Some Context

Colombia has a long history of violence. Most recently, however, it has been on the world’s radar given the signing of a peace deal with the FARC, the rejection of the agreement by the citizens, and the new a definitive signature this past November.

The conflict is incredibly complicated, involving several different actors, ideologies, motives and types of violence. The groups that have been involved have committed different kinds of crimes including war crimes, and crimes against humanity. The following timeline seeks to put the conflict in context by pointing out important historical periods (yellow) and several (although not all) the parts involved in the conflict. Guerrillas are represented by red. These usually hold left-wing ideologies and arose, in part, as a response to the abandonment that rural areas had found themselves in. Drug cartels, which have played a complicated role throughout the years, are depicted in green. Finally, paramilitary groups are represented in blue. These arose as a response to the violence that was generated by guerrillas and drug cartels.

It is important to note that data for victimizing acts only exists form 1985 onwards. This data is collected and managed by the RNI, the national network of information.


Because of the prolonged violence and Colombia’s ability to progress despite the situation, it has become hard to understand the particular characteristics of the different actors and the specific situations of the varied victims. However, now that we find ourselves at a turning point, understanding the context is vital for the successful repairing of the civilians that have suffered the effects of the war. Moreover, understanding the nature of the victimizing acts is fundamental to ensure stability in the future.

Perception Survey

To begin, I wanted to find out how informed people from around the world are about the victims of the conflict. To address this question, I conducted a survey and distributed through two mediums: among my friends using Facebook, and among strangers using Amazon Mechanical Turk. Below is a map showing the IP locations of all the 149 respondents.

More analysis has to be conducted on these responses. However, from the preliminary exploratory analysis I did, some interesting facts arose. On one hand, on average, Colombians accurately predicted that displacement is the type of victimizing act that has left the greatest impact (in number of victims). On the other hand, people who are from other parts of the world, predicted that violence threats would be the type of victimizing act with the greatest amount of victims. In fact, there was a significant difference (p=0.0368) in ranking of displacement (how high they ranked it according to the number of victims) between Colombians and others.

Data Reported on Victimizing Acts

Moving away from the perception survey, to start understanding the problematics, I looked at the records of victims accross time and according to the victimizing act reported. This data is openly available at the RUV website, however, I had to clean the data, remove all UT8 characters and translate it from Spanish to English. I used Tableau to perform exploratory data analysis and to create a series of visualizations that demonstrate some of the trends I found.

Below you can find an interactive visualization that makes evident the prevalence and gravity of displacement throughout the years. It is important to note that one person could have reported multiple assaults and thus the total does not account for the total number of victims, but the total number of victimizing incidents.

Data is available for both, the years the victimizing acts occurred, and the years they were reported. I visualized these two and found some interesting trends about the conflict. First of all, many years passed before the government started collecting data about the victims. A significant increase after 2011 can be traced back to the creation of RNI the national network of information that has facilitated keeping track of these records.

I found this trend tells an interesting story about statistics and information systems in today's political climate. Foucault talks about biopower and statistics as tools of governmentality. In light of today's pervasive sharing of information between government, private corporations and academia (Van Dijck 2014), these data gathering practices about citizens has been looked at with a critical eye. This type of data, however, speaks to a more positive side of the story, and one in which lack of tracking and lack of statistical accounting of citizens can actually be harmful to the civil population.

We may not want to be tracked by our governments when this leads to the violation of our rights, but we encourage it when it facilitates the recognition of our rights. The question is then, how do we know when the government is doing one or the other?


From the 1980s up to 2014, around 7 million Colombians were victims of forced displacement. This means that approximately, 13% of the population has suffered from this type of violence. Unfortunately, this has placed Colombia in second place globally for highest number of internally displaced people.

The problematic dates to the middle of the XX century when Colombia went through an extremely violent period due to conflicts between liberals and conservatives. In 1964, the FARC and the ELN, followed by several other paramilitary and rebel groups, arose. This would aggravate the problematic in the years to come. In fact, 1980 has been marked as the starting point for the current dynamics of displacement. Among the illegal interests that are tied to displacement in this current form are narcotrafficking and illegal mining (Centro Nacional de Memoria Histórica, 2015).

Geographic Trends

To explore this phenomenon in more detail, I first looked at the general geographic trends. Interestingly, Antioquia is, by far, the department with most displaced events reported. I looked back at the survey I conducted and realized this fact is not widely known. Among Colombians, Antioquia was, on average, ranked 4th after Caqueta, Choco and Putumayo. Only 30% of Colombians ranked it as the department with most amount of victims. Media and, I think, the romanticization of the periphery by people in metropolitan areas, are probably key player in this disparity between perception and reality.

Massive Evictions

Such a prolonged history of violence has made attacks against the civil population become naturalized to Colombians. Moreover, the breach between the metropolitan areas and the rural parts of the country has produced a certain degree of oblivion towards the tragic events that have happened throughout this historical period.

I got interested in looking at the trends of displacement: were specific municipalities targets of mass displacement, or were people moving in small groups across the country? To answer these questions, I looked at the trends according to the percentage of the municipality's population that was being displaced and the percentage of the department's population that has been displaced through time. I obtained the data for municipality populations from the DANE (National Administrative Department of Statistics) and calculated the percentage of people that were displaced every year for every municipality.

The visualization above was one of the most meaningful ones for me. To my surprise there have been years where up to 12.8% of a department has been displaced (see Choco 1997). These particular moments that stood out for me in the graph led me to search for news that correspond to those years where massive evictions have happened. I scraped data from Colombia's newspaper El Tiempo using Web Scraper and cleaned it using Excel. In the future, I want to visualize that data, possibly conducting a network analysis of the text contained in them to have a deeper look into the different facets of the conflict.

Moreover, this visualization made me think about the role that data plays in the study of history. I certainly believe that using tools like Tableau and data like these in the classroom would be very useful and would lead students to see history from a different angle. These exploratory visualizations are also great sources of questions and new lines of research.


The last part of this project involved looking at the relationship between drugs, specifically cocaine, and displacement. Although it is nationally recognized, the role of drug cartels and of narcotrafficking practices by rebel groups is largely overlooked in international media. Given that the war against drugs has been strongly promoted by the United States, and that this same country has the highest prevalence of cocaine use, I found it relevant to investigate this relationship.

I used data obtained from the ODC (National Drug Observatory) and the UN Office of Drug and Crime to build a dataset with the numbers of hectares of coca harvested each year in each department in Colombia.

I looked at the number of hectares of coca harvested over time and compared it to the number of displacement cases by year. The trend is apparent, however, the timeline shown above makes it obvious that the correlation should exist. I wanted, then, to dig deeper into these dynamics. For that, I looked at the relationship between crops and displacement at the municipal level: were people being displaced in the same municipalities where coca was being cultivated?

In the maps presented below, color maps the number of people expelled from that municipality, while size tracks the number of hectares cultivated. To put it simply, big green circles would prove that people were being displaced by groups that wanted to harvest coca. Although I have not performed statistical analysis on these data, this trend does not seem to emerge. My theory for this apparent trend is that the different cartels and rebel groups depend on field workers to for coca harvesting. Increased coca production is tied to a civilian population being there to harvest it.

Final Remarks

This project was conducted as part of the Information, Society and Culture course at Duke University on December 2016. Plots are exploratory and seek to find trends in the data that allow me to formulate interesting questions for future research. If you have any questions, please contact me.