## 2 Background Knowledge
### Agent-Based Modelling and Simulation  
Human mobility analysis comes hand in hand with the necessity to generate realistic spatiotemporal trajectories of people's mobility. Concerning the generation of realistic models of human mobility, simulation, specifically agent-based modelling, is probably the most-used in the literature [58]. Agent-based modelling (ABM) is a novel modelling approach when complex systems of independent, interacting agents need to be modelled. ABM can support, through the simulation of an environment's participants' actions and interactions, understanding how one's decisions may affect the whole system. The COVID-19 crisis is also the result of interacting agents. Therefore, apart from studying human mobility, research engineers have extensively used agent-based modelling to understand the COVID-19 crisis and its implications, predict its future outcomes [59; 60; 61; 62], and make decisions for measures to be taken, such as social distancing interventions [63], reducing transmission in facilities [64], and comparing different policies [65]. Agents have behaviours and interact with other agents, changing the agents' behaviour, which influences other agents' behaviour in continuous interaction. By this bottom-up modelling approach, where each agent and each interaction are programmed explicitly in the system through rules, new behaviours, patterns, and structures may emerge that were not introduced into the models but ensued from the agents' interaction [66]. An example of a city simulation where ABM can be used for a what-if analysis to unravel new phenomena and test scenarios is the simulation of urban mobility changes after constructing new infrastructures or when alarming events happen, such as terrorist attacks and epidemics [31; 67]. In another example, the authors in [68] develop an agent-based data-driven model for urban traffic signal timing, while in [69], the authors use agent-based modelling to model and study future urban land-use scenarios. ABM can also be used to model interactions with the environment, apart from among individuals. An agent can be anything from a human being to a road or a building in the ABM universe. This adaptability that ABM offers justifies its increasing use as a powerful technique for decision making in entirely diverse areas. The abundance of special-purpose agent tools further proves the significance and wide adoption of ABM by many researchers. These are developed to address the specific requirements for modelling agents, such as Netlogo, StarLogo, Repast, Swarm, Mason, and Anylogic [70; 71], allowing scientists to focus on the modelling rather than the visualisation of model progress and outcomes. ABM frameworks have also been built in the most popular software programming languages, such as the Mesa framework in Python [72] and the JABM framework in Java [73]. Space and time are fundamental components of ABM and the base for many modern ABM applications. The range of combined ABM and GIS applications at different temporal and spatial scales is indicative of the usefulness of their functional interconnection. In [74], the author briefly reviews the many and diverse application fields of ABM using GIS, ranging from disease modelling to migration and border security studies. As [PERSON] states, 'agent-based simulations serve as artificial laboratories where we can test ideas and hypotheses about phenomena that are not easy to explore in the'real world' [74] (p. 71). He mentions the example of pedestrian modelling of an evacuation, where the building cannot, based on logic and ethics, be set on fire to test how people will respond to this. In this work, the investigation for a missing child is examined through the involvement of citizens. This is also a case where it would be impossible and even unethical to actually test different scenarios with unknown results since this would mean testing it directly on individuals already in dire distress.