Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio-temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.
Nowadays large amounts of movement data is available. This makes it important not only to be aware of how to collect and store this data, but also how to visually represent the information to get insights and "read" the story behind data. When visualising origin-destination data, the traditional flow map is the solution most often selected. A single flow map, however, does not necessarily show all the available attribute variables and also tends too clutter quickly.A more appropriate solution is a dashboard. It provides users with summaries of the represented information. Despite the dashboard suitability to support getting insights, current dashboards have some limitations regarding the flexibility of the layout. To overcome these limitations, we introduce adaptability in dashboards. In our case adaptability ensures that users get insights into the component of interest (space, time, or attribute) on 3 levels of detail. Adaptability is initiated by user tasks to resulting in changes in the visualizations of represented information and dashboard interfaces. We illustrate the concept of an adaptable dashboard with two case studies.
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