Big data, agents and the city

Big data offers researchers the scope to simulate population behavior through vastly more powerful agent-based models (ABMs), presenting exciting opportunities in the design and appraisal of policies and plans. Agent-based simulations capture system richness by representing micro-level agent choices and their dynamic interactions. They aid analysis of the processes that drive emergent population level phenomena, their change in the future and their response to interventions. The potential of ABMs has led to a major increase in the number of applications, yet such models are limited in that the individual-level data required for robust, reliable calibration are often only available in aggregate form. New (‘big’) sources of data offer a wealth of information about the behavior (e.g., movements, actions, decisions) of individuals. By building ABMs with BD, it is possible to simulate society across many application areas, providing insight into the behavior, interactions and wider social processes that drive urban systems. This chapter will discuss, in context of urban simulation, how big data can unlock the potential of ABMs and how ABMs can leverage real value from big data. In particular, we will focus on how big data can improve an agent’s abstract behavioral representation and suggest how combining these approaches can both reveal new insights into urban simulation and also address some of the most pressing issues in ABM; particularly those of calibration and validation.