It’s always a pleasure to hear LIRNEasia research cited by others. Its even more pleasing when the person mentioning it is a senior government official (in this case the Chairman of Mongolia’s National Statistics Office) and that too at a briefing for senior government officials at Mongolia’s Parliament. Without knowing that LIRNEasia too was in the room, Chairman Mendsaikhan showcased some of LIRNEasia’s ongoing big data research as examples that were relevant for Mongolia and which should be ideally replicated. The briefing was part of a series of events on Data for Development held in Ulaanbaatar on 24th and 25th Feb 2016. The events were jointly organized by the World Bank and the Cabinet Secretariat of Mongolia, who had invited me to share our experience in leveraging big data for development in Sri Lanka.
Daniel Solove’s work forms the basis of our recent analyses of big data privacy. It is impressive that he pulls together a comprehensive analysis of the implications of the passing of Justice Scalia for the third-party doctrine within a day. Justice Scalia’s opinion in Jones actually provides very little protection against government location tracking. Only the physical affixing of a GPS device to a car violates the 4th Amendment according to his view. But under the third party doctrine, the government can readily obtain GPS data from third parties that provide GPS services without a physical trespass to the car.
As befitting an article on BIG data, the writer of this piece, done for Center for Internet and Society, is liberal with superlatives. A colossal increase in the rate of digitization has resulted in an unprecedented increment in the amount of Big Data available, especially through the rapid diffusion cellular technology. The importance of mobile phones as a significant source of data, especially in low income demographics cannot be overstated. This can be used to understand the needs and behaviors of large populations, providing an in depth insight into the relevant context within which valuable assessments as to the competencies, suitability and feasibilities of various policy mechanisms and legal instruments can be made. However, this explosion of data does have a lasting impact on how individuals and organizations interact with each other, which might not always be reflected in the interpretation of raw data without a contextual understanding of the demographic.
I first talked about the competitive issues of big data at the 2013 IGF in Bali. In actual fact the competitive implications of a subset, utility customer information, were discussed back in 1992. But it was rare to think that there was anything to talk about other than privacy. Finally, the message seems to be getting through. The concern is that while data can give a business competitive advantage, unique treasure troves of data can provide one player with unique insight and, potentially that can be translated into market power.
Samarajiva, R & Lokanathan, S.
Looks like what we wrote in EPW is having ripple effects. A cost-effective, intermediary method of collecting data has emerged in the form of direct source collection, in which individual citizens themselves are relied upon as the primary source of information. This is done through mobile network data, generated by all cellphones and includes information such as frequency and duration of calls, Internet plans and visitor location registry data. In cities buckling under the pressure of a growing population and facing a possible breakdown of infrastructure, this method of pre-informed planning allows the populace itself to contribute to the solution. LIRNEasia, a Sri Lanka-based think tank, has carried out an extensive study demonstrating the value of mobile network data.
One thing working on big data has done is to sensitize us to the power of visualization, especially using maps. Here is one that impresses, especially in view of our focus on urban development: Data viz extraordinaire Max Galka created this map using NASA’s gridded population data, which counts the global population within each nine-square-mile patch of Earth, instead of within each each district, state, or country border. Out of the 28 million total cells, the ones with a population over 8,000 are colored in yellow. That means each yellow cell has a population density of about 900 people per square mile—“roughly the same population density as the state of Massachusetts,” Galka writes in the accompanying blog post. The black regions, meanwhile, reflect sparser population clusters.
Returning from an expert meeting on big data n Bangkok, I was in the passenger seat on the way back from the airport. Looked up Google maps for the traffic. This feature has been available in Sri Lanka only for a few weeks. On the main roads, it was pretty accurate. Once we turned off to a busy, but not-a-principal road, the traffic indicator went blank.
ESCAP is part of the UN. By design, it is better positioned to work across silos than specialized agencies such as the ITU and WHO. One of the key points made about the sustainable development goals that were recently adopted is that they require working across silos. Big data naturally cuts across disciplinary boundaries. It transcends organizational silos.
In a few hours I’ll be speaking at a panel on how big data is already being used for sustainable development. ESCAP has pulled together an interesting group of people together to talk about how big data can help with the daunting task of measuring progress on the 169 targets that the UN has set for itself. The slides I will be using are here. Sorry they do not tell the full story since I have been asked to keep them to a minimum.
The Urban Development Authority of Sri Lanka and the Young Planners Association of Sri Lanka organized a workshop at the UDA premises on 4th December 2015 for LIRNEasia to share is ongoing research on leveraging mobile network big data for urban and transportation planning. The slides are available HERE.
Big data is sexy these days but still it’s a big deal to get coverage in the New York Times for research conducted in Rwanda. Josh’s work is complex and involves training data sets and also the use of multiple kinds of data. He and his colleagues relied on anonymized data on billions of interactions, including details about when calls were made and received and the length of the calls. The researchers also looked at when text messages were sent, and which cellphone towers the texts and calls were routed through in order to get a rough idea of geographic location. “So it’s the who, where and when of the call, but not the what or the why,” Dr.
I spent the last two days at a meeting on big data in the global south. The sixty people in the room had no shared understanding of big data, which led to some interesting discussions. Then someone stated that he wished big data would be defined. Big data is characterized by volume and variety. The third part of the 3 Vs, velocity, is irrelevant, as has been argued by many including Viktor Mayer-Schonberger.
I was a little surprised to be invited to a meeting on big data organized by the Institute of Technology and Society of Rio de Janeiro. But then I realized that the event was scheduled back to back with IGF 2015 in Joao Pessoa and that they were basically piggy-backing on the attraction of large numbers of international experts to Brazil in November 2015. With some effort, I was able to find a few people who were not lawyers participating in the event, but it was dominated by those of the legal persuasion. This meant that there was a presumption that laws and regulations were needed to avoid the bad things that could be imagined. Usually, what we have is a battle of imaginations.
Today I spoke at a session on Big Data for Development: Privacy Risks and Opportunities organized by UN Global Pulse and SIDA at Internet Governance Forum 2015. My presentation that sought to set the stage is here. Many interesting questions were raised, but I will here focus on one particularly uninformed one. The questioners (this is a synthesis of two questions) said that while the data holders may give data for free, they will start to charge for it soon. Therefore, it is important to ensure that the value of the data created by mobile users should be addressed and that users should get paid for their data.
Today I had the pleasure to talk about LIRNEasia’s ongoing multi-disciplinary big data for development research at the IDRC Asian Regional Office in Delhi. The work that we have been doing in this space has been funded primarily by IDRC. It was engaging talking to experts with interests in different domains (agriculture, health, governance, climate change adaptability, urban and transportation policy, electricity, livelihoods) working in India as well as elsewhere. The slideset I used is here.