Kenya recently marked one year after the 2013 polls. It is common fact that the election went on peacefully though several questions remain unanswered to date. As per the constitution,  for one to be validly elected, the winner must garner more than 50 percent plus 1 of the total votes cast.

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{Need to figure a way to embed html elements on this blog!!!}

Can be accessed here

One year down the line, I have decided to look at the events before the election. Mainly the voter registration statistics. Compare these statistics against the numbers used on the election day. The 2013 election was one of the most contested on all fronts. From the use of social media, the use of cleaver PR strategies to plain political schemes which may not be easily identifiable on the surface. Take for instance, the push by member of certain areas forcing members of their communities to register.  A simple example was to the lead up to the election, bus conductors would request to see one’s voters card to be eligible to catch the bus(matatu) to their destination. This of course pushed the registration numbers in favor of certain politicians. A counter argument could be made that,  other communities that were as not hands on around voter registration have no right to complain of such tactics. One year down the line, politicians who suffered from this maneuver are asking citizens to forgo family planning in-order to increase their political base in future.  This is indeed shallow as those most likely to be born would not be eligible to vote for the next eighteen years.  Also not forgetting the fast changing political dynamic.

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I wrote a data story on the policy ideas that made their way into four key bills related to the financial crisis.

The link to the article is below:

Tracing Policy Idea Trajectories in Congress

Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010

What did I try to do?

I chose the two news stories that I would have been covering these last two weeks if I had been in Madrid and I though of how to communicate them with data visualization. The two stories were:

– WHO’s report  about deaths atributed to air pollution.

– UN’s intergovernmental panel on climate change (IPCC) latest report warming that climate change will be an important threat to security, food and humankind.

My (many) DIFFICULTIES

My first difficulty was acces to data. I got a press release and a longer written report from WHO about its latest findings about air pollution consequences for health, but I didn’t have the data and I couldn’t find it online. I asked for it and they sent me data from 2004!

I tried to find air pollution and deaths information for Europe  and for Spain, and… it was not possible to find anything suitable to put into a spreadsheet.

After having not success on the air pollution I jumped into the climate change story. On a week where there are tons of literature in western media about the natural disasters that are yet to come because of global warming, I wanted to develop a data visualization story showing that we are already suffering an increase of extreme weather events that seems to have something to do with climate change.

Again, I couldn’t find the data for Europe or Spain, though this time I was lucky with the US.

NOAA has a compilation of the largest climate disasters occurred in the US since 1980. Data was on PDF format and although I tried different programs, I couldn’t converted it into an excel spreadsheet… So, I did a spreadsheet by myself with the data from the last 3 years, which took me about 4 hours.

Next step was mapping the data. I tried ArcGIS Online, Google Fusion Tables and Tilemill and I didn’t success with any of them because after many hours I got to the conclusion that the data collected didn’t really match with the idea of visualizacion that I had. I learned that I should have collected and organized the data in a different way to get the result I wanted; and that I should have asked for help to somebody who knows how to code, but when I figured this out it was too late.

Since I wanted to show something for the class tomorrow, I decided to use some of my data with a tool I wanted to learn about: Timeline.

The result

So, here are the main climate disasters occurred in the US in 2013, according to NOAA’s National Climatic Data Center

I was away for my business trip and started to work on this since Sunday. I am trying to tell story about Sumo wrestler and the change of Japanese society. I could not find the csv data, so I am typing the profile data of top group from ranking table, Banzukehyou, in 60’s and 90’s and the latest.I am doing the tutorial of Google Fusion Table at this very last moment.

ｗ（￣△￣；）ｗ

Would I make it in time…..?

It has been a year since Kenya went to the polls. Though the polls were peaceful, there have ben claims that the electoral body did not do its work professionally. Having had the chance to work with data pre-election, specifically electoral delimitation and voter registrations  more data has has been data provided after the election. I would like to highlight some of the discrepancies before,during and after the election. The main purpose is to have a fully based data audit of the election based on data provided before,during and after the election. Also I seek to understand if there were discrepancies, how big are the discrepancies that can we conclusively say the election was bungled. The electoral body’s chaiman did receive an award last year named the ICPS Electoral Conflict Resolution Award which also did raise a storm. The two main antagonists have very divergent views on the election, one lead by Uhuru Kenyatta believes they won the election fair and square, while the other lead by the former Prime Minister believes their victory was stolen.

For this assignment, I plan to use data to illustrate how the United States Congress works and responds to societal challenges. Specifically, I want to illuminate how Congress responded to the 2008 financial crisis through text data such as bills, reports, and hearings. What was the legislative response to the crisis? Who were the key actors and players? What was the content of successful laws?

I am working with data that is accessible through .gov websites such as the Government Printing Office. There are also some academic efforts to parse bills and laws into sections that I will use. My hope is that this approach will also be useful for current congressional activities.

More generally, and beyond the scope of this assignment, I’d be interested people’s ideas about how data-driven approaches could help us understand and analyze the processes of government and democracy.