The design space of construction tools for information visualization: A survey

Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.

Section: Design Phasementioning

confidence: 99%

Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff

Walny

Frisson

West

et al. 2020

“…Design evaluation investigates the visual encodings of scatterplots in particular scenarios and provides empirical guidance on many facets, such as size and color of points [18,66,77], aspect ratio [20,33,76], selection of dimensions [9,68], amenities [14], and interaction [49,55] and animation [12,57]. Furthermore, many quality metrics have been developed for automatic design decision [8,43,45]. Quality metrics, such as those for clutter reduction [48] and cluster detection [35,68,79], measure the ability of diagrams to perform certain analytical tasks.…”

Section: Visual Encodings Of Scatterplotmentioning

confidence: 99%

Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Wei

Mei

Zhao

et al. 2020

Self Cite

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

“…Besides the smooth exploration experimence, flexible visual representation methods are also required [40,46,64]. For example, a simple equi-width binning cannot guarantee desired charts that can exhibit a particular pattern due to skewed data distribution.…”

Section: Scenariosmentioning

confidence: 99%

RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query

Mei

Chen

Wei

et al. 2020

Self Cite

Fig. 1. RSATree facilitates fast answering of aggregate queries in large-scale tabular datasets while allowing flexible binning strategies. (a) A case built on a social network check-in dataset with 4.5 million records. A brushing and linking operation is performed by brushing workdays (Monday -Friday) and 13 hours of each day (9am -9pm) for filtering. (b) Binned scatterplot created from an airline on-time performance dataset with 180 million records. The bin width can be freely modified and instant previews are shown. (c) Capability of RSATree to generate a histogram with varied bin widths by using the same dataset as (b). As shown on the left side of (c), the distribution of "LateAircraftDelay" is relatively unbalanced. Application of log-scale binning produces a recognizable histogram (right side). Conventional approaches cannot simultaneously achieve low response time and flexible binning strategy in the three cases.Abstract-Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.