Juri Buchmüller

Visual analytics of terrorist activities related to epidemics

The task of the VAST 2011 Grand Challenge was to investigate potential terrorist activities and their relation to the spread of an epidemic. Three different data sets were provided as part of three Mini Challenges (MCs). MC 1 was about analyzing geo-tagged microblogging (Twitter) messages to characterize the spread of an epidemic. MC 2 required analyzing threats to a computer network using a situational awareness approach. In MC 3 possible criminal and terrorist activities were to be analyzed based on a collection of news articles. To solve the Grand Challenge, insight from each of the individual MCs had to be integrated appropriately.

Integrated Spatial Uncertainty Visualization using Off-screen Aggregation

Visualization of spatial data uncertainties is crucial to the data understanding and exploration process. Scientific measurements, numerical simulations, and user generated content are error prone sources that gravely influence data reliability. When exploring large spatial datasets, we face two main challenges: data and uncertainty are two different sets which need to be integrated into one visualization, and we often lose the contextual overview when zooming or filtering to see details. In this paper, we present an extrinsic uncertainty visualization as well as an off-screen technique which integrates the uncertainty representation and enables the user to perceive data context and topology in the analysis process. We show the applicability and usefulness of our approach in a use case.

Visual analytics for exploring local impact of air traffic

The environmental and noise impact of airports often causes extensive political discussion which in some cases even lead to transnational tensions. Analyzing local approach and departure patterns around an airport is difficult since it depends on a variety of complex variables like weather, local and general regulations and many more. Yet, understanding these movements and the expected amount of flights during arrival and departure is of great interest to both casual and expert users, as planes have a higher impact on the areas beneath during these phases. We present a Visual Analytics framework that enables users to develop an understanding of local flight behavior through visual exploration of historical data and interactive manipulation of prediction models with direct feedback, as well as a classification quality visualization using a random noise metaphor. We showcase our approach using real world data from the Zurich International Airport region, where aircraft noise has led to an ongoing conflict between Germany and Switzerland. The use cases, findings and expert feedback demonstrate how our approach helps in understanding the situation and to substantiate the otherwise often subjective discourse on the topic.

SpaceCuts: making room for visualizations on maps

Visual map features like streets, rail tracks, or rivers do not provide enough space to visualize multiple attributes on them. Related approaches to solve space issues distort the map with lenses, apply distortion techniques to the map geometry, or employ three dimensional visualizations. All these techniques come at the cost of distortion or overlapping of relevant map features or they even produce overlap of visualized data. In this paper, we present SpaceCuts, a technique to generate additional space for data visualization on maps that does not distort the map and introduces only minimal overlap by cutting the map along a geographic structure and pulling the resulting areas apart. Besides introducing the basic technique, we discuss possible interactions, further extensions, application scenarios, and outline potential future research.

Using visual analytics to provide situation awareness for movement and communication data

Analyzing and correlating movement and communication data is challenging. To gather insights and gain knowledge out of such datasets, we propose a visual analytics system. We apply automated clustering techniques and propose a combination of various visualizations to provide overviews. We support the analyst in exploring the data to eventually enhance situational awareness in complex analysis scenarios. To evaluate our approach, we apply our techniques in the context of the VAST 2015 Grand Challenge (GC). Within this challenge, we successfully identify suspicious patterns and interesting groups of distinctive behavior among visitors of an amusement park, and correlate them with their respective communication patterns to gain insights.

Visual analytics for inspecting the evolution of a graph over time: Pattern discovery in a communication network

We present two approaches developed to visually analyze and find patterns in a communication network. The work was done for the VAST 2015 Mini-Challenge 2 (MC2), featuring a dataset with records of timestamps as well as identification of sender and receiver of text messages. Further information included the location from which a message was sent in the fictional amusement park. In the first approach, we present the data preprocessing pipeline we used for a custom visualization. In the second approach, we present how we used available data preprocessing and visualization software to get a quick and clear overview of the problem, and how we used the generated findings to feed our custom visualizations.

Using visual analytics to analyze movement and action patterns

This paper describes our approach and tools used to solve this years VAST 2015 Mini-Challange 1 (MC1). It also explains how we came up with the idea for a new tool created for solving of the challenge and how this tool helps the user analyzing the data. Further, preprocessing steps will be discussed and how new features were created to be able to cluster guests in goups walking together. Finally findings of this visualization will be presented.

Applying visual analytics to explore and analyze movement data

The VAST Challenge 2015 movement dataset is mirroring current challenges in the analysis of large spatiotemporal datasets. We present a tool featuring different exploratory approaches analyze and visualize spatiotemporal data to build and confirm hypotheses. Our tool helps the user to find patterns, anomalies and groups in a data set that can not be processed manually. We present custom visualizations to solve the tasks stated by the VAST 2015 Mini-Challenge (MC1).