Generative Computational Urban Planning through Big Data Analysis

For the near future, forecasts predict an uncontrolled growth of urbanization in the world, in which cities are fragmented and uneven systems in relation to fast evolving environmental, economic, and social phenomena. The traditional urban planning approach, essentially theoretical-predictive, adapts poorly to face future challenges. Hence, the need to rethink how to govern the transformations of cities, which can be described by models of urban metabolism. The city sensing has changed the way cities are explored and used. With the transition from digitalization to datafication, through the computational approach, georeferenced big data can be analysed and exploited by algorithms. They originate a generative computational urban planning process, which can achieve a higher quality of the project and provide cities with adaptive capability. This process exploits data provided by public administrations, companies, and citizens who take part in an inclusive and adaptive urban planning.