Abstract
Abstract. 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.
Highlights
It is well known that graphical representations of especially spatial data communicate better than textual descriptions
Examples are Shneiderman’s (1996): “Visual Information-Seeking mantra” following the Overview first, zoom and filter, details-on-demand approach. Later this strategy has been adapted by Keim et al (2006) as Visual analytics mantra: “Analysis First-Show the Important-Zoom, Filter and Ana- lyse Further-Details on Demand”
Dashboard provides an aggregated summary of data displayed in multiple representations
Summary
It is well known that graphical representations of especially spatial data communicate better than textual descriptions. Examples are Shneiderman’s (1996): “Visual Information-Seeking mantra” following the Overview first, zoom and filter, details-on-demand approach. Later this strategy has been adapted by Keim et al (2006) as Visual analytics mantra: “Analysis First-Show the Important-Zoom, Filter and Ana- lyse Further-Details on Demand”. Both approaches suggest to provide users first the general overview and encourage for further explorations. Dashboard provides an aggregated summary of data displayed in multiple representations It visually identifies trends, patterns and anomalies and encourages the user to drill down to reach the information which is hidden due to aggregation. The user will have the possibility to explore the data based on the three components of spatio-temporal data and choose the data representation method
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