Launch, grow, and unlock your career in data

November 14, 2013

VizCup Roundup–3rd place: Micah Rice, an analysis of natural disasters

No comments

This is a guest post by Micah Rice, the third place finisher at the Facebook VizCup.  In his day job, Micah is a Strategy Consultant for Wells Fargo.

I was interested in the natural disasters data set for two reasons: 1) because I work in a location analysis group and I wanted to use maps and geo-spatial analysis, and 2) because I am interested in using data to solve big problems that impact people’s lives.  The first question I wanted to answer was where in the world disasters happen: most occurrences and most people affected (Dashboard 1). A quick map revealed that China and India have the highest number of events and people impacted. The U.S. has a high number of events, but low population affected. 

The second question I had was what type of disasters cause most of the damage and how deadly are they (Dashboard 2). As it turns out, drought affects the most people by far, but extreme temperature kills a much higher proportion of those affected. I plotted this change over time and while the number of events recorded has increased tenfold, the proportion killed has actually decreased over time.  That got me thinking about what might be causing the disparities between number of events, people affected, and proportion killed.

I thought a country’s economic ability to deal with a disaster was a good place to start, so I did a quick search and found a GDP by country data set online and created a little interactive toggle to bring in Per Capita GDP cohorts into the analysis. This new data revealed that not only are poorer countries more likely to experience natural disasters, but they see much higher rates of mortality in several disaster categories.

This made me question what could be done about this from here in the Bay Area (Dashboard 3). I plotted the number of disasters and the distance from San Francisco in a few different ways and found that if an aid organization wanted to be well-located in order to respond quickly to these disasters, they would not be located here, but rather somewhere near the Middle East or South Asia.

While this analysis does not solve any real problems, I was very happy to think that good visual analytics might serve to improve the logistics around disaster response, and possibly even inform policy around what factors contribute to the unevenly-concentrated human impact we saw in the data.

No comments

Post a Comment