Colombo was recently the host of the South Asian Urban Forum 2015 that was held from 21st-23rd September 2015. The main objective of this event was to encourage researchers to approach the rapid urbanization of South Asia from the viewpoint of South Asians. LIRNEasia researchers working on the Big Data for Development research participated in the forum and presented our ongoing research at the Researchers’ Forum that was held on the third day at the Department of Town and Country Planning of the University of Moratuwa. The audience included experienced researchers in urban infrastructure and planning, was held at the department of town and country planning of University of Moratuwa. Danaja Maldeniya and Kaushalya Madhawa presented their ongoing work.
It was a long way to come for a one-day workshop so I was hoping it would be a good event. It was. The Joint Research Center is described as the European Commission’s in-house science service. Within it exists an Institute for Prospective Technological Studies. I guess it was inspired by the Office of Technology Assessment that the US created in 1972 and shut down in the Gingrichian frenzy in the mid 1990s.
Maldeniya, D., Kumarage, A., Lokanathan, S., Kriendler, G., & Madhawa, K.
Madhawa, K., Lokanathan, S., Samarajiva, R., & Maldeniya, D.
Madhawa, K., Lokanathan, S., Maldeniya, D., & Samarajiva, R.
The European Union Joint Research Center-Institute of Prospective Technological Studies is convening a workshop in Sevilla on Big Data for the analysis of Digital Economy and Society on 22 September 2015. LIRNEasia has been invited to speak on Big data for development: New opportunities for emerging markets. The slideset is here.
Our intervention in the most widely read English daily in Sri Lanka emphasizes the “smart” in smart cities. A middle option focuses on citizens moving through time and space in the city as the primary sensors. They generate the big data that when analyzed constitute the feedback that is the essence of a smart city. Experimentation and learning are integral to this low-cost approach. It is especially appropriate for the organically developed, congested cities in developing countries where the costs of installing and maintaining city-owned sensors would be quite high.
Dr Srinath Perera, who heads research at WSO2 and serves as an advisor to our big data team, has written a blog post with the above heading, where he makes reference to our work. Understand social dynamics like people geographic distribution, demographic distribution, mobility patterns etc to aid in policy and urban planning. This can be done through data sets like Census, CDR data, social media data ( in the right context) etc. The good news is this is already underway by Lirneasia ( see Big Data for Development Project, http://lirneasia.net/projects/bd4d/).
A recent write up by IDRC featured our big data work. Similarly, private telecom companies’ data on mobile phone traffic has become a crucial resource for researchers at the Sri Lanka-based think tank LIRNEasia, a long-time IDRC research partner. Using phone data that tracks traffic flows can be a low-cost means of helping governments decide where to invest in road and public transport upgrades, says LIRNEasia chair Rohan Samarajiva. Since mobile phones are ubiquitous in Sri Lanka and phone traffic data is anonymous, studies are less likely to be biased in favour of the rich, he says. “We see that a mobile phone travels down a highway at a certain speed, but whether it’s rich or poor, travelling in a car or bus or motorbike — we don’t know.
In the context of LIRNEasia’s big data work, we intend to wrestle with these issues. If we are not getting our hands dirty with the data and the stories we extract from them, I fear the conversation will be sterile. First, students should learn that design choices in algorithms embody value judgments and therefore bias the way systems operate. They should also learn that these things are subtle: For example, designing an algorithm for targeted advertising that is gender-neutral is more complicated than simply ensuring that gender is ignored. They need to understand that classification rules obtained by machine learning are not immune from bias, especially when historical data incorporates bias.
I will be participating in this Internet Governance Forum session in Joao Pessoa, Brazil, later this year. The session is organized by UN Global Pulse: In recent years, the potential of big data derived from the Internet and other digital devices to transform targeted advertising, recommender systems, location based services, logistics and other activities in the private sector has come to fruition. Increasingly, parallel applications in development work have emerged, proving the utility of big data for monitoring and measuring social phenomenon including disease outbreaks, food security, or migration. However, the opportunities presented by big data simultaneously raise serious concerns about privacy, especially when it comes to use of personal data. To realize the benefits of “Big Data for Development” it is important to find solutions for how to protect fundamental rights and values, including the right to privacy as recognized by the UDHR and ICCPR.
Late May in Ottawa, I was among those interviewed for an article about big data. Similarly, private telecom companies’ data on mobile phone traffic has become a crucial resource for researchers at the Sri Lanka-based think tank LIRNEasia, a long-time IDRC research partner. Using phone data that tracks traffic flows can be a low-cost means of helping governments decide where to invest in road and public transport upgrades, says LIRNEasia chair Rohan Samarajiva. Since mobile phones are ubiquitous in Sri Lanka and phone traffic data is anonymous, studies are less likely to be biased in favour of the rich, he says. “We see that a mobile phone travels down a highway at a certain speed, but whether it’s rich or poor, travelling in a car or bus or motorbike — we don’t know.
The full webcast of the Shades of Open session which dealt with whether data held by private entities should be open is available here. At the session moderated by Stefaan Verhulst, I framed the issues within the context of principal-agent theory and competition and illustrated my arguments from our experience in working with mobile network big data. I went first, so my opening presentation is at 4:26. The second intervention is at around 26:00.
New generation aircrafts like Boeing 787 Dreamliner or Airbus A350 are getting smarter. They are even more connected than the passengers they carry. According to Airbus, its A380 superjumbo — which first flew a decade ago — collects information on more than 200,000 aspects of every flight. With one terabyte of data generated on every flight, aircraft manufacturers are considering how to leverage the information they gather across their global fleets. The aviation industries are excited about this wealth of data.
The Deputy Mayor of New York City under Bloomberg and Google are launching a new initiative, presumably for cities in the developed economies, that will take an approach different from the sensor-intensive centralized models promoted by IBM and the like, according to NYT: Major technology companies, like IBM and Cisco, already have large businesses that apply information technology, to improving the efficiency of cities. IBM has used its researchers and technical prowess in projects like traffic management in Stockholm and microlevel weather forecasting to predict the location of life-threatening mudslides in Rio de Janeiro. Sidewalk Labs, Mr. Doctoroff said, planned to work in “the huge space between civic hackers and traditional big technology companies.” While big technology companies take a “top-down approach and seek to embed themselves in a city’s infrastructure,” he said Sidewalk Labs would instead seek to develop “technology platforms that people can plug into” for things like managing energy use or altering commuting habits.
LIRNEasia was among team of researchers under the umbrella of Data-Pop Alliance was contracted to produce a synthesis report as part of a DfID project on Big Data for climate change and disaster resilience in developing countries, meant to feed into the 2016 World Humanitarian Summit. In this context I participated in a discussion workshop in London on June 5, 2015. Also participating were researchers from Flowminder (working on big data and climate change issues in Bangladesh) and University College London (analyzing all kinds of big data, including GPS locations of fishing fleets worldwide and data from public-transport payment cards. There was much discussion about how big data research could be made more responsive to the needs of users (defined as including citizens rather than government officials) and on inclusiveness. As the only South-based research organization that had obtained data and conceptualized and executed big data research, LIRNEasia was asked to present its experience in the form of a case study on how barriers to data access could be overcome.