This article explores how the ability to recall information in data visualizations depends on the presentation technology. Participants viewed 10 Isotype visualizations on a 2D screen, in 3D, in Virtual Reality (VR) and in Mixed Reality (MR). To provide a fair comparison between the three 3D conditions, we used LIDAR to capture the details of the physical rooms, and used this information to create our textured 3D models. For all environments, we measured the number of visualizations recalled and their order (2D) or spatial location (3D, VR, MR). We also measured the number of syntactic and semantic features recalled. Results of our study show increased recall and greater richness of data understanding in the MR condition. Not only did participants recall more visualizations and ordinal/spatial positions in MR, but they also remembered more details about graph axes and data mappings, and more information about the shape of the data. We discuss how differences in the spatial and kinesthetic cues provided in these different environments could contribute to these results, and reasons why we did not observe comparable performance in the 3D and VR conditions.

Much of the visualization literature focuses on assessment of visual representations with regard to their effectiveness for understanding data. In the present work, we instead focus on making data visualization experiences more enjoyable, to foster deeper engagement with data. We investigate two strategies to make visualization experiences more enjoyable and engaging: personalization, and immersion. We selected pictographs (composed of multiple data glyphs) as this representation affords creative freedom, allowing people to craft symbolic or whimsical shapes of personal significance to represent data. We present the results of a qualitative study with 12 participants crafting pictographs using a large pen-enabled device and while immersed within a VR environment. Our results indicate that personalization and immersion both have positive impact on making visualizations more enjoyable experiences.

Pictographic representations and animation techniques are commonly incorporated into narrative visualizations such as data videos. General belief is that these techniques may enhance the viewer experience, thus appealing to a broad audience and enticing the viewer to consume the entire video. However, no study has formally assessed the effect of these techniques on data insight communication and viewer engagement. In this paper, we first propose a scale-based questionnaire covering five factors of viewer engagement we identified from multiple application domains such as game design and marketing. We then validate this questionnaire through a crowdsourcing study on Amazon's Mechanical Turk to assess the effect of animation and pictographs in data videos. Our results reveal that each technique has an effect on viewer engagement, impacting different factors. In addition, insights from these studies lead to design considerations for authoring engaging data videos.

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

Data videos, motion graphics that incorporate visualizations about facts, are increasingly gaining popularity as a means of telling stories with data. However, very little is systematically recorded about (a) what elements are featured in data videos and (b) the processes used to create them. In this article, we provide initial insights to build this knowledge. We first report on a qualitative analysis of 50 professionally designed data videos, extracting and exposing their most salient constituents. Second, we report on a series of workshops with experienced storytellers from cinematography, graphics design and screenplay writing. We provided them with a set of data facts and visualizations and observed them create storyboards for data videos. From these exploratory studies, we derive broader implications for the design of an authoring tool to enable a wide audience to create data videos. Our findings highlight the importance of providing a flexible tool supporting a non-linear creation process and allowing users to iteratively go back to different phases of the process.

GPS, RFID, and other technologies have made it increasingly common to track the positions of people and objects over time as they move through two-dimensional spaces. Visualizing such spatio-temporal movement data is challenging because each person or object involves three variables (two spatial variables as a function of the time variable), and simply plotting the data on a 2D geographic map can result in overplotting and occlusion that hides details. This also makes it difficult to understand correlations between space and time. Software such as GeoTime can display such data with a three-dimensional visualization, where the 3rd dimension is used for time. This allows for the disambiguation of spatially overlapping trajectories, and in theory, should make the data clearer. However, previous experimental comparisons of 2D and 3D visualizations have so far found little advantage in 3D visualizations, possibly due to the increased complexity of navigating and understanding a 3D view. We present a new controlled experimental comparison of 2D and 3D visualizations, involving commonly performed tasks that have not been tested before, and find advantages in 3D visualizations for more complex tasks. In particular, we tease out the effects of various basic interactions and find that the 2D view relies significantly on "scrubbing" the timeline, whereas the 3D view relies mainly on 3D camera navigation. Our work helps to improve understanding of 2D and 3D visualizations of spatio-temporal data, particularly with respect to interactivity.

Crowd sourced crisis mapping is a relatively new phenomenon and platform that enables the collection and visualization of real-time crisis data submitted by users through social media tools and cellular technologies. Crisis maps are generally used by both state and non-state actors for sense-making and as a reference point for action. The current crisis map visualizations only show the location from which documents such as reports or short messages have been generated. Such a limited representation fails to immediately show important content, such as themes from a document and their changes over time. As a result, sense-making becomes time-consuming and cognitively demanding. We present a new visualization approach, Geo-Temporal Tag Visualization (GTViz), which treats the tags on the crowd sourced reports as spatio-temporal textual datasets. GTViz embeds a map to represent the spatial properties of the dataset and compactly shows the changes of key terms from the data source over time. We demonstrate the value of GTViz through two case studies and a controlled user study.

In this paper, we analyze possible security attacks to the personal WSN carried on the patient’s body and its close vicinity. We assume that WSN is implemented using 802.15.4 beacon enabled technology with a secure sensing, location and power management blocks based on the ZigBee specification and built on top of 802.15.4 link layer. We present networking and security issues which can be used as a basis for security attacks.

In this paper, we analyze performance of the 802.15.4 cluster in beacon enabled mode under the presence of key exchange protocol. We assume that all nodes are applying power management technique based on the constant event sensing reliability required by the coordinator. Power management generates random sleep times by every node which in average fairly distributes the sensing load among the nodes. Key exchange is initiated by cluster coordinator after some given number of sensing packets have been received by the coordinator. We develop analytical model of key exchange integrated into the cluster's sensing function and evaluate the impact of frequency of key exchange on the cluster's energy consumption.

The IEEE 802.15.4 specification is a recent low data rate wireless personal area network standard. While basic security services are provided for, there is a lack of more advanced techniques which are indispensable in modern personal area network applications. In addition, performance implications of those services are not known. In this paper, we describe a secure data exchange protocol based on the ZigBee specification and built on top of 802.15.4 link layer. This protocol includes a key exchange mechanism. Then, we evaluate the overhead of this scheme under different application scenarios. Initial results show the range of network and traffic parameters wherein the proposed scheme is feasible to use