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.

Geo-located graph drawings often suffer from map visualization problems, such as overplotting of nodes as well as edges and location of parts of the graph being outside of the screen. One cause of these problems is often an irregular distribution of nodes on the map. Zooming and panning do not solve the problems, as they either only show the overview of the whole graph or only the details of a part of the graph. We present an interactive graph drawing technique that overcomes these problems without affecting the overall geographical structure of the graph. First, we introduce a method that uses insets to visualize details of small or remote areas. Second, to prevent the subgraphs within insets from overplotting and edge crossing, we introduce a local area re-arrangement. Moreover, insets are automatically drawn/hidden and repositioned in accordance with the user's navigation. We test our technique on real-world geo-located graph data and show the effectiveness of our approach for showing overview and details at the same time. Additionally, we report on expert feedback concerning our approach

Geo-located networks are analyzed in various domains such as supply chain management. When simulating supply chain processes or when testing geo-visualization techniques, synthetic test datasets are needed. However, real world data are difficult to obtain and artificial data are cumbersome to create manually. In this paper, we present an interactive visual tree network generator that not only generates a network, but also attaches geo-locations to its nodes. We designed a modular rulebased system to control the generation process. A user can interactively use rules to parametrize the data generation process. The user can visually explore and adjust results intermediately after each generation iteration

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