A long time correspondent of NPR in Beijing, China, Louisa Lim, describes her as the one “who kept of my secrets and offered unstinting moral support” in her book, “The People’s Republic of Amnesia: Tiananmen Revisited” which was published early last year.
The Chinese immigration official sees her as a fashion reporter, working for a publication with a very industrial title, “Women’s Wear Daily” which based in New York City, covering non-political events such as the Miss World beauty pageant at the city of Ordos, near Mongolia.
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