Data and Analytics making our cities more intelligent
Many existing cities of developed countries have begun to use IoT technologies to collect data on traffic, pollution, waste management systems, crisis response, and criminal activities to create data sets and run analytics to deploy their resources more efficiently and effectively.
India’s Smart Cities Mission, launched in 2016 by Prime Minister Narendra Modi, is one of India’s most ambitious and exciting projects in urban design and planning. Despite being spoken about time and time again, if asked, most Indian citizens would be unable to articulate what a ‘smart city’ is. The Smart Cities Mission Statement speaks of developing an entire urban eco-system with comprehensive institutional, physical, social, and economic infrastructure – but there is no clear process or design of a city in India that guarantees this ambitious goal. Interpreting what such comprehensive infrastructure would look like or be composed of is still unknown; it is only understood that it should be geared towards providing for the needs and aspirations of these cities’ citizens. A data analytics driven approach might be the missing key that can unlock what these needs and aspirations are, and how best they should be catered to.
When large data sets about human beings are collected, it gives insights into complex human behavior and patterns. While it is impossible to completely define ‘human needs and aspirations’ in the abstract, data and analytics can help provide clear and practical insights into design that can aid in assembling systems that cater to human needs better. Conventionally, urban design focused on presumptions about what human needs might be, and designing infrastructure to furnish these needs. Such presumptions are never absolutely accurate, and are often misplaced enough to create problems. Our cities are, at present, full of municipal programs that while being well-intentioned are unable to get the desired results simply because human behavior isn’t quite what people imagine. Traffic rules and their frequent violations are a good example of the same.
Plugging data analytics into this mix removes the presumptions in modern urban design. If urban planners have clear insights into human behavior at an aggregated level, they are better able to understand how best to provide residents with the services they need. Further, such data can reveal the priorities and desires of citizens. For example, how far should a marketplace be from a residential area? While proximity is convenient, the constant urban problems of crowding and parking negatively impact the quality of life of nearby residents. Keeping the commercial area too far makes it too remote and inconvenient. Having insights into what the ideal distance should be to keep everyone happy makes designing that much more intuitive, responsive, and efficient.
Urban design, especially of smart cities, depends substantially on being able to account for and prioritize between wide varieties of residents’ needs. The sheer volume of variables – from designing and allocating zones for schools, hospitals, police stations, and government offices, to setting up public information and grievance redressal systems would be impossible for any individual to manage. Large teams of designers are generally used for such large-scale projects, which make coordination difficult and leads to some asymmetry or the other. An analytics-based visualization tool based on large data sets and geo-mapping would allow urban designers to keep a macro-level view of the entire city’s design needs to ensure a comprehensive solution that accounts for all the needs of the citizens of these smart cities.
Even past the design stage, data analytics has huge applications to the functioning of smart cities. Many existing cities of developed countries have begun to use IoT technologies to collect data on traffic, pollution, waste management systems, crisis response, and criminal activities to create data sets and run analytics to deploy their resources more efficiently and effectively. By implementing such data collection systems in these upcoming smart cities, urban planners will ensure more responsive and efficient governance. This will optimize delivery of services to citizens while saving substantially on their tax dollars. For example, the Avon and Somerset Constabulary in the United Kingdom deployed Qlik’s data analytics platform and saved nearly £60 million while achieving better coordination and information sharing. Thanks to the highly-contextual real-time data which was analyzed and visualized in an easily consumable format, the Constabulary officers were able to respond to incidents faster and were better prepared, a point of difference that is extremely critical in high-risk situations. Suspect management also improved significantly, as Qlik helped officers in identifying and dealing with the top-thirty highest risk offenders in the region. Their success in capturing twenty-five of their ‘most wanted’ suspects from the area in a week highlights the effectiveness of the analytics solutions they used.
There are many such successes across the globe as governments have begun to recognize the benefits of using data analytics in civic services. Applying them to cities being built on the basis of these cues would only make these services more efficient. What India’s smart cities will end up looking like is anyone’s guess, given the nascent stage of operations and the many variables still to be determined. However, one thing seems adequately clear –data analytics will be the engine that will drive the development and the efficient running of the future home of India’s urban population.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house