You are browsing the archive for Anders Pedersen.

Join us for a Data Expedition on water in Dar es Salaam on June 6th

- June 3, 2014 in Events

A Data Expedition in Dar es Salaam coming up

School of Data and Code for Africa are stopping by Dar es Salaam in Tanzania this week – to celebrate this we’ll host a Data Expedition on Data from the Water Ministry and other public data we can surface in Tanzania on:

Friday June 6th from 9:30 to 16:00 at BUNI Hub Join the team and register for free here.

David from Code for Africa and Ketty and Michael from School of Data are in Tanzania this week, mainly to assist the Ministry of Water and the National Bureau of Statistics. To round off the week they will guide a Data Expedition using some of the Data they have worked with. Data Expeditions are an experimental training concept developed by the School of Data to help you start working with data. You will explore different aspects of the data in a small team and learn from each other as well as our trainers in how to best deal with it.

Join us at:

Ali Hassan Mwinyi Road, Kijitonyama
P.O. Box 4302
Dar es Salaam

Food and drinks will be provided for.

The event is generously hosted by BUNI hub under the TANZICT Project, the World Bank and the Partnership for Open Data. We’ll start out at 10am and work until around 4pm. A snack will be provided for lunch.

Flattr this!

Mapping company network in the Nigerian extractive industry

- December 23, 2013 in Data Expeditions

On December 7 data wranglers, maptivists and arm chair researchers got together for an online Data Expedition to analyse the Nigerian extractive industry in a collaboration between School of Data, African Media Initiative and OpenOil. More than 30 participants checked into the Unhangout, took part in one of the three teams and joined trainings on mapping. session wherefrom presentations and trainings where conducted.
Below you will find some of the results from the amazing work that the teams pulled off on the day and during the following days to unlock data from the explored corners of pipelines and company registrations within the oil contractors in Nigeria.

###Investigating the corporate networks in Nigeria’s oil industry
A team of dedicated researchers collected data on more than 100 companies within the corporate supply chain of the oil industry contributing to a Gephi network visualisation (image below). The network analysis shows how companies in the extractives sector are connected based on contracts and ownership and thanks to the team the network expanded substantially during the expedition leaving a model for future research.


In another company analysis project Khadija Sharife, an investigative journalist from African Media Initiative, collected a list of oil rigs operating off-shore in Nigeria.

###Mapping the pipelines and other parts of the oil infrastructure
Another team headed up an exercise to map essential elements of the oil infrastructure in 12 of the highest producing concession areas operated by Big Oil. Participants traced water basins located near oil drills, pipelines and other items which could be identified from satellite imagery. The map below below shows the annotations completed during the expedition.

View Nigeria Onshore Concessions – First Round in a larger map

###Analysing the revenues from oil
The third team conducted an analysis of the government revenues from the oil industry and its impact on the Nigerian budget. Participants started out by reviewing the complex revenue flows, oil terminology and revenue data available from the Nigeria country report to the Extractive Industries Transparency Initiative. As part of the work on revenue analysis Heather Leson headed up some data collection of the Canadian extractive industry and its interests in Nigeria. The team extracted data on Nigeria’s oil and gas revenues from 2009-2011 from the 2012 NEITI report and ended up with this treemap.

You can find all the their notes in this team scratch pad.

Flattr this!

Data Expedition, December 7: Investigate the Extractive Industries of Nigeria

- November 15, 2013 in Data Expeditions

The data expedition described on this post is over. For more recent data expedition announcements, visit the blog.


Who operates the often poisonous wells in the Niger Delta? How does the money flow between the contractors running the oil fields and the government?

Join us for an online Data Expedition to Investigate the Extractive Industries of Nigeria December 7, Noon-18:00 CET / Lagos.

Register for free

###The problem: Companies hide in plain sight
Data on the extractives industry is increasingly going public, from EITI‘s information about money flows from companies to governments to the UK’s decision to make its register of the beneficial owners of private companies public in the future. As more information about the oil, gas and mining industries makes it into the public domain, more people living in resource-rich countries have the potential to benefit. Information transparency can lead to greater public scrutiny of these industries that affect so many lives. Databases such as OpenCorporates are rapidly expanding and making companies involved in extractives and other industries easier to trace. Meanwhile, other data published in local media or tucked away in companies’ annual reports has seemingly been hiding in plain sight for years.

###What are we going to do?
We want to begin cracking this data open and analysing it to facilitate investigations by journalists, organisations, activists and governments who all need to know how extractives impact people’s lives. In collaboration with OpenOil and African Media Initiative, School of Data will bring together those with an interest in learning to work with data to help tackle some of the biggest issues in the extractive industries today, with a focus on Nigeria. The Data Expedition will complement our recently launched Follow the Money network, which pushes for the transparency needed to help citizens around the world use information about public money to hold decision-makers to account.

###What will you learn?
– Network analysis: Investigate the corporate supply chain in Nigeria’s oil industry by using networks to see who is connected to whom
– Corporate research: Cut through generic names like “Shell” and “Exxon” to identify the specific corporate vehicles responsible for activities in places such as the Niger Delta
– Mapping: Work with maps of geo-coded oil spills, company license areas and other data to draw connections that might not be apparent in text-based media
– Web-scraping: Find company data and establish leads for other investigations related to the oil industry by scraping the web

To join the Data Expedition register in the form below!

Flattr this!

Mapping public finances in Nigeria

- November 13, 2013 in Data Expeditions, Data for CSOs


On October 10 School of Data ran a Data Expedition at the OpenGov Hub in Washington DC to explore the public contracts and finances in Nigeria. The Data Expedition was organised in collaboration with BudgIT in Nigeria: an organisation which promotes awareness about Nigeria’s finances through data driven campaigning.

###Tracing government revenue flows

Participants at the expedition took a closer look at the complex public finances of Nigeria. Revenues from ordinary taxes as well as the extractive industry are channeled through a multiplicity of entities inside the government. Isabel Munilla led a attempt to draft a flow chart of how Nigerian revenues might be administered and steered through the government (image above).

###Mapping government contracts
Participants also decided to  investigate how federal Nigerian contracts are distributed by government departments and to which companies they are directed. The procurement data was scraped from PDF-format by BudgIT in Lagos and coded according to budget classifications, while participants at the OpenGov Hub geocoded the contracts based on locations where work and services were planned to be delivered. The geo-coding process resulted in a map of the distribution of government contracts (image at top).

The data cleaning and formatting of the procurement data also enabled the team to add it to OpenSpending. It turned out that a sizeable share of the more than 300 contracts were awarded by the Nigerian Ministry of Petroleum Resources. Many of these contracts relate to public school projects in oil rich areas as an outcome of large oil drilling contracts.

The Data Expedition was supported by the Department for International Development (UK) and concluded with presentations for members of the Steering Committee of the Global Partnership for Effective Development Co-operation. The presentations were finally followed up by a discussion about the benefits of open data in development.

Flattr this!

Announcing the Data Expedition: Investigate the Garment Factories

- October 2, 2013 in Data Expeditions

The data expedition described on this post is over. For more recent data expedition announcements, visit the blog.
Photo credit: Andy Teo

Photo credit: Andy Teo

In May, the School of Data community got together in a Data Expedition to respond to the Rana Plaza catastrophe. They built a crowdsourced database on garment factories and used it to expose the bad safety standards and non-transparency that contributed to the disaster.

Now we are taking the garment factory investigation to the next level with a new online Data Expedition. School of Data (a collaboration between the Open Knowledge Foundation and Peer 2 Peer University) together with the International Labor Rights Forum, School of Data will bring data explorers from around the globe together online to answer some of the tough questions about the global garment industry. Join us for the Online Data Expedition: Investigate the Garment Factories October 18-20.

What we will do:

  • Geocode garment factories with the Open Street Map community
  • Create visualizations to explore and explain the data from the global garment supply chain
  • Investigate the global supply chains: find new data sources and dig into the key issues in the garment supply chain

Join the Data Expedition using this participant signup form or in the form below!

The Data Expedition will also take place at on-site locations around the world such as Dhaka (Bangladesh). Would you like to help us turn the online expedition into a fantastic global expedition? Sign up to organise a local data expedition or help us run the global expedition! We’ll be there to support you along the way. All you need is a small venue, some great friends, and lots of curiosity.

Get in touch to help facilitate the expedition with this facilitator signup form.

Flattr this!

Data Expedition story: Why garment retailers need to do more in Bangladesh

- June 4, 2013 in Data Expeditions, Data for CSOs

On May 25-26 almost 50 participants from several teams set out on a data expedition to map the garment factories. This is a report from the team comprised of Roy Keyes, Naomi Colvin, Sybern, Bhanupriya Rao and Daniela Mattern. The team used a crowdsourced database on garment factories to expose questionable standards and highlight the need for open supplier lists from all retailers. The article concludes that major retailers like Wal-Mart maintains high levels of opacity around their supply chain and audit standards, which are detrimental to improving working standards in the garment industry.

Not the first time!
When the Rana Plaza collapsed killing 1127 people and injuring over 2500 people of its 5000 workforce, it shocked the world and shone an instant light on the working conditions of the garment factories in Bangladesh. While it may have been the worst disaster of our times, it is my no means the first in Bangladesh, where fire due to faulty electrics and short-circuits or building collapses due to structural and maintenance issues are commonplace. Just 8 days later, another fire broke out in one of the Tung Hai group factory killing 8 people. The fire in Tazreen garment factory in November 2012, which killed 100 people should have acted as a wake up call to take health and safety issues seriously. But all it did was lull the government, retailers and the Bangladesh Garment Manufacturers and Exporters Association (BGMEA) into deeper slumber after dubbing it as arson.

The Rana Plaza tragedy seemed like a rude awakening, one that shone a spotlight on the appalling conditions that Human Rights Watch and others have warned about for many years in sweat shops. There was an instant rush by Western retailers who source a major chunk of their ready-made garments from Bangladesh, to appear to be doing the right thing: to be holier-than-thou. Wal-Mart was quick to release a list of 250 factories that it blacklisted from its supplier list in what appears to be a PR exercise, without any transparency around their audit findings or the exact reasons for the blacklist except for a vague statement that the ‘violations could relate to safety issues, social issues, unauthorized subcontracting or other requirements established by our set of Standards for Suppliers. Suffice it to say that, H&M still sources from eleven and Van-Gruppen from two of the factories. In the absence of transparent data on their methods of audit and their findings, simply blacklisting of companies is not very helpful. Wal-Mart’s blacklist consists of large textile groups such as Akh Fashions, Hop Lun and Mohammadi Group that that own several factories and supply to several big western retailers. MJ Group – whose subsidiary, Columbia Garments, is on the Wal-Mart list – lists Replay, New Yorker, C&A, Espirit, GAP, Old Navy and Macys alongside H&M as customers on its website.

Sustainability and Ethical codes
The essential point being missed in the rush to appear holier-than-thou is the compliance with ethical standards initiatives that rely largely on a multi-stake holder model. Worldwide Responsible Accredited Production (WRAP) is one such accreditation initiative which has released a list of 194 factories in Bangladesh that meets its standards. That these certified factories constitute a mere 3% of all factories in Bangladesh gives us an insight into how far the industry has to go as far as certification is concerned. Interestingly, 22 of the Wal-Mart blacklisted factories feature on this list. While Wal-Mart was quick to disclose a blacklist in a bid to appear responsible, it would do well to disclose all its suppliers in the interests of transparency and responsible sourcing.

H&M has been much more transparent here, not just disclosing a list of its worldwide suppliers, but also spelling out its stringent audit policy. Only one H&M factory was both WRAP certified and on Wal-Mart blacklist. And the story is a bit more encouraging because 15% of H&M’s suppliers in Bangladesh are WRAP accredited. Brands like Puma (10%) and Varner-Gruppen (15%) show some good signs of sourcing from accredited suppliers as opposed to Timberland and Nike, none of whose suppliers are WRAP accredited. While by no means adequate, it does show that some retailers are better at sourcing ethically than the others.

Table: Which retailers use WRAP Certified factories?



in Bangladesh

WRAP Certified

Retailer % WRAP Certified

























Source: Crowdsourced garment factory list

The blacklist from Wal-Mart is pretty rich considering that along with Gap it has refused to sign the Accord on Fire and Building Safety in Bangladesh, instead preferring to rely on their own codes and audits. H&M was the first retailer, followed by 31 others, to sign the agreement which includes provisions for independent safety inspections, mandatory repairs and renovations and a commitment to pay for them and a role for workers and their unions to make garment factories safe in Bangladesh safe. The accord is a watershed moment for the reason that it is a multilateral initiative driven by retailers, global unions IndustriALL and UNI, in alliance with Clean Clothes Campaign and Worker Rights Consortium.

It certainly could be the last!
In the aftermath of the Wal-Mart blacklist, other retailers like H&M have rushed in to rethink their sourcing policy and look at new supply chains in Africa and Latin America. While any rethink is welcome, it needs to be in the area of more responsible auditing, greater transparency in supply chains, not just of primary suppliers, but secondary ones where there is astounding opacity. What would be a great step forward for western retailers like H&M is to make public their factory wise audit findings for greater accountability. Simply moving supply chains and tolerating the same conditions will not see the end of tragedies such as the Rana Plaza. There needs to be timely and better audit data as well as supplier data down to the last in the supply chain as well as greater commitment to multi-stakeholder processes such as the Fire safety accord. This could be the beginning of a long-term political engagement on workers safety and better wage and working conditions. This also means that Rana Plaza could be the last in the list of terrible tragedies.

Flattr this!

Data Expedition: Mapping the garment factories

- May 18, 2013 in Data Expeditions

Women sewing at long tables next to tall windows in a garment factory.

The horrific factory collapse at Rana Plaza in Dhaka has brought the business practices of global garment brands, as well their thousands of suppliers, into the spotlight.

At School of Data we noted that corrupt and missing data were part of the story. Data on building permits in Bangladesh is largely unavailable due to lack of state inspections. However, after years of pressure on global apparel brands from labor activists, the publishing of garment factory supplier lists is becoming increasingly standardized. We’re asking you to join us in mapping the data on garment factories.

Data Expedition: Mapping the garment factories 

When: Saturday May 25 – 12:00 BST to May 26 18:00 BST – link to your timezone

We’ll be looking for projects such as:

  • Mapping garment factories locally and globally

  • Exploring the global supply chain of garment export and imports

  • Mapping the ownership of local factories and global brands with open company data

  • Finding stories and patterns in the connections between global brands and local garment factories

Sign up here for the Data Expedition!

Please note that limited space is available. For more information about the Data Expedition format, we encourage you to read this article.

Before the Data Expedition – Help us build an open garment factory supply list

Before heading out on this important expedition, we’ll need to gather as much data as possible on garment factories. Labor activists and campaigners typically articulate the data in terms of “supplier lists.” Some brands, such as Nike, provide a list of all factories in their supplier network via Excel and JSON downloads; while others, such as Levi-Strauss, only offer lists in PDF format. In order to prepare a solid dataset for the Data Expedition, we’re asking you to help locate, clean, and merge the supplier lists from across garment brands into one comprehensive Open Garment Factory List.

Begin today by adding to the Open Garment Factory List and join us for a GoogleHangout on Thursday, 23 May at 19:00 CET, where we’ll be engaging in joint data collection.

Flattr this!

How to visualize political coalitions in electoral coverage

- January 24, 2013 in Data Stories

This post was written by Gregor Aisch.

If I was asked for the golden rule of information visualization, it would be:

“Show the most important thing first!”

Not second or third, but first! And what is the most important thing to show about the outcome of an election? Who actually won.

In political systems like Germany’s, where we have no party getting anywhere near 50% of the vote, the usual one-bar-per-party bar charts totally fail to answer this most important question.

For example, in the following chart we can see the number of seats won by different political parties – but this does not tell us who won the election.


This is because the elections are not won by the party with the most votes, but the party who manages to get a majority of seats in parliament. And, with the exception of Bavaria and Hamburg, in Germany there’s no way to take government without forming a coalition.

The bar chart above makes it tremendously difficult for readers to figure out which coalition has won. Therefore one must calculate the total number of seats for each coalition, and compare it to the number of seats needed for a majority (which itself is the sum of each parties seats divided by two).

Humans aren’t particularly good at calculating and weighing up these different possibilities on the fly. That’s why most election reporting websites show an additional coalition view. But where is it? Right – often it is the last thing that they show, such as in this recent example from the Zeit Online:


How coalitions have been visualized in the past

In past elections in Germany coalitions have been visualized in two different ways: either as simple horizontal bar chart or as an interactive coalition calculator.


The simple bar chart (as seen above) usually shows a limited selection of two or three coalitions having a majority. One problem is that sometimes it would be interesting to compare those coalitions with other possible – but politically unlikely – coalitions, such as the CDU and the Greens in Germany.

The second problem is that excluding the coalitions that fail to have a majority eliminates valuable contextual information.

Do it yourself: the coalition calculator

An alternative approach is the coalition calculator. The main idea is to let the users try out their own coalitions and see whether or not a given coalition could have a majority.


However, this puts quite a bit of effort onto the user, who might well be checking back several times during election nights. Also the calculator only shows one or two coalitions at a time, so it’s hard to actually compare different possible coalitions.

A new approach: extended coalition charts

This isn’t exactly groundbreaking stuff, but for some reason nobody seems to have ever visualized elections this way before. The idea is to show as many coalitions as possible side by side, including the politically unlikely and those who fail the majority.

To visually separate the winning coalitions from the rest I finally decided to simply pull them apart. Since I need some more space I went for vertical bars instead.


There’s a nice side effect of showing all coalitions: when new preliminary results are coming in during election nights, the visualization doesn’t show an entirely different picture, but some coalitions simply ‘change sides’.

Since the actual total number of seats depends on the election results, I decided to label the coalitions seats with relative numbers. This means that instead of saying ‘coalition X has 70 seats’ we say ‘3 seats are missing for majority’.

Coalition maps

It was a small step from extended coalition charts to coalition maps. A coalition map shows in which election districts a coalition holds the majority of votes. To indicate the coalition I decided to go for diagonal stripes, although I don’t recommend looking at them for too long. :-)


It is interesting to compare how coalition maps vary between coalitions. For instance from comparing the two most preferred coalitions you can see a clear divide within the state Lower Saxony in the North West.


Try it out!

If you want to try out either the extended charts or the mapping mini-apps shown above, then you can grab the code via the following links. All of my examples above were created using the open source Raphaël JavaScript Library.

Note: This post is a translated version of this one.

Flattr this!

Data roundup, January 16

- January 16, 2013 in Data Roundup

We’re rounding up data news from the web each week. If you have a data news tip, send it to us at [email protected].

Credit: Library of Congress / Euclid vander Kroew CC BY-NC-SA 2.0


Hacks and Hackers is organizing a Meet Up in London tonight January 16th under the title: “How big data is changing financial journalism”. The line up includes talks from Retuters, Bloomberg News and Open Corporates. As of writing registrations are still open.

The free online course Introduction to Databases from Stanford University started this week on January 14. Registration is still open and the course will run until the final exams on March 21. The entire course schedule is available here.

On February 2-3. Stanford University and Columbia Journalism School (both US) will co-host a bi-coastal Datafest on Big Money and Big Data. The ideas page already gives a hint of some interesting projects using R and Stata.


The New Scientist launched a site on global warming, which enables you to check how temperatures have increased locally around the world since 1893. Their nerd box provides some details on the data they used from the Goddard Institute for Space Studies at NASA.

In the US the publication of a map with information on addresses for citizens holding gun licenses by the local newspaper The Journal News sparked a lively public debate about how media outlets use access to public records and the ethics of journalism.

A Reuters series about the last two decades of growing income inequality in the US demonstrated how data analysis and classical reporting combined can help explain complex economic issues.


The European Union launched a public beta of the Open Data Hub with 5815 datasets primarily from Eurostat, but also other agencies.

Finally the Data Blog of the Guardian they published a handy index with links to all datasets they have used since 2009.


Flattr this!