MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Abstract

Fig. 1. Hourly flows of residential Twitter users in Greater London area over a week are spatially and temporally simplified to gain an overview of mobility dynamics. A calendar view shows the temporal cluster distribution over a week. Each map displays movements between spatial aggregates in a temporal cluster. Movement direction is represented by a color gradient from dark to light blue.Abstract-Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time intervals) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

“…The queries are performed by means of a text search engine. Furthermore, a discrete representation of aggregated movements of flows between places can be treated as a graph, to which graph analysis methods can be applied [32] [49].…”

“…Regions can be defined by means of spatial clustering of neighboring locations [29], possibly, taking into account the strengths of the flows between them [49]. Flow data can also be simplified by grouping and aggregating spatially close OD flows using hierarchical clustering [81].…”

“…Von Landesberger et al [49] present an approach to explore daily and weekly temporal patterns of collective mobility, where the source data are episodic trajectories of people reconstructed from georeferenced tweets or mobile phone use records. The trajectories are aggregated into flows between territory compartments by hourly intervals within the weekly time cycle.…”

Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part II: Impact, Comparison, and Future Developments

“…Nowadays, there are plenty of exploratory studies on how best to model and visualize 2D/3D geospatial information (such as maps, images, and 3D models) on virtual globes [64][65][66]. Visualizing and analyzing geospatial objects in four dimensions (x, y, z, and time), however, is still a relatively new field [4][5][6][7][8][9][10][11][12][13][14][15][67][68][69][70]. With the increased attention being paid to the fourth dimension of the Earth space, and the significant improvements to acquire spatio-temporal data, more and more people are getting interested in the representation and analysis of time-dynamic geospatial objects on virtual globes [71,72].…”

“…In recent years, the importance of time-dynamic geospatial objects and their virtual globe-based applications has been rapidly increasing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. At present, the most favorite schema for describing custom geospatial objects is the Keyhole Markup Language (KML) [16][17][18].…”