universal. Instead of thinking about hanging the banners, we can think of a way a garden fashion spreads. Or an innovative method used in farming. Or segregating trash.

Can you think about other processes that might spread spatially? Please share your ideas with other learners.

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People, Networks and Neighbours: Understanding Social Dynamics

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