{'fraction_non_alphanumeric': 0.013196480938416423, 'fraction_numerical': 0.0, 'mean_word_length': 5.013215859030837, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An interactive web-based visual analytics tool for detecting strategic eye movement patterns

In this paper we describe an interactive and web-based visual analytics tool combining linked visualization techniques and algorithmic approaches for exploring the hierarchical visual scanning behavior of a group of people when solving tasks in a static stimulus. This has the benefit that the recorded eye movement data can be observed in a more structured way with the goal to find patterns in the common scanning behavior of a group of eye tracked people. To reach this goal we first preprocess and aggregate the scanpaths based on formerly defined areas of interest (AOIs) which generates a weighted directed graph. We visually represent the resulting AOI graph as a modified hierarchical graph layout. This can be used to filter and navigate in the eye movement data shown in a separate view overplotted on the stimulus for preserving the mental map and for providing an intuitive view on the semantics of the original stimulus. Several interaction techniques and complementary views with visualizations are implemented. Moreover, due to the web-based nature of the tool, users can upload, share, and explore data with others. To illustrate the usefulness of our concept we apply it to real-world eye movement data from a formerly conducted eye tracking experiment.