Coca and peace

Coca and peace

Organisation: El Tiempo Colombia (Colombia)

Publication Date: 04/07/2017

Size of team/newsroom:small

Description

Coca and peace are unbreakables in Colombia. On coca production regions, peasants say "without coca, there is no peace possible". Coca & Peace is an investigative report developed by El Tiempo Data Unit. This investigation was born months before the peace agreement were signed. El Tiempo Data Unit decided to inquire what was behind Government Counsellor for Peace’ affirmation: “With coca, there is no peace”. What is the reason? One of the biggest peace process’s problems was the coca production surges along the country, included the territory influenced by Farc guerrilla. People in Colombia believes that those territories that Farc guerrilla leaves will be occupied by others armed actors whose will take advantage of the illegal business, and coca production. This investigative report is divided into five chapters series in order to explore the complex coca’s supply chain in Colombia. We extracted, compiled, analyzed and visualized data from SIMCI report (United Nations Office on Drugs and Crime Crop monitoring System). We gathered and standardized data since 1999 to 2015 and explained them in three main categories: Coca cultivation, coca eradication efforts, and crop substitutions. Data were visualized by coca crop density indicator (ha/km2), control grow's actions, coca crops increase versus control actions, and families benefited by Government coca crops' substitution programs versus coca crops' increase. Additionally, a component was introduced to explain why Government plans to decrease coca crops were not effective and what would be the possibles scenarios to solve the problem through a predictive analysis with experts. During the reporting, we traveled to deep Colombia hit by war. After data analysis, we selected four different regions to visit and we spoke to peasants who grow coca there. We made four videos in: Nariño: The region with the highest coca crop density in the country. Only this region has more coca crops cultivated than whole Bolivia country. Cordoba: The region with the highest coca leaf's price in the country. Briceño: The region where Government has the pilot coca crop substitution program with the guerrilla. Catatumbo: The region when coca is the queen and all armed actors live without problems because of it. Finally, this report introduced an exercise that we denominated “Disruptive Journalism” showed at the five chapter, there we explain the phenomenon consequences and impact on the kids in the region, the most vulnerable population.

What makes this project innovative? What was its impact?

Taking into account the complexity of the data-driven reportage, it was editorially decided to present it as a multimedia series of five chapters that came out day after day during a week. This kept the expectation of the audience and it had on average 40.000 daily unique users. One of the chapters addressed the problem of coca cultivation from a predictive scenario, with help from experts, and asking what would happen in a territory if the Government does not take the right measures to work against the phenomenon. In the data process, we extracted, collected, and standardized data from SIMCI reports, calculating the coca crop density indicator (ha/km2) and explained the real impact of control grow's actions on coca crops increase.

Technologies used for this project:

Fundamentally, data visualization in this project was built on d3.js library. We also used other JavaScript libraries such as mapbox-gl.js and jQuery. As an example, interactive map template was designed with a mapbox style created in Mapbox Studio app. HTML5 and CSS3 using Bootstrap were indispensable for data visualization. In order to create and design each graphic, data cleaning, processing, and analyzing were also fundamental. For this purpose we use Python programming language, and some of its data analysis libraries, including pandas, scipy, and numpy. Exploratory data analysis was possible using other python dependencies, such as itertools, json, csv, and codecs. Considering technologies and softwares, we used Jupyter Notebook made by continuum analytics anaconda. This environment enabled us explore our data from beginning to end. Its facility to import Python libraries was fundamental to data structure and visualization using matplotlib module. In terms of design we used Adobe Illustrator.

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