Composite Indicators (CIs), are a common measurement and benchmarking tool that are used to reflect and measure multidimensional concepts such as digital divides, individual's well-being and more. Measurement iterations produce a series of time-oriented data, which stakeholders as well as the general public might be interested to interpret. Visualization of a CI is highly recommended in order to ease interpretation, and many CI websites use radial solutions to visualize CIs. Yet it is unclear how to visualize the temporal dynamics in radial diagrams. Static solutions, mapping time to small multiples might be challenging due to screen space issues. Dynamic solutions are appealing, yet, there is no clear empirical evidence on benefits of dynamic time coding in radial diagrams. In this paper, we compare static vs. dynamic time mapping using two radial CI visualization methods. The popular Radar chart technique is compared to the innovative Flower chart as used in the well-known OECD Better Life index. We compare users' performance and preferences empirically under formal task taxonomy, adjusted to CI tasks. Results indicate that in general, static time encoding was more effective than dynamic encoding. Still, an in depth analysis showed that the dynamic approach is a feasible and sometimes even better solution for important CIs tasks, leveraged by the fact that users seem to like and enjoy it.

A composite indicator (CI) is a measuring and benchmark tool used to capture multi-dimensional concepts, such as Information and Communication Technology (ICT) usage. Individual indicators are selected and combined to reflect a phenomena being measured. Visualization of a composite indicator is recommended as a tool to enable interested stakeholders, as well as the public audience, to better understand the indicator components and evolution over time. However, existing CI visualizations introduce a variety of solutions and there is a lack in CI's visualization guidelines. Radial visualizations are popular among these solutions because of CI's inherent multi-dimensionality. Although in dispute, Radar-charts are often used for CI presentation. However, no empirical evidence on Radar's effectiveness and efficiency for common CI tasks is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares the Radar chart technique with two other radial visualization methods: Flower-charts as used in the well-known OECD Betterlife index, and Circle-charts which could be adopted for this purpose. Examples of these charts in the current context are shown in Figure 1. We evaluated these charts, showing the same data with each of the mentioned techniques applying small multiple views for different dimensions of the data. We compared users' performance and preference empirically under a formal task-taxonomy. Results indicate that the Radar chart was the least effective and least liked, while performance of the two other options were mixed and dependent on the task. Results also showed strong preference of participants toward the Flower chart. Summarizing our results, we provide specific design guidelines for composite indicator visualization. Fig. 1: Three radial solutions for composite indicator visualizations compared empirically for users' performance and preferences.

The next-generation mobile devices include smart watches, wristbands, wearables (e.g., Google Glass), etc. In the future such devices will constitute a large fraction of the total devices available in the market [1]. Latest studies confirm that location-based services are the most requested feature by developers with a market share of 13B in 2013 and have expected exponential growth [2]. Future location-based applications/services will use the data generated by the new mobile devices for providing enhanced user experience. This paper presents a vision of such next-generation location-based services, which we call LBS 2.0. We present the challenges and opportunities that LBS 2.0 will pose for mobile data management.

Abstract For museum curators it is imperative to learn, analyze, and understand the behavior patterns of the visitors in their museum. Recent developments in the field of indoor positioning systems make the acquisition and availability of visitor behavior data more attainable. However, the analysis of such data remains a challenge due to its noisiness, complexity and sheer size. The current paper applies information visualization techniques to analyze this data and make it more accessible to museum curators and personnel. We first provide a detailed description of the application domain including an analysis of the curators' information needs and a description of how a dataset on visitors' spatiotemporal behavior could be acquired. In order to address the curators' needs, we designed a visualization to encode and convey the information based on a newly adjusted visual glyph that we callTangram Diagrams. We thereby focus on the adaptability of the technique to a particular domain, rather than on the novelty aspects of the technique itself. We have evaluated our design decisions empirically, and conducted an expert study to describe the insights gained and the value of the information obtained from the visualization. The contribution of this work is twofold. First, we apply information visualization to the museum domain and discuss how it extends to general indoor spatiotemporal behavior analysis. Second, we show how a visual glyph metaphor can be applied in different ways and contexts to efficiently encode multi‐faceted information.

We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data.

Analyzing the occurrence of stops in transportation systems is an important challenge to better understand traffic congestion problems and find corresponding solutions. We propose an efficient system to analyze stop occurrences. It consists of two major parts: (1) an efficient clustering algorithm to partition the stops into groups based on strongly connected components (2) an interactive visual representation of the results to provide insights to domain experts.

Abstract The widespread adoption of location‐aware devices is resulting in the generation of large amounts of spatiotemporal movement data, collected and stored in digital repositories. This forms a fertile ground for domain experts and scientists to analyze such historical data and discover interesting movement behavioral patterns. Experts in many domains, such as transportation, logistics and retail, are interested in detecting and understanding movement patterns and behavior of objects in relation to each other. Their insights can point to optimization potential and reveal deviations from planned behavior. In this paper, we focus on the detection of the encounter patterns as one possible type in movement behavior. These patterns refer to objects being close to one another in terms of space and time. We define scalability as a core requirement when dealing with historical movement data, in order to allow the domain expert to set parameters of the encounter detection algorithm. Our approach leverages a designated data structure and requires only a single pass over chronological data, thus resulting in highly scalable and fast technique to detect encounters. Consequently, users are able to explore their data by interactively specifying the spatial and temporal windows that define encounters. We evaluate our proposed method as a function of its input parameters and data size. We instantiate the proposed method on urban public transportation data, where we found a large number of encounters. We show that single encounters emerge into higher level patterns that are of particular interest and value to the domain.

The analysis of trajectories has become an important field in geographic visualization, as cheap GPS sensors have become commonplace and, in many cases, valuable information can be derived either from the data themselves or their metadata if processed and visualized in the right way. However, showing the "right" information to highlight dependencies or correlations between different measurements remains a challenge, because the technical intricacies of applying a combination of automatic and visual analysis methods prevents the majority of domain experts from analyzing and exploring the full wealth of their movement data. This paper presents an exploration through enrichment approach, which enables iterative generation of metadata based on exploratory findings and is aimed at enabling domain experts to explore their data beyond traditional means.

Information systems increasingly provide options for visually inspecting data during the process of information discovery and exploration. Little research has dealt so far with user interactions with these systems, and specifically with the effects of characteristics of the displayed data and the user on performance with such systems. The study reports an experiment on users' performance with a visual exploration system. Participants had to identify target graphs within a large set of candidate graphs by using visual filtering criteria that differed in their efficiency for reducing the number of candidate graphs. A pay-off matrix and a time limit served to motivate users to select filter criteria efficiently. Performance was measured as the number of correct identifications of target graphs within a time-limit, and the number, type and position of filter criteria selected for the search. Efficiency was somewhat biased by users' preference to select filter criteria sequentially, starting from the left to the right. Rational and experiential cognitive styles affected performance, and they interacted with learning and the types of filter criteria chosen. The study shows that not only visual search tools can be used effectively but also that data and user characteristics affect task performance with such systems.

Abstract Many origin‐destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio‐temporal data have been developed, but they prove to be only of limited use when applied to temporal origin‐destination datasets. We present Flowstrates, a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin‐destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.

Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.

Advanced visual interfaces, like the ones found in information visualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visual representations and providing interactive tools to explore and navigate, it is possible to get an understanding of the data and possibly discover new knowledge. With the advent of modern data collection and analysis technologies, the direct visualization of data starts to show its limitations due to limited scalability in terms of volumes and to the complexity of required analytical reasoning. Many analytical problems we encounter today require approaches that go beyond pure analytics or pure visualization. Visual analytics provides an answer to this problems by advocating a tight integration between automatic computation and interactive visualization, proposing a more holistic approach. In this paper, we argue for Advanced Visual Analytics Interfaces (AVAIs), visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but where the synergy between automation and visualization is in fact advanced. We offer a detailed argumentation around the needs and challenges of AVAIs and provide several examples of this type of interfaces.

Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of high-dimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.

Abstract Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.

In this article, we present a methodological framework, based on georeferenced house-level socio-demographic and infrastructure data, for investigating minority (or ethnic) group residential pattern dynamics in cities. This methodology, which uses visual analytical tools, is meant to help researchers examine how local land-use configurations shape minorities' residential dynamics and, thereby, affect the level of minority–majority segregation. This methodology responds to the need to refer to the relationship between local land-use configurations and the identity of a building's residents, without simultaneously revealing sensitive house-related details. The research was instantiated on the residential patterns exhibited by the Arab community in Jaffa, Israel. The residential data were collected for over 40 years at four different moments, each associated with the population and housing censuses conducted by Israel's Central Bureau of Statistics and the Ministry of the Interior. Using this methodology enabled us to remain on the level of the individual building when identifying the relationships between spatial land-use configurations and rates of change in ethnic composition and the Arab community's residence pattern dynamics at different geographical scales. It likewise allowed us to identify the qualitative changes in the population's residential preferences during the pattern's development.

Space- and time-referenced data published on the Web by general people can be viewed in a dual way: as independent spatio-temporal events and as trajectories of people in the geographical space. These two views suppose different approaches to the analysis, which can yield different kinds of valuable knowledge about places and about people. We present several analysis methods corresponding to these two views. The methods are suited to the large amounts of the data.

Spatiotemporal analysis of sensor logs is a challenging research field due to three facts: a) traditional two-dimensional maps do not support multiple events to occur at the same spatial location, b) three-dimensional solutions introduce ambiguity and are hard to navigate, and c) map distortions to solve the overlap problem are unfamiliar to most users. This paper introduces a novel approach to represent spatial data changing over time by plotting a number of non-overlapping pixels, close to the sensor positions in a map. Thereby, we encode the amount of time that a subject spent at a particular sensor to the number of plotted pixels. Color is used in a twofold manner; while distinct colors distinguish between sensor nodes in different regions, the colors' intensity is used as an indicator to the temporal property of the subjects' activity. The resulting visualization technique, called Growth Ring Maps, enables users to find similarities and extract patterns of interest in spatiotemporal data by using humans' perceptual abilities. We demonstrate the newly introduced technique on a dataset that shows the behavior of healthy and Alzheimer transgenic, male and female mice. We motivate the new technique by showing that the temporal analysis based on hierarchical clustering and the spatial analysis based on transition matrices only reveal limited results. Results and findings are cross-validated using multidimensional scaling. While the focus of this paper is to apply our visualization for monitoring animal behavior, the technique is also applicable for analyzing data, such as packet tracing, geographic monitoring of sales development, or mobile phone capacity planning.