It is expected that in 2030 about 5 billion people will live in cities as compared to the 3.6 billion now. This massive growth challenges the liveability of the urban environment. To design cities that offer a pleasant home to its citizens, are attractive to visitors, industry and commercial organisations, and are as well sustainable, it is crucial to understand urban dynamics, and the effects of human behaviour on the city and vice-versa.
Various ideas on how cities work as well as methods to simulate and analyse spatial-temporal and social processes have been developed past decades. The work of, amongst others, Lynch, Hillier, Hägerstrand, and Batty offer basic concepts and methods to represent, simulate, and understand urban systems. Currently cities are increasingly recognised as Complex Adaptive Systems (CAS). Myriad entities, processes, and feedbacks lead to non-linear and often unexpected outcomes complicating the development of urban policies and design. Agent Based Modelling (ABM), Cellular Automata (CA), and participatory modelling offer the tools and bottom-up techniques that can deal with this complexity.
This workshop aims to discuss novel concepts and methods to simulate spatial-temporal dynamics of cities from the bottom-up. Contributions on both fundamental issues as well as applications are welcome. Focus is on models of interactions between humans and the city Examples include, but are not limited to, human movement behaviour, tourism, urban development and expansion, participatory modelling for city development, gentrification and segregation, housing, and urban health.
The specific objectives of this half-day workshop are:
- To discuss state-of-the-art and promising developments in modelling spatial-temporal processes of the city.
- To discuss how simulation of city dynamics contributes to understanding the development of cities.
- To discuss how simulation contributes to the development of policies that better support the design and management of sustainable cities.
- To discuss the desire and required level of realism of ABM given the challenges concerning data requirements and validation.