Reading the 2019 Chilean riots

In late 2019, a price hike in public transportation fares triggered a nationwide uprising in Chile, emanating from the capital city of Santiago, where more than 1.2 million people protested social inequality. The riots continued over a six-month period, and their impacts on the country’s political and economic landscape have persisted to the present.

Carlos Cartes examined the riots to understand how socioeconomic factors and population mobility influenced their spatial distribution and temporal evolution in Santiago. Along with data on daily commuting patterns and income distribution, Cartes used a non-local epidemiological model developed in the aftermath of 2005 riots in France.

“Previous studies found the spatial distribution of the riots was well represented by the model when using data on daily travel origins and destinations,” said Cartes. “This suggested the unrest was largely driven by ‘commuter rioters’ — urban residents who took advantage of the anonymity provided by large crowds to engage in property destruction and other criminal acts.”

But Cartes added to his examination the variable of population income distribution, which improved the model’s predictive accuracy. For instance, some high-income areas registered only mild riot activity, when the original model predicted more intense unrest. Conversely, some peripheral areas of Santiago experienced severe riots, while the model forecasted less activity. The new formulation resolved many of these discrepancies.

“Studying rioting behavior through mathematical and computational tools provides a new perspective on these phenomena but also can aid in designing more effective control strategies,” said Cartes. “The model can anticipate areas likely to be affected — and in what order — in the event of a widespread uprising, which can help the development of better public security policies.”

Source: “Influence of commuter rioters and income distribution on the 2019 Chilean unrest,” by Carlos Cartes, Chaos (2025). The article can be accessed at https://doi.org/10.1063/10.0036670 .

This paper is part of the Advances in Mathematics and Physics: from Complexity to Machine Learning Collection, learn more here .