Chart Types Research Articles

Chart Mining: A Survey of Methods for Automated Chart Analysis.

Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.

Memorability of Enhanced Informational Graphics

Design enhancements are often added to charts, signage, and infographics to help garner attention or communicate a message. Though it is argued that they may also detract user’s focus on the underlying information, prior studies have contributed evidence that visual design enhancements and even simple decorations can improve memory of visual displays. However, there is limited empirical knowledge about how the type of aesthetic enhancements infuences memory, and what informational and data elements are remembered from a visualization. We conducted a user study testing chart types (line, pie, and bar), the presence of color, and whether added enhancements were contextually related to the data topic presented in each chart. We found that enhancements relevant to the data helped in the recall of title and thematic elements, but enhancements did not signifcantly affect recall of specifc data values. This suggests that using relevant enhancements can have a positive effect on memorability of some chart content, but only if the design styles are chosen well to match the information topic. Recall of chart topics for unrelated embellishments was worse than plain, un-enhanced charts, which suggests that visual enhancement can distract or interfere with memorability if the viewer does not understand a meaningful connection between informational topic and design modifcations.

An empirical evaluation of three-dimensional pie charts with individually extruded sectors in a geovisualization context

This study compares participants’ performance in extracting information from three-dimensional pie charts with individually extruded sectors in a single map frame against two-dimensional pie and bar charts in adjacent map frames. Specifically, we examine the response accuracy and response times of 181 adults who were asked to (1) identify the highest magnitude, (2) estimate a proportion, (3) perform both at the same time, and (4) solve a map-related task using the two chart setups. For each task, charts were shown on backgrounds with increasing visual complexity: a blank, a borders-only, and a choropleth map. Furthermore, we tested whether participants’ performance improved through additional practice with the two chart types. We did not observe any differences in participants’ aggregated response accuracy or response times between the tested three-dimensional and two-dimensional chart types for the (1) highest magnitude task and (2) proportion task. However, participants solved the (3) combination task with two-dimensional pie and bar charts on a blank background more accurately and were faster in fulfilling the (4) spatial task with three-dimensional pie charts. The first difference, however, leveled for participants who gained more practice and who accomplished the combination task on maps with higher visual complexity, whereas the second difference persisted even for more trained subjects.

Usefulness of the Bristol Stool Form Chart scoring system for the laboratory processing of faecal samples in suspected Clostridioides difficile cases

Clostridioides difficile infection (CDI) remains a threat to hospitalized patients. All patients with diarrhoea [defined as Bristol Stool Form Chart (BSFC) types 5-7] not attributable to another cause should be tested for CDI. Samples should quarter fill and take the shape of the specimen container. Use of the BSFC definition of diarrhoea to determine which samples should be tested for CDI in the laboratory was assessed. In this study, two-thirds of glutamate-dehydrogenase-positive, toxin-positive samples were rated BSFC type <5. Therefore, the BSFC should not be used by laboratories to determine which samples are tested for CDI.

Expressive Authoring of Node-Link Diagrams With Graphies.

Expressive design environments enable visualization designers not only to specify chart types and visual mappings, but also to customize individual graphical marks, as they would in a vector graphics drawing tool. Prior work has mainly investigated how to support the expressive design of a wide range of charts generated from tabular data: bar charts, scatterplots, maps, etc. We focus here on an expressive design environment for node-link diagrams generated from multivariate networks. Such data structures raise specific challenges and opportunities in terms of visual design and interactive authoring. We discuss those specificities and describe the user-centered design process that led to Graphies, a prototype environment for expressive node-link diagram authoring. We then report on a study in which participants successfully reproduced several expressive designs, and created their own designs as well.

Large-scale empirical optimisation of statistical control charts to detect clinically relevant increases in surgical site infection rates

ObjectiveSurgical site infections (SSIs) are common costly hospital-acquired conditions. While statistical process control (SPC) use in healthcare has increased, limited rigorous empirical research compares and optimises these methods for SSI...

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT.

MotivationData visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.ResultsWe have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.Availability and implementationOur browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease.Supplementary information Supplementary data are available at Bioinformatics online.

The efficacy of stacked bar charts in supporting single-attribute and overall-attribute comparisons

Stacked bar charts are a visualization method for presenting multiple attributes of data, and many visualization tools support these charts. To assess the efficacy of stacked bar charts in supporting attribute-comparison tasks, we conducted a user study to compare three types of stacked bar charts: classical, inverting, and diverging. Each chart type was used to visualize six attributes of data where half of the attributes have the characteristics of ‘lower better’ whereas the other half attributes are with ‘higher better.’ Thirty participants were asked to perform two types of comparison tasks: single-attribute and overall-attribute comparisons. We measured the completion time, error rate, and perceived difficulty of the comparison tasks. The results of the study suggest that, for overall-attribute comparisons, the inverting stacked bar chart was the most effective with regards to the completion time. The results also show that performing overall-attribute comparisons using the classical and diverging stacked bar charts required more time than performing single-attribute comparisons using these charts. Participants perceived the inverting and diverging stacked bar charts as easier-to-use than the classical stacked bar chart for overall-attribute comparisons. However, for single-attribute comparisons, all chart types delivered similar performance. We discuss how these findings can inform the better design of interactive stacked bar charts and visualization tools.

Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches.

We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14-1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary.

Chart decoder: Generating textual and numeric information from chart images automatically

Charts are commonly used as a graphical representation for visualizing numerical data in digital documents. For many legacy charts or scientific charts, however, underlying data is not available, which hinders the process of redesigning more effective visualizations and further analysis of charts. In response, we present Chart Decoder, a system that implements decoding of visual features and recovers data from chart images. Chart Decoder takes a chart image as input and generates the textual and numeric information of that chart image as output through applying deep learning, computer vision and text recognition techniques. We train a deep learning based classifier to identify chart types of five categories (bar chart, pie chart, line chart, scatter plot and radar chart), which achieves a classification accuracy over 99%. We also complement a textual information extraction pipeline which detects text regions in a chart, recognizes text content and distinguishes their roles. For generating textual and graphical information, we implement automated data recovery from bar charts, one of the most popular chart types. To evaluate the effectiveness of our algorithms, we evaluate our system on two corpora: 1) bar charts collected from the web, 2) charts randomly made by a script. The results demonstrate that our system is able to recover data from bar charts with a high rate of accuracy.

ECharts: A declarative framework for rapid construction of web-based visualization

While there have been a dozen of authoring systems and programming toolkits for visual design and development, users who do not have programming skills, such as data analysts or interface designers, still may feel cumbersome to efficiently implement a web-based visualization.In this paper, we present ECharts, an open-sourced, web-based, cross-platform framework that supports the rapid construction of interactive visualization. The motivation is driven by three goals: easy-to-use, rich built-in interactions, and high performance. The kernel of ECharts is a suite of declarative visual design language that customizes built-in chart types. The underlying streaming architecture, together with a high-performance graphics renderer based on HTML5 canvas, enables the high expandability and performance of ECharts. We report the design, implementation, and applications of ECharts with a diverse variety of examples. We compare the utility and performance of ECharts with C3.js, HighCharts, and Chart.js. Results of the experiments demonstrate the efficiency and scalability of our framework. Since the first release in June 2013, ECharts has iterated 63 versions, and attracted over 22,000 star counts and over 1700 related projects in the GitHub. ECharts is regarded as a leading visualization development tool in the world, and ranks the third in the GitHub visualization tab.

A Study of the Effect of Doughnut Chart Parameters on Proportion Estimation Accuracy

AbstractPie and doughnut charts nicely convey the part–whole relationship and they have become the most recognizable chart types for representing proportions in business and data statistics. Many experiments have been carried out to study human perception of the pie chart, while the corresponding aspects of the doughnut chart have seldom been tested, even though the doughnut chart and the pie chart share several similarities. In this paper, we report on a series of experiments in which we explored the effect of a few fundamental design parameters of doughnut charts, and additional visual cues, on the accuracy of such charts for proportion estimates. Since mobile devices are becoming the primary devices for casual reading, we performed all our experiments on such device. Moreover, the screen size of mobile devices is limited and it is therefore important to know how such size constraint affects the proportion accuracy. For this reason, in our first experiment we tested the chart size and we found that it has no significant effect on proportion accuracy. In our second experiment, we focused on the effect of the doughnut chart inner radius and we found that the proportion accuracy is insensitive to the inner radius, except the case of the thinnest doughnut chart. In the third experiment, we studied the effect of visual cues and found that marking the centre of the doughnut chart or adding tick marks at 25% intervals improves the proportion accuracy. Based on the results of the three experiments, we discuss the design of doughnut charts and offer suggestions for improving the accuracy of proportion estimates.

A Nursing Pain Assessment and Record Information System: Design and Application in the Oncology Department.

Pain is an unpleasant sensory and emotional feeling accompanying existing, impending, or potential tissue damage. Valid pain assessment and standardized pain documentation are important in oncology pain management; however, they are still deficient. Thus, we developed a pain assessment and record information system for nurses in the oncology department and implemented a questionnaire survey to evaluate users' acceptance of the system. The pain assessment system focused on usability and efficiency to provide a modified workflow that was safe, less time-consuming, patient centered, enjoyable, and efficient. The pain assessment and record chart types in the system enabled greater standardization of pain assessments and records. The application of the system greatly improved the efficiency of nursing in the oncology department, guiding nurses in an accurate and comprehensive patient pain assessment and contributing significantly to further improvement in pain care standards and care decisions. Nurses and doctors surveyed reported a high degree of satisfaction with factors such as saving time and improving the capacity of pain control, suggesting that the system enhanced the quality of pain management. Through this system, we can promote pain management, improving care quality for patients.

Know When to Test: Optimizing Diagnostic Practices for Clostridium difficile Infection (CDI) Among Patients at a Tertiary-Care Cancer Center

BackgroundPolymerase chain reaction (PCR) based diagnostic testing for the detection of toxigenic Clostridium difficile infection (CDI) does not distinguish between carriers and patients with true CDI. As a result, CDI is over-diagnosed in hospitalized patients with diarrhea. Unnecessary testing generates false positives and several downstream sequelae.Aim: The aim of this study was to reduce unnecessary testing for CDI through an electronic alert, targeted education, and implementation of evidence-based laboratory testing policy.MethodsIn order to quickly identify laxative induced diarrhea and avoid CDI testing, an electronic alert was created in the electronic medical record (EMR) system. The alert was built on a logic that identified patients who had received laxatives within 48 hours of a CDI PCR test order. The alert additionally provided the rationale for avoiding testing in patients on laxatives and guidance on appropriate testing for CDI. The following steps were taken simultaneously to complement these efforts: 1) Infection Control conducted hospital-wide education for licensed independent practitioners on a CDI testing algorithm 2) Laboratory based policy was instated to reject all formed stools (Bristol Stool Chart types 1 through 4) for CDI testing.ResultsIn the 6 month pre-intervention period, there were 29 CDI tests per 1000 patient-days. In the post-intervention period, CDI tests decreased to 19 per 1000 patient-days, a 35% decrease (P < 0.0001). The decline in testing has been sustained for 7 months. The following observations were also made: 1) HAI rate reduction of 28%; 2) decrease in oral vancomycin use.ConclusionDecreasing inappropriate testing has several distinct advantages, including reducing excessive and unnecessary antibiotic use, avoiding misclassification of carriers as CDI cases, normalizing healthcare-associated CDI rates, and diminishing healthcare costs associated with preventable tests. Laboratories that use PCR only testing for CDI diagnosis should follow stringent policies to ensure that only patients with high pretest probability are tested. EMR systems are a useful and effective resource to achieve this for patients with laxative induced diarrhea.Disclosures All authors: No reported disclosures.

Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations.

Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations "in the wild" often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.

Digitizing of Analogue Data-Charts from Thermohygrographs

A thermo-hygrograph is a chart recorder that measures and records both temperature and humidity in an analogue form. Converting the results into digital form is necessary to enhance evaluation of measurements archived on paper charts. Coordinate system of the charts is different than common Cartesian rectangular axis. Temperature and relative humidity axis are curved according to recorder pen holder ray. Different chart types, depending on particular thermohygrograph model, were used so far. In every case, however, the axis grid depicted in the background is geometrically precisely defined. So pattern recognition techniques could be used to find its location automatically. Temperature and relative humidity patterns can be retrieved automatically assuming that the color group of plotted lines is different than the background including the grid. Due to storing conditions, manual descriptions, discoloration and other factors, the plotted pattern cannot be unambiguously identified. Many randomly scattered pixels are interpreted as being part of a plotted line. Among additional measures, like excluding particular areas from analysis, manually removing and including points, a vector analysis of retrieved courses makes digitizing more effective.

Reverse‐Engineering Visualizations: Recovering Visual Encodings from Chart Images

AbstractWe investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end‐to‐end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role (e.g., chart title, x‐axis label, y‐axis title, etc.), and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types.

Converting Basic D3 Charts into Reusable Style Templates

We present a technique for converting a basic D3 chart into a reusable style template. Then, given a new data source we can apply the style template to generate a chart that depicts the new data, but in the style of the template. To construct the style template we first deconstruct the input D3 chart to recover its underlying structure: the data, the marks and the mappings that describe how the marks encode the data. We then rank the perceptual effectiveness of the deconstructed mappings. To apply the resulting style template to a new data source we first obtain importance ranks for each new data field. We then adjust the template mappings to depict the source data by matching the most important data fields to the most perceptually effective mappings. We show how the style templates can be applied to source data in the form of either a data table or another D3 chart. While our implementation focuses on generating templates for basic chart types (e.g., variants of bar charts, line charts, dot plots, scatterplots, etc.), these are the most commonly used chart types today. Users can easily find such basic D3 charts on the Web, turn them into templates, and immediately see how their own data would look in the visual style (e.g., colors, shapes, fonts, etc.) of the templates. We demonstrate the effectiveness of our approach by applying a diverse set of style templates to a variety of source datasets.

Graphical Perception of Value Distributions: An Evaluation of Non-Expert Viewers' Data Literacy

An ability to understand the outputs of data analysis is a key characteristic of data literacy and the inclusion of data visualisations is ubiquitous in the output of modern data analysis. Several aspects still remain unresolved, however, on the question of choosing data visualisations that lead viewers to an optimal interpretation of data. This is especially true when audiences have differing degrees of data literacy. In this paper we describe two user studies on perception from data visualisations, in which we measured the ability of participants to validate statements about the distributions of data samples visualised using different chart types. In the first user study, we find that histograms are the most suitable chart type for illustrating the distribution of values for a variable. We contrast our findings with previous research in the field, and posit three main issues identified from the study. Most notably, however, we show that viewers struggle to identify scenarios in which a chart simply does not contain enough information to validate a statement about the data that it represents. In the follow-up study, we ask viewers questions about quantification of frequencies, and identification of most frequent values from different types of histograms and density traces showing one or two distributions of values. This study reveals that viewers do better with histograms when they need to quantify the values displayed in a chart. Among the different types of histograms, interspersing the bars of two distributions in a histogram leads to the most accurate perception. Even though interspersing bars makes them thinner, the advantage of having both distributions clearly visible pays off. The findings of these user studies provide insight to assist designers in creating optimal charts that enable comparison of distributions, and emphasise the importance of using an understanding of the limits of viewers’ data literacy to design charts effectively.

Application Of Underwater Lamp For Bagan Tancap At Lekok

Baganis one type of fishing gear that uses light to collect fish. The lights are expected to stimulate the fish to come and stay around the light and than arrested with net. The use of light for fishing is growing rapidly, almost all fishing gear use light as a tool for collecting fish. Application of this technology aims to increase fish catch fishermen in District Lekok through the application of technology underwater lamp with LED. Underwater lamp applied to the bagantancapwith a certain intensity and color to find the intensity of light and color that give the best catches.Best catches in the light of 450 lux is capable of delivering the highest total catch of 254 kg, it is supported by the values of illumination of 1.5 lux at a distance of 5 m. Types of fish that were caught on as much as 23 step chart types where the type of Stolephorus spp, Selaroides spp, Leiognathus spp, Loligo sp, Rastrelliger spp is a type of fish that always caught each fishing operation Keywords: lamp, bottom, tools, bagan, Lekok