Introduction

Recently, a series of widespread power outages in Venezuela have made international headlines, as the lack of electricity has escalated the current political and economic instability. While electricity shortages have been a reoccurring issue since 2010, a major blackout occurred March 7, 2019 that was mainly the cause of years of government mismanagement. This was the beginning of three major blackouts that took place during the month of March through the vast majority of the country, including the capital city of Caracas. The power outages lead to the deaths of dozens of people as hospitals, schools and businesses were unable to function.

Thinking about Visualizing Change

How can we find quantifiable data of the electricity crisis in Venezuela, and then use it to visualize how the situation has changed over time? While NOAA has created this interactive VIIRS DRB nighttime imagery that is meant to show changes in nighttime lights on a global scale (including Venezuela), there is so much data that the map is very slow and hard to use, especially if trying to hone in on a specific region. While this visualization from ESRI draws a connection between changes in nighttime lights and population density, the data is from 2017 and does not show the 2019 blackouts.

Because this situation is ongoing and intensifying, there is a need for relevant and geographically specific geovisualizations that show the nature of the power outages. To date, I have not been able to find a geographically specific geovisualization of this nature. The most recent one I could find was this side-by-side visualization of the day of and after the March 7 blackout on Wikipedia, but I felt there was a need for an animated component.

Technical Process

To create my visualization, I used VIIRS DNB Nightly Mosaic data from the Earth Observation Group. I selected the tile that included Venezuela (Tile 1) and downloaded the corresponding .tif. I opened each frame in qGIS Desktop 3.6.0. I then downloaded the GADM shapefile of Venezuela, and used the raster extraction tool in qGIS to clip the large raster nighttime files to the shapefile. I then calculated a logarithmic scale to create a black-to-white color gradient with five classifications and a linear interpolation. I used qGIS’ print layout feature to adjust the background and exported each frame as a .tif. I exported these .tifs into Adobe Photoshop CC 2019, which I then used to create the animated .gif.

Result

Conclusion

One difficulty with working with nighttime data is the inevitable cloud coverage – this was particularly visible in the time period after the first blackout and before the second. This makes it difficult to make any sort of quantitative comparison. With access to the proper data, I also would have liked to analyze the specific regions within Venezuela and see how different populations were impacted by the blackouts. While this visualization was good at painting a general picture, it could have provided more political insights.

By Veronica Correa

This website was created as part of my final project for my “Geovisualizing Change” course at the University of North Carolina at Chapel Hill. I am an undergraduate student pursuing a degree in environmental science with a minor in journalism. My primary goal for this project was to use my multimedia and geovisualization skills to tell a unique story.