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5 Use Cases

5.1 Investigating Daily Routine in Trip Making Behaviour

One purpose of FlowSampler is to experiment with alternative approaches to identify the centres of sub regions in territories based on urban flows. The significance of analysing geolocated social media data is that the information extracted may give an alternative image of how functional urban areas are shaped in comparison to existing techniques that mainly analyse home to work commuting information obtained through census data [4].

Using 734,494 geolocated tweets collected from 2,786 twitter users in Belgium over the duration of a year, we generate a flow map consisting of trajectories belonging to 2,194 users. We omit 592 users because of insufficient tweeting activity. Figure 5a. provides a visual summary of the trips that have occurred over the year. From the map, we identify four distinct clusters that reveal a polycentric distribution of movements. To obtain a map that illustrates routine trip making, we remove the flow pathways that exhibit spontaneous characteristics by filtering flows that fall into the lower percentile of the trajectory selection widget and flows that fall into the upper percentile of the headcount selection widget (fig. 5b). The resulting map characterises the routine trip making behaviour of Twitter users in Belgium. To verify the regularity of the remaining flow pathways, we inspect the frequency of these trips with the time selection widget. Through this process, we discovered that majority of the routine movements take place around local communities typically in towns and villages. Yet, we also observe routine intercity travel across contiguous urban areas between three major Belgian cities (fig. 5c).

5.2 Investigating Exceptional Trip Making Behaviour

To demonstrate the scalability of FlowSampler, we describe an orthogonal use case investigating exceptional, short-term transit behaviour that took place over the touristic season in Italy. The dataset we analyse consist of 13,953,814 tweets generated by 344,660 twitter users over a period of three months. For this study, we are specifically interested in identifying movements that converge on, and take place within, Cilento, a national park in southern Italy. From this data, we construct trajectories beloinging to 78,477 twitter users. We omit 266,183 users due to insufficient tweeting activity. The map in figure 6a. illustrates that majority of movement towards Cilento originate from three major Italian cities. We refine the spatial granularity of the map to obtain a more precise boundary over the park and exclude twitter users who were in fact travelling to nearby cities. This reduces the analysis to 1,214 trajectories that transit Cilento. Figure 6b. presents a micro view of the park showing a concentration of twitter users along the coastline. This reveals the extent by which the coastal regions are perceived as privileged destinations in contrast to the inland regions. Visually inspecting the

Fig. 5. (A) Flow map aggregated trajectories that have occurred over the course of a year in Belgium. We identify four distinct clusters that show a polycentric distribution of movement.

(B) Flow pathways that exhibit spontaneous characteristics are removed from the analysis. (C) Flow pathways that represent the routine trip making behaviour of Twitter users in Belgium. A particularly striking feature in this map is the connection between three major Belgian cities.

Fig. 6. (A) The arcs highlighted in red indicate that majority of the flow pathways heading towards Cilento Park originate from Milan, Rome and Naples. (B) Micro view of Cilento Park showing a concentration of activity near the coastland.

Fig. 7. Comparing the connectivity of coastal regions to inland regions. We observe that flows occurring in the inland regions are limited to adjacent localities while regions along the coastland are better connected. We tint unrelated flow pathways in a lighter shade to improve the legibility of the image.

incoming and outgoing edges of each node reveals an asymmetry in trip making behaviour. Whereas coastal regions appear well connected to other locations, trip making within the inland regions of Cilento tend to be limited to adjacent localities (fig. 7). This finding corresponds to the availability of transport infrastructure as well as to how the coastline is marketed as a key touristic attraction. Filtering the time selection widget further reveals an “inland, coastal, inland” travel pattern between two disjoint inland regions showing that the settlements along the coastline serve as important hubs for transit between locations.

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