In our article published last year on big data for urban development in the developing world, we said At one extreme of smart-city initiatives lies the vision of a centrally coordinated city resting on pervasive use of specialized sensors (e.g., one under each parking space; multiple sensors at intersections), real-time or non-real-time analysis of the resultant big-data flows, and reliance on mathematical models. South Korea’s Songdo is the exemplar. Reports of plans for green-field developments indicate that the Modi government is leaning toward this vision.
Some time ago, Minister of Urban Development and Water Supply Rauff Hakeem announced “Kandy” would be developed as the first “Smart City” in Sri Lanka. While many projects are taking place in Kandy such as Strategic City Development Project, Greater Kandy water supply project, it is important to assess the concept of smart city, and how it can be applied to Sri Lankan context. “Smart City” as a concept emerged during the last few decades. It’s been widely marketed and adapted by private organizations as well as public organizations in cities, due to the introduction use and adaptability of information and communication technology (ICT). At the moment, more people lives in cities compared to rural areas.
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.
The research was done in Sri Lanka, but it was first reported on in India, then in Bangladesh and now the Sri Lankan English-language Sunday newspaper with the largest circulation has chosen to reprint what Nalaka Gunawardene wrote for SciDev. Now we need to work on Pakistan and Nepal. City planners need to know where people live and congregate, when and how they move, their economic conditions, where they spend their money, and about their social networks. Currently the best big data source for these variables involves mobile phones – ubiquitous device used by the rich and poor alike. Mobile network big data (MNBD) is produced by all phones, smart and otherwise, and include call detail records (CDRs) generated when calls and texts are sent or received, web is accessed, and prepaid values are loaded.
I was somewhat disappointed by the Modi government leaning toward the IBM vision of smart cities, where sensors would be ubiquitously placed across green-field new-build satellite cities across India. Our vision is lower cost and seeks to improve existing cities relying on citizens as the principal sensors. So I was pleased to our thinking echoed in a http://blogs.worldbank.org/ic4d/building-smarter-cities?
I am not sure surveying current smartphone users, especially in countries where smartphone penetration is still low, is the best way to gauge the demand for smart-city services, but it is a useful input. Here are some key findings from an Ericsson study that is available on the web. The report – which surveyed over 9,000 smartphone users in nine cities (including Beijing, Delhi and Tokyo) – found that 76% of respondents would use traffic volume maps, while 70% would use energy usage monitors and 66% would use apps to check water quality. “These are services that consumers will expect cities to make available via the internet,” says Michael Bjorn, Ericsson ConsumerLab’s head of research. Bjorn adds that demand for smart-city services could also drive future concepts such as interactive road navigation, social bike/car sharing, indoor maps, as well as healthcare concepts like heart-rate monitoring rings, posture sensors and a digital health network of medical data accessible by physicians.
Technology, especially measuring and monitoring technology, does not exist in a power vacuum. As we struggle with getting our hands on data and finding the best ways of extracting insights, we should also give some thought to power dynamics. Reading this may get the process started. Life in a smart city is a frictionless; free of traffic congestion, optimally lit, with everything from bins to buildings constantly reporting their status and managing their interactions with residents. The smart slum is still a peripheral idea, but we can speculate on the likely impact of extending this ‘smartness’ to slums and make two competing claims.
The Economist talks about how New York and Chicago are using different approaches to the analyze big data generated from within their operations. Sadly, no such activity can be reported from our part of the world. Many cities around the country find themselves in a similar position: they are accumulating data faster than they know what to do with. One approach is to give them to the public. For example, San Francisco, New York, Philadelphia, Boston and Chicago are or soon will be sharing the grades that health inspectors give to restaurants with an online restaurant directory.