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Useful Insights: Predictive analytics enables developers to anticipate citizen needs

July 22, 2015
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Across the world, urban development is being characterised by a greater focus on the deployment of cutting-edge technologies to address issues regarding resource management, urban mobility and public safety. Developers at large are deploying various communication and IT tools to pave the way for a connected and far more enriched lifestyle for the inhabitants of smart buildings, townships and campuses. According to Navigant Research, global smart city technology revenue is expected to grow from $8.8 billion in 2014 to $27.5 billion in 2023. The smart city concept is driving new integrated approaches to city operations and leading to significant investments in areas like wireless communications, sensor networks, data analytics, and cloud computing.

Among the various kinds of technologies, predictive analytics has been gaining popularity among smart city developers. Traditionally used largely for tracking web traffic and formulating marketing strategies for different businesses, it is now making inroads into the infrastructure space. This is primarily because it enables developers and city managers to leverage large amounts of data and make predictions regarding the intelligent usage of available space as well as effective distribution of amenities. Predictive analytics can be used to significantly improve service levels by identifying bottlenecks, triggers and solutions related to the delivery of different utility services within smart cities. Based on the insights gained through large volumes of data, the management can make instant changes to services and daily operations, thereby addressing the concerns of inhabitants on a priority basis. A collective pool of data based on traffic, population, power consumption and other trends can help city planners and managers undertake corrective measures to further improve the living experience of residents through more effective utilisation of resources.

The successful leveraging of predictive analytics depends on the ability of city administrators to collect vast amounts of data by tracking consumption and usage patterns within a city and extrapolating these to identify potential bottlenecks and arrive at suitable solutions. Increasingly, supportive technologies like big data are playing a greater role in ensuring that predictive analytics gains prominence in identifying the challenges associated with service delivery and helps managers take pre-emptive measures to address the concerns of smart city residents.

City developers can leverage predictive analytics in the following areas:

City planning

Data can play a key role in helping developers carve out intelligent spaces by maximising the accessibility of certain areas or services while lowering the risk of overloading important elements of the city’s infrastructure. Predictive analytics helps achieve efficiency as data can be used to map the infrastructure outcomes of different usages of space with a high degree of accuracy.

Security and policing

Globally, predictive analytics is being used in several cities to help predict where crimes are likely to take place. Historical data and geographical data are taken into consideration to identify sensitive and high-crime areas. Cities like London, Los Angeles and Chicago have succeeded in bringing down crime rates by mapping crime-prone areas and are accordingly deploying police officers to crack down on criminals. Developing countries like India can use predictive analytics to create safer cities by collecting valuable data for identifying crime-prone areas and increasing policing in those localities.


Data can also be used to map out peak hour traffic and accident-prone areas. This can be analysed to improve public transport systems by making more vehicles available during rush hours and diverting traffic to alternative routes. Data can be used to create an intelligent public transport network that is flexible and supported by healthy equipment like traffic monitoring control rooms and traffic light infrastructure.


Smart cities and campuses are characterised by uninterrupted broadband connectivity wherein citizens and students can access a host of utility services with the click of a mouse. For the effective use of available resources, it is necessary for city planners to have the ability to shift bandwidth to meet the high speed broadband requirements of particular areas or businesses within smart premises. This can be achieved by using data to track the usage patterns of residents.


Once infrastructure is established, it needs to be upgraded and monitored regularly to ensure sustained growth. Often, utilities like water and electricity are affected by the growing number of residents or businesses. Through predictive analytics, city planners can identify the incremental infrastructure required for meeting the increasing needs of residents. Using modelling and mapping techniques, developers can upgrade and create infrastructure to support crucial amenities within a smart city or premise.

Given the advantages offered by predictive analytics, city planners are expected to build scalable infrastructure based on insights drawn from big data to address critical issues regarding energy, transportation, health care, building, infrastructure, and governance within smart premises.

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