## 2 Background Knowledge
### Human Mobility  
Over the last few years, the predominance of the internet and subsequent smartphone technology and positioning methods, such as GPS and WiFi, has enabled human mobility data collection. Therefore, academic research and professionals' interest in analysing these data have been stimulated to understand the main laws that drive people's motion and mine patterns within them to boost the development of location-based applications and services [26]. The study of human mobility became an even 'hotter' topic in 2020 due to the COVID-19 pandemic. Many researchers examined the virus's spread based on people's mobility and social interactions and the impact of social distancing policies and other control measures for the virus containment in mobility [27, 28, 29]. More research datasets have emerged to facilitate this goal. Key players, such as Google, have supported scientific research in the battle against COVID-19 by providing the aggregated, anonymised mobility insights they use in their products, such as Google Maps, to the public [30]. Human mobility has long been used to study epidemics [31, 32, 33, 34, 35], long before the COVID-19 crisis. Analysing and understanding human mobility, however, is vital in many other application domains. Large-scale transportation, mainly served by air and sea, is also studied to better understand global connectivity and migration patterns [36]. On the other side of large-scale mobility, many researchers have attempted to understand the empirical patterns governing pedestrian movement and study emergency evacuation of transport systems, buildings, and public spaces, especially in cases where crowding exists (e.g., religious commemorations, sports events, and festivals) [37]. Applications in that respect may involve many different fields, such as urban planning [38] and simulation [39], public transportation planning [40], and traffic forecasting [41, 42]. Over the years, the results of this analysis have shown that human mobility on a macroscale, such as migration, exhibits structural patterns deriving from geographic and socioeconomic factors and constraints. At an individual's level, human mobility also exhibits strong periodic behaviour while influenced by many factors [43]. Former studies had suggested that human mobility follows the random walk model [26; 44] to compromise the many obscure factors that stimulate human mobility. However, such abstractions usually fail to reproduce realistic models since social and many other influences differentiate people from random walkers [45]. Certain studies have focused on the role the area of residence plays [46; 47], deducing several related parameters, including environmental factors and the effect of population size, the morphology, and the street network's topological structure [43; 48] on the travel distances and mobility patterns. For example, [PERSON] et al. [48] have examined the extent to which two urban morphological features, size and compactness, influence intra-urban human mobility. They concluded that individuals' human travel exponents differ based on the city's morphology, despite all following the exponential law. Several different empirical data types have been used to study both individual and aggregate mobility and help adjust and validate models and their parameters, stemming from census data [49; 50], Call Detail Records (CDRs) [51; 52], taxicabs exploiting tracking and positioning methods [53], and smartphones using social media, such as Foursquare [54], Twitter [55], Gowalla [43], or their GPS receivers [56]. Analysis and exploitation of the raw mobility data are achieved by using interdisciplinary approaches. Techniques and algorithms of machine learning, data mining, and statistical analysis are used for the deciphering of human movement, the extraction of patterns, and the detection of events affecting this movement [26]. Humans are moving daily to earn their living and carry out their social and leisure activities.