Adam Green - August 29, 2019 in advertisements, advertising, america, americana, John Margolies, On the Road, roads
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Codrina Ilie - August 28, 2014 in Data for CSOs, Election data, On the Road
After the crash course of only 3 hours for grasping how the Bosnian election system actually works and after a powerful and general brainstorming of what we have and what we want to get, the happy hackers divided into 3 teams: the data team, the development team and the storytellers each with clear tasks to achieve.
Michael Bauer - August 22, 2014 in Election data, On the Road
Elections are one of the most data-driven events in contemporary democracies around the world. While no two states have the same system rarely can one encounter an election system as complex as in Bosnia and Herzegovina. It is of little surprise that even people living in the country and eligible to vote often don’t have a clear concept of what they can vote for and what it means. To solve this Zasto Ne invited a group of civic hackers and other clever people to work on ways to show election results and make the system more tangible.
Through our experience wrangling data we spent the first days getting the data from previous elections (which we received from the electoral commission) into a usable shape. The data levels were very dis-aggregated and we managed to create good overviews over the different municipalities, election units and entities for the 4 different things citizens vote on in general elections. All the four entities generally have different systems, competencies and rules they are voted for. To make things even more complicated ethnicities play a large role and voters need to choose between ethnic lists to vote on (does this confuse you yet?). To top this different regions have very different governance structures – and of course there is the Brcko district – where everything is just different.
To be able to show election results on a map – we needed to get a complete set of municipal boundaries in Bosnia and Herzegovina. The government does not provide data like this: OpenStreetMap to the rescue! Codrina spent some time on importing what she could find on OSM and join it to a single shapefile. Then she worked some real GIS magic in QGis to fit in the missing municipal boundaries and make sure the geometries are correct.
In the meanwhile Michael created a list of municipalities, their electoral codes and the election units they are part of (and because this is Bosnia, each municipality is part of 3-4 distinct electoral units for the different elections except of course Brcko where everything is different). Having this list and a list of municipalities in the shapefile we had to work some clever magic to get the election id’s into there. The names (of course) did not fully match between the different data sets. Luckily Michael had encountered this issue previously and written a small tool to solve this issue: reconcile-csv. Using OpenRefine in combination with reconcile-csv made the daunting task of matching names that are not fully the same less scary and something we could quickly accomplish. We discovered an interesting inaccuracy in the OpenStreetMap data we used and thanks to local knowledge Codrina could fix it quite fast.
What we learned:
Zara Rahman - March 10, 2014 in Events, On the Road
Image credit: the engine room
Image credit: Aspiration Tech
Tarek Amr - March 4, 2014 in On the Road
There were nine attendees in the training that was run by two School of Data mentors, Ali Rebaie from Lebanon and yours truly from Egypt. The training lasted for four days, and the following are some of the topics tackled there:
After the training, the attendees were asked to rank the sessions according to their preferences. It was clearly shown that many of them were interested in plotting data on maps after scraping, cleaning, and analysis to report different topics and news incidents. Design principles and charting where are also a common need among the attendees.
Different tools were used during the training for data scraping and cleaning as well as mapping and charting. Examples of the tools covered are TileMill, QGIS, Google Spreadsheets, Data Wrapper, Raw, InkScape, and Tabula.
In the end of the training, we also discussed how the attendees could arrange to give the same training to other members of their organization, how to plan it, and how to tailor the sessions based on their backgrounds and needs. Another discussion we had during the training was how to start a data journalism team in a newspaper, especially the required set of skills and the size of the team.
Finally, from our discussions with the Welad El-Balad team as well as with other journalists in the region, there seems to be a great interest in data-driven journalism. Last year, Al Jazeera and the European Journalism Centre started to translate The Data Journalism Handbook into Arabic. Online and offline Data Expeditions are also helping to introduce journalists and bloggers in the region to the importance of data journalism. Thus, we are looking forward to seeing more newspapers in the region taking serious steps towards establishing their own data teams.
Michael Bauer - December 6, 2013 in On the Road
Map of school enrolment
Milena Marin - November 28, 2013 in On the Road
Photo credit: UNDP
Michael Bauer - August 14, 2013 in On the Road
The reason for my visit to Kenya was a workshop with Development Initiatives – who trained a group of 30 activists from CSOs on Aid and Budget Transparency. The workshop had a strong focus on campaign and advocacy strategies but also involved some data work. I was there to guide the participants through a data scouting excercise (imagine the first bits of a data expedition: brainstorming ideas and working out what data are needed to answer them). Since the workshop had a budget focus, the participants were further introduced to visualizing data with OpenSpending – feeding back into the documentation around the project. In the scouting excercise participants were free to brainstorm questions around the topic or even leave the topic completely: one of the groups decided to focus on diamond mining in Sierra Leone. Participants brought up crucial questions around where money is spent and how – diving into corruption, need and where projects are conducted. We took a quick look about the water supply to Kenyas population, where only a fraction of people do have access to a clean-water tap at home, corruption, especially related to service delivery and tried to find data and information on government run sanitation projects. The groups investigating mining in Sierra Leone took a deep dive into the EITI process to see how revenues are distributed – only to find out the country is currently listed as suspended from the process (due to failure to comply to the minimum requirements).
After intense and cold days at the workshop in Limuru (said to be one of the coldest places in Kenya), I spent some days in Nairobi meeting new and old acquaintances. Back in Nairobi it was time for a small data expedition with the Data Dredger team of Internews Kenya. We started from data they had collected on Malaria in Kenya. Three teams quickly formed and explored questions like: is there a relationship between income and malaria, is money better spent on prevention, rather than curing people and having them sick and is malaria better diagnosed and treated in some areas of Kenya than in others. The results were quite interesting: the team investigating income related malaria incidence discovered that younger children are more often sleeping under mosquito nets than older ones – attributed partly to the habit of passing nets down to younger siblings and the particular vulnerability of little children. They also discovered an interesting outlier in mosquito net use of women: women in the 4th income quintile (a quite wealthy quintile) were clearly using nets less often than expected by the otherwise quite linear relationship between net usage and income category. However, in pregnant women, they used nets more often than would have been expected. A fact, that left the team curious. A possible lead is the number of children per woman in higher income families: there is an inverse correlation between wealth and children per family. Thus it could simply signify that children were perceived more important and thus pregnant women received more care.
The day continued with a visit to the iHub – a technology accellerator in Nairobi, started by people around Ushahidi and now a major institution in Nairobi. A year ago they started a research venture and started researching the impact of technology in various areas in Africa. They also are evaluating the Code for Kenya project, organized by our friends at the Open Institute. I met them next in the Nairobi Hacks/Hackers meetup. We talked a lot about how to scrape information from the Kenya Gazette – the official government bulletin. We experimented with Natural Entity Recognition using the ActivityAPI – a small rudimentary scraper was the result. In my planning for meetings, I had further arranged a meeting with Creative Commons in Kenya the next day – only to discover I met the publisher of the online version of the Kenya Gazette – the Kenya Law Report. Before the report was published online it was very hard for both lawyers and citizens to learn about high-court rulings, changes to the law etc. The Law Report started collecting all this information and putting it online. While there is an ongoing initiative to improve the website with a better searchable cross-referenced database – the website as it is already helped to greatly increase the transparency of legal procedings. A quick meeting with the Kenya ICT board – the project leaders for the Government Open Data program concluded the intense series of meetings in Kenya.
Zara Rahman - June 21, 2013 in On the Road
Planet Open Knowledge