Page 31 - Shaping smarter and more sustainable cities - Striving for sustainable development goals

involve the creation of multiple infrastructures (as discussed above), as well as strengthening the
motivation for government participation, the application of technology, and the integration of
various smart infrastructure management systems combined with citizen collaboration.
This integration can be achieved through ICTs, with ICT tools acting as the “glue” between the
different physical infrastructures. For example, ICT could be used as the key medium to disseminate
information on the locations of electric vehicle charging stations in order to optimize traffic flows
and energy usage of electric vehicles.

ICTs also enable the following functions, which are keys to achieving the goals and maximizing the
performance of SSCs:

 ICT‐enabled information and knowledge sharing: Traditionally due to inefficiency on sharing of
information, a city may not be ready to solve a problem even if it is well equipped to respond.
With immediate and accurate information, cities can gain an insight on the problem and take
action before it escalates.

 ICT‐enabled forecasts: Preparing for stressors like natural disasters requires a considerable
amount of data dedicated to study patterns, identify trends, recognize risk areas, and predict
potential problems. ICT provides and manages this information more efficiently, so that the city
can improve its preparedness and response capability.
 ICT‐enabled integration: Access to timely and relevant information (e.g. ICT‐based early warning
systems) need to be ensured in order to better understand the city's vulnerabilities and
strengths.

Together with this concept of integration of all the individual services, urban stakeholders can
implement, optimize and make the city a smarter and better place to live in.

b. Data prediction

According to Gartner, Predictive analytics describes any approach to data mining with four primary
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attributes :
1) An emphasis on prediction (rather than description, classification or clustering).
2) Rapid analysis measured in hours or days (rather than the stereotypical months of
traditional data mining).

3) An emphasis on the business relevance of the resulting insights.
4) An emphasis on ease of use, thus making the tools accessible to business users.
Predictive analysis essentially applies modern statistical techniques of modelling, machine learning,
data mining facts (current and historical) to make predictions about future events. Predictive
analytics has become an essential tool in business modelling. Such models exploit historical and
transactional data to develop a better understanding of behavioural patterns and use them for
business purposes, for example, credit scoring techniques.
Such tools can now be applied to large datasets (i.e. Big Data) in order to improve or enhance the
city's development. For example, constant data sharing would be able to provide immediate
warning for any fragile water pipelines to relevant government departments before it bursts, mobile
applications that predict which traffic routes to avoid or use, or predict which trains will be fully
occupied at a given time and modelling people flows or workflows with real‐time feedback loops.

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33 http://www.gartner.com/it‐glossary/predictive‐analytics/

ITU‐T's Technical Reports and Specifications 21