This project is a collaboration between David Jimenez, Charles Kaioun, Celeste LeCompte, and Léa Steinacker.
In San Francisco, there is a growing concern about residential fires, which have displaced more than 100 residents from their homes since the beginning of the year. Have there been more big fires? If so, why? We turned to the data to answer the question.
Read on for more background on our analysis.
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I started this data visualization set at 4.30 pm today and finish it almost four hours later. This is the first time I try to visualize several data sets using CartoDB, after participating in a workshop on using this tool last January.
The idea is combining three different data sets about natural disasters in Indonesia (floods, landslides and forest fire) to see the places where it happened most of the time in 2014, and then layered it with a map of places where non oil and gas mining operates in the country.
I suspect that most of these disasters happened in places where nature has lost its ability to sustain the balance, due to over exploitation of the resources. Obviously, although the trigger is natural cause, disasters such as flood, landslides and forest fire are basically human made.
All of the data sets used here are taken from government database, available at http://data.go.id.
I try to find other relevant datasets to combine the disasters map, such as: industrial zone maps, deforestation map, oil and gas mining zone, but unfortunately, those map don’t have similar georeference codes that can be read in CartoDB. So I finally settled with only a distribution map of the non-oil and gas mining industry.
Initially I wasn’t sure how to make the connection because Indonesia has more that 1.000 mining locations spread all over the archipelago, but then I found the torque heat animation which I think can represent the different concentration level of the mining industry in different places. The heat animation can highlight and contrast the disasters map which are only represented by different colors of simple circles.
From doing this exercise, I realize the complexity of data visualization, the importance of having a clean data set and the powerful image it can give to the audience. I hope when people look at this map, they can really make the connection between these horrible disasters and the mining industry that for years have been operating without a clear environmental regulation and oversights. (*)
Click here to see the map: Indonesian Natural Disasters Vs Mining Operation Distribution Map
Background: Kevin Hu & Travis Rich built a site called GIFGIF, which aims to crowd tag animated gifs with various emotions. From GIFGIF’s website: “An animated gif is a magical thing. It contains the power to convey emotion, empathy, and context in a subtle way that text or emoticons simply can’t. GIFGIF is a project to capture that magic with quantitative methods. Our goal is to create a tool that lets people explore the world of gifs by the emotions they evoke, rather than by manually entered tags.” As we know, animated gifs are also a popular storytelling mechanism for social news and entertainment websites.
The cultural phenomenon of using animated gifs to express emotions has been the subject of numerous journalistic inquiries:
GIF hearts Tumblr: a fairytale for the Internet age – Wired.co.uk
Visualization project for this week: Kevin, Travis, and I built a map tool so people can explore GIFGIF’s current dataset to see which gifs are most representative of certain emotions across different countries. Out of 1.8 million votes, 1.4 million votes had IP data which links the votes to the location of the voter. GIFGIFmap can be found here.
In a future version, we would like to show the top gifs per emotion that countries have in common with each other, and what are unique top gifs for each country (along the lines of What We Watch). However, there are limitations to the GIFGIF data set in terms of global coverage. For example, the top 21 countries account for 92% of the votes. Additionally, we excluded countries that had less than 10,000 total votes across all categories, so as to avoid making generalizations based on limited data. We chose to include the number of votes per country (per emotion) to make the data set more transparent in terms of representation.
We think the tool we are building could complement existing stories about the phenomenon of using animated gifs to communicate (stories like the ones we linked to above).
These are some potential questions that we hope journalists could explore using a map interface to the GIFGIF dataset:
1) Do people from different countries interpret the emotional content of gifs differently?
2) If there are variances in interpretation, are there clusters of countries that have more similar interpretations? Do these match up with proximity, or immigration patterns?
3) What top gifs per emotion are unique to a given country?
Note: GIFGIF’s data will soon be made publicly available through an API.