Do We Need Big Data to Create Smart Cities?

Do We Need Big Data to Create Smart Cities?

Data collection and analysis have played an important role in smart cities, but should citizens’ privacy be used as a foundation?

The real-time analysis of data procured over various electronic platforms has become integral to the development of infrastructure, planning and policy formulation within cities across Asia. Big data collected from social media, cellphone usage patterns and mobile networks has become instrumental in the planning of cities, enabling a form of urban development that allows for an efficient and (close to) an equitable allocation of resources.

With the population of cities on an exponential rise, the only available alternative to meet the demands of the growing numbers is to use their behavioral patterns, reflected in their cyber footprint, to create a system of predictable assessment—one that would be able to deliver specific services to meet this growing demand.

This can be done through a real-time analysis of data collected over a period of time from specific geographic clusters. For example, if a particular arterial road has been blocked due to maintenance requirements or an accident, an alternative route can be established well before traffic begins to build up by simply analyzing the information collected from mobile networks, GPS systems and traffic lights. Big data has become integral to urban planning in the 21 century.

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Smart cities have become a central component of policy deliberations in South Asia. Prime Minister Narendra Modi recently committed to creating close to 100 smart cities in India. But what exactly is a smart city?

Smart cities have been designed as localized hubs where the information communication technologies (ICTs) are used to create feedback loops with significantly minimized time gaps.

Earlier, the only legitimate source of collecting data for the formulation of central initiatives or government policies used to be a state-sponsored census. However, these surveys have begun to falter in their accuracy resulting in policymakers becoming more averse to relying on this data.

Smart cities use a refined and significantly more accurate method of enabling data collection. A central framework of sensors is used to determine the movement and behavior of communities of individuals in real-time. An example of this is the parking space sensors placed at intersections in the South Korean Smart City of Songdo. These sensors are able to relay information regarding travel patterns, individual commuter preferences and, with a more in-depth analysis, economic demographics within short spans of time.

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Another popular model has been to use crowdsourced information from applications that have been developed by private companies. They organize data into easily retrievable sets, which reveal information on economic demographics, housing and travel patterns of populations, thus attuning the raw data to the needs of the public in a cohesive manner. This system, however, requires extremely efficient means of organization, without which any significantly useful results would be impossible to achieve.

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.

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LIRNEasia, a Sri Lanka-based think tank, has carried out an extensive study demonstrating the value of mobile network data.;

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