Page 11 - U4SSC: City Science Application Framework

Urban problems in these categories create enormous opportunities for the application of city
science.

2.2. Data

Cities today have a large number of data sources. Digital transformation and ICT initiatives in the
landscape of emerging technologies have proliferated data creation and consumption in cities. Edge
devices, IoT, city information systems, legacy systems, among others, generate a huge amount of
data. In this context, data acts as a strategic asset for cities through which they can generate new
insights, create new services and resolve various urban challenges.

City science utilizes city data as inputs, or raw material, to solve urban challenges. The potential of
data is enormous when transformed into beneficial insight and action by the city.

2.3. Scientific Techniques and Methods

City science uses scientific techniques and other data-driven methods to solve urban challenges.
The dynamic and complex nature of cities allows mathematical models and techniques to be
applied; that is, urban problems can be modelled and recast as scientific models and problems.

The complex behaviours and relationships within and between different city constituents form
various flows and networks. Modelling such phenomena is conducive to application of sophisticated
analysis and problem-solving techniques such as optimization, stochastic and deterministic models,
simulation, bottom-up evolutionary models, graphs and networks, mathematical programming
techniques (e.g. linear programming, dynamic programming, etc.).

City Science Application Framework 3