Stacked Bar Charts Research Articles

Parallel Coordinates Visualization in the ELK Stack

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in re-sources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the Elastic Search storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heat maps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding Parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of Parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata.

“It’s Not as Simple as Just Looking at One Chart”: A Qualitative Study Exploring Clinician’s Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data

Background: Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient’s physical activity patterns. Objective: To explore clinicians’ opinions on different visualisations of actigraphy data. Methods: Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis. Results: Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret. Conclusion: The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

Development and usability testing of Understanding Stroke, a tailored life-sustaining treatment decision support tool for stroke surrogate decision makers

BackgroundSurrogate decision makers of stroke patients are often unprepared to make critical decisions on life-sustaining treatments. We describe the development process and key features for the Understanding Stroke web-based decision support tool.MethodsWe used multiple strategies to develop a patient-centered, tailored decision aid. We began by forming a Patient and Family Advisory Council to provide continuous input to our multidisciplinary team on the development of the tool. Additionally, focus groups consisting of nurses, therapists, social workers, physicians, stroke survivors, and family members reviewed key elements of the tool, including prognostic information, graphical displays, and values clarification exercise. To design the values clarification exercise, we asked focus groups to provide feedback on a list of important activities of daily living. An ordinal prognostic model was developed for ischemic stroke and intracerebral hemorrhage using data taken from the Virtual International Stroke Trials Archive Plus, and incorporated into the tool.ResultsFocus group participants recommended making numeric prognostic information optional due to possible emotional distress. Pie charts were generally favored by participants for graphical presentation of prognostic information, though a horizontal stacked bar chart was also added due to its prevalence in stroke literature. Plain language descriptions of the modified Rankin Scale were created to accompany the prognostic information. A values clarification exercise was developed consisting of a list of 13 situations that may make an individual consider comfort measures only. The final version of the web based tool (which can be viewed on tablets) included the following sections: general introduction to stroke, outcomes (prognostic information and recovery), in-hospital and life-sustaining treatments, decision making and values clarification, post-hospital care, tips for talking to the health care team, and a summary report. Preliminary usability testing received generally favorable feedback.ConclusionWe developed Understanding Stroke, a tailored decision support tool for surrogate decision makers of stroke patients. The tool was well received and will be formally pilot tested in a group of stroke surrogate decision makers.Trial registrationClinicalTrials.gov (NCT03427645).

Arizona Veterinarians' Perceptions and Consensus Regarding Skills, Knowledge, and Attributes of Day OneVeterinary Graduates.

The purpose of this study was to assess Arizona veterinarians' perceptions and consensus regarding the importance of items in the domains of clinical skills, knowledge, and attributes of Day One graduates of veterinary school and to determine demographic predictors for items on which consensus was low. In this survey-based prospective study, respondents were asked to rate the importance of 44 items on a 5-point Likert scale ranging from 1 (not at all important) to 5 (extremely important). Responses were visualized as divergent stacked bar charts and evaluated via summary quantitative and qualitative analyses. Several items had a median score of 5. For clinical skills, items were the ability to formulate a preventive health care plan, the ability to interpret test results, and basic safe handling and restraint of animals; for knowledge, knowledge of pain management and anesthesia; and for attributes, teamwork, problem-solving skills, and client communication skills. The majority of items (80%) had a strong or very strong consensus measure, 18% had a moderate consensus measure, and 2% had a weak consensus measure. Six items (14%) varied by at least one demographic category. We found demographic differences between large and small animal practices in the clinical skill of ability to perform a necropsy, knowledge of large animal theriogenology, and knowledge of canine theriogenology. In conclusion, we found differences in the importance of items and agreement among practitioners, suggesting that critical evaluation of the mapped curriculum, particularly with regard to core curriculum compared with electives and clinical tracks, may benefit students and future employers.

Impact of Competing Treatment Scenarios on Lifetime Disease Burden and Quality Adjusted Life Expectancy (QALE) Among Survivors of High-Risk Childhood Hodgkin Lymphoma (HR-HL): A Modeling Study Based on the St. Jude Lifetime (SJLIFE) Cohort

Background: Children and young adults diagnosed with HR-HL have a 5-year survival rate of >95%. Yet, as compared to the general population, survivors remain at elevated risk for therapy-related early mortality and excess long-term morbidity. Second malignancies (SMNs), cardiac disease, and pulmonary complications are major contributors to excess mortality/morbidity experienced by survivors of HL. Since several treatment regimens used for younger HR-HL all offer excellent overall survival and tolerable short-term morbidity profiles, it is not clear which is best. The objective of this study was to compare the life-long impact of treatment-related toxicity and early mortality associated with 4 common treatment scenarios used to treat HR-HL. Outcomes measured included overall life expectancy (LE), 10-year post-diagnosis QALE and cause of death. Methods: A microsimulation model was developed using the lifetime horizon for a cohort of 10-year survivors of HR-HL treated with 4 common treatment scenarios utilized by pediatric and adult oncology groups. The data from 5522 adult 10-year survivors of childhood cancers in the SJLIFE cohort (56% males; mean age at last follow-up=35.3 years [y], range 18.9-67.9; ≥18y at study enrollment) was used to estimate the mortality risk and incidence of cardiac and pulmonary complications and SMNs as functions of age at primary cancer diagnosis, attained age, sex, race, and treatment exposures including cumulative doses of anthracyclines, alkylating agents, and bleomycin plus radiation dose to the heart and chest. Prospective and retrospective clinical assessments were used to obtain and grade late-effect outcomes using a modified Common Terminology Criteria for Adverse Events. Rates from population-based registries and published systematic analyses were used for background mortality, mortality risk from the complications and for extrapolation. Calibration was performed against SJLIFE data to ensure model consistency. Quality of life utility weights (ranging from 1 for perfect health to 0 for dead) associated with complications were estimated from the SJLIFE cohort. The mortality and incidence functions and utility weights were applied to the lifetime of 10,000 simulated HL survivors (same demographic distribution as in the 667 SJLIFE HL participants: 58% males; mean age at HL diagnosis=13.9y, range 3-25) starting at 10y after HL diagnosis to death to calculate the average LE and QALE associated with the 4 treatment scenarios. Results: The figure shows the results for 4 scenarios based on OEPA [vincristine, etoposide, prednisone, doxorubicin] (2 cycles) plus COPDac [cyclophosphamide, vincristine, prednisone, dacarbazine] (4 cycles) and ABVD [doxorubicin, bleomycin, vincristine, dacarbazine] (6 cycles), both with and without heart and chest radiation. The average LE for the cohort assuming general population mortality risk is 80.8y (dashed line). Average LEs associated with the 4 scenarios are represented by the asterix above each bar. The average 10-year post-diagnosis QALE and the proportion of time spent living with cardiac, pulmonary and/or SMN are depicted in the colored stacked bar-chart. The decrement in average LE due to treatment for HR-HL ranged from 12.8y for OEPA/COPDac alone to 25.8y for ABVD with radiation. OEPA/COPDac based regimens without and with radiation had higher average LE (68.1y and 64.1y, respectively) and QALE (29.9y and 27.4y, respectively) compared to the ABVD regimens (LE: 60.9y and 55.0y; QALE: 25.0y and 22.7y). The addition of radiation reduced average LE and QALE in both the OEPA/COPDac (4.0y and 2.5y) and ABVD (5.8y and 2.3y) regimens and increased the proportion of the cohort who developed pulmonary disease and SMNs for all scenarios. The cohort receiving OEPA/COPDac with radiation had similar average post-10 year QALE compared to those treated with ABVD without radiation. Increased proportional mortality due to SMN was observed among the cohort exposed to ABVD. Conclusion: Using a lifetime horizon simulation approach, we demonstrate that the OEPA/COPDac regimen results in greater average LE and QALE when compared against ABVD for HR-HL. This approach highlights a yet untapped opportunity to leverage data from survivorship cohorts by evaluating late-effect trade-offs to inform clinical trial design and aid patient decision-making when choosing between different treatment options that have similar efficacy. Disclosures No relevant conflicts of interest to declare.

PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

The study area is located in western Pacific Ocean, Mariana Trench. The aim of the data analysis is to analyze the potential influence of how various geological and tectonic factors may affect the geomorphological shape of the Mariana Trench. Statistical analysis of the data set in marine geology and oceanography requires an adequate strategy on big data processing. In this context, current research proposes a combination of the Python-based methodology that couples GIS geospatial data analysis. The Quantum GIS part of the methodology produces an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12,590 bathymetric observation points. The sampling of the geospatial dataset are located across the Mariana Trench. The second part of the methodology consists of statistical data processing by means of high-level programming language Python. Current research uses libraries Pandas, NumPy and SciPy. The data processing also involves the subsampling of two auxiliary masked data frames from the initial large data set that only consists of the target variables: sediment thickness, slope angle degrees and bathymetric observation points across four tectonic plates: Pacific, Philippine, Mariana, and Caroline. Finally, the data were analyzed by several approaches: 1) Kernel Density Estimation (KDE) for analysis of the probability of data distribution; 2) stacked area chart for visualization of the data range across various segments of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing the data distribution by tectonic plates; 5) stacked bar charts for correlation of sediment thickness by profiles, versus distance from the igneous volcanic areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7) scatterplot matrices for correlation analysis between marine geologic variables. The results presented a distinct correlation between the geologic, tectonic and oceanographic variables. Six Python codes are provided in full for repeatability of this research.

Effectiveness of contrast-associated acute kidney injury prevention methods; a systematic review and network meta-analysis

BackgroundDifferent methods to prevent contrast-associated acute kidney injury (CA-AKI) have been proposed in recent years. We performed a mixed treatment comparison to evaluate and rank suggested interventions.MethodsA comprehensive Systematic review and a Bayesian network meta-analysis of randomised controlled trials was completed. Results were tabulated and graphically represented using a network diagram; forest plots and league tables were shown to rank treatments by the surface under the cumulative ranking curve (SUCRA). A stacked bar chart rankogram was generated. We performed main analysis with 200 RCTs and three analyses according to contrast media and high or normal baseline renal profile that includes 173, 112 & 60 RCTs respectively.ResultsWe have included 200 trials with 42,273 patients and 44 interventions. The primary outcome was CI-AKI, defined as ≥25% relative increase or ≥ 0.5 mg/dl increase from baseline creatinine one to 5 days post contrast exposure. The top ranked interventions through different analyses were Allopurinol, Prostaglandin E1 (PGE1) & Oxygen (0.9647, 0.7809 & 0.7527 in the main analysis). Comparatively, reference treatment intravenous hydration was ranked lower but better than Placebo (0.3124 VS 0.2694 in the main analysis).ConclusionMultiple CA-AKI preventive interventions have been tested in RCTs. This network evaluates data for all the explored options. The results suggest that some options (particularly allopurinol, PGE1 & Oxygen) deserve further evaluation in a larger well-designed RCTs.

SRVis: Towards Better Spatial Integration in Ranking Visualization.

Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.

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.

Dairy producers' attitudes toward reproductive management and performance on Canadian dairy farms

The objectives of this study were to explore Canadian dairy producers' attitudes toward reproductive performance and challenges they perceive to be related to reproduction and reproductive management practices. A survey in both English and French was developed, validated, and administered to Canadian dairy farmers between March and May 2014 to collect general farm, reproduction management, and reproductive performance data, as well as opinions and perceptions about different facets of reproduction. Associations between management practices and the perceived importance of reproduction were tested using a logistic regression model. Thematic network analysis was used to identify themes from the open-ended survey questions about challenges concerning reproduction. Finally, questions that were answered on a Likert scale were graphically represented using diverging stacked bar charts. A total of 832 questionnaires were completed online and by mail, which represents approximately 7% of all dairy farms in Canada. Respondents that ranked reproduction in lactating dairy cows as 1 of the 3 most important challenges faced on their farm (66%) were more likely to house their lactating cows in a tiestall and to have a lower herd annual 21-d pregnancy rate. Estrus detection and conception risk were 2 major themes raised and discussed by the respondents. Other concepts, including housing and milk production, were also perceived to affect estrus detection and conception risk. Whereas analysis of open-ended survey questions does not allow for quantification of the importance of different themes in the sample as a whole, it does show that respondents are aware of the multifactorial complexity of reproductive challenges on dairy farms. Improving performance was the main factor influencing decisions concerning reproduction for 80% of the respondents, and they adopted tools and technologies such as synchronization programs and automated activity monitoring systems to improve herd reproductive performance. More research is required to describe how this performance is defined and perceived by the respondents, and how it relates to the actual variability of performance (i.e., pregnancy rate) among farms.

Understanding captive user behavior in the competition between BRT and motorcycle taxis

Transit-dependent users, far from being captives, may have other transportation alternatives. In some developing countries, depending on the characteristics of the city, instead of cycling or walking, captive users opt for informal services to commute. Although they are less safe and possibly less comfortable than regulated BRT services, demand for informal services such as motorcycle taxis has grown in recent years, affecting the self-sustainability of some BRT systems. This research aims to provide a richer explanation of the choice process between the BRT feeder service and motorcycle taxis in the context of the City of Bucaramanga, Colombia. Initially, we analyzed a set of effect indicators by diverging stacked bar charts, and then we used a hybrid discrete choice model to incorporate comfort and safety perceptions, in addition to the tangible attributes traditionally used. We found that safety perception, which varies by gender, age and income, is a key variable in modeling the choice process. Based on an empirical chart analysis and the modeling results, we proposed some policies aimed at changing user choices to stimulate the demand for BRT feeder system.

AssayR: A Simple Mass Spectrometry Software Tool for Targeted Metabolic and Stable Isotope Tracer Analyses.

Metabolic analyses generally fall into two classes: unbiased metabolomic analyses and analyses that are targeted toward specific metabolites. Both techniques have been revolutionized by the advent of mass spectrometers with detectors that afford high mass accuracy and resolution, such as time-of-flights (TOFs) and Orbitraps. One particular area where this technology is key is in the field of metabolic flux analysis because the resolution of these spectrometers allows for discrimination between 13C-containing isotopologues and those containing 15N or other isotopes. While XCMS-based software is freely available for untargeted analysis of mass spectrometric data sets, it does not always identify metabolites of interest in a targeted assay. Furthermore, there is a paucity of vendor-independent software that deals with targeted analyses of metabolites and of isotopologues in particular. Here, we present AssayR, an R package that takes high resolution wide-scan liquid chromatography–mass spectrometry (LC-MS) data sets and tailors peak detection for each metabolite through a simple, iterative user interface. It automatically integrates peak areas for all isotopologues and outputs extracted ion chromatograms (EICs), absolute and relative stacked bar charts for all isotopologues, and a .csv data file. We demonstrate several examples where AssayR provides more accurate and robust quantitation than XCMS, and we propose that tailored peak detection should be the preferred approach for targeted assays. In summary, AssayR provides easy and robust targeted metabolite and stable isotope analyses on wide-scan data sets from high resolution mass spectrometers.

Survival to Diabetes by age, gender and BMI category

ABSTRACT
 AimTo carry out a 10 year survival analysis to provide insight to NHS partners regarding the time to Type -2 Diabetes across the population of ABM HB and by a variety of key criteria including BMI, gender, age and deprivation.
 ApproachSurvival time was taken from the window start date (current data date – 10 years) to patient exit from the study (Death, Type-2 Diabetes diagnosis or window end date). Survival output was generated using the Kaplan-Meier estimate as part of the survival package (Therneau T. M., 2015) in R, with Type-2 Diabetes diagnosis as the event variable with the following control and stratification variables: Gender, Age Group (at window start), Average BMI Category and Deprivation Quintile.
 To identify Diabetic and I particular type-2 patients we developed dictionaries using diagnosis codes from both ICD-10 and READ. These dictionaries were then mapped against the GP, Inpatient and Outpatient datasets to capture a diagnosis of Diabetes as early as possible. The logic to determine whether patients are Diabetic involved the use of 277 READ and 56 ICD-10 codes, incorporating and expanding upon the QOF standard rule-set.
 To identify as many BMIs as possible three methods were used firstly using absolute BMI measurements. Secondly, using READ codes for pre-binned BMI categories. Thirdly, by combining separate height and weight measurements. The latter method in particular introduces data quality problems, both with erroneous values and in cases where different GPs use different units of measurement (e.g. metres, centimetre, feet, stones). To use these measurements in a reliable manner, we developed complex conditional statements to extract valid measurement records and store them uniformly, such that we can use them easily in a variety of data queries to classify patients by weight category.
 ResultsCumulative probability (calculated from inverse ‘survival time’) to diabetes is greater in obese and more elderly. There was not the expected impact of deprivation. Representation of survival curves were difficult for lay people and some other stakeholders to interpret so stacked bar charts of cumulative probabilities were visualised. Stakeholders valued being able to interact with the different aggregated visualisations to display different aspects of the results.The results provided valuable insights for NHS partners and informed predictive models.

Metacoder: An R package for visualization and manipulation of community taxonomic diversity data.

Community-level data, the type generated by an increasing number of metabarcoding studies, is often graphed as stacked bar charts or pie graphs that use color to represent taxa. These graph types do not convey the hierarchical structure of taxonomic classifications and are limited by the use of color for categories. As an alternative, we developed metacoder, an R package for easily parsing, manipulating, and graphing publication-ready plots of hierarchical data. Metacoder includes a dynamic and flexible function that can parse most text-based formats that contain taxonomic classifications, taxon names, taxon identifiers, or sequence identifiers. Metacoder can then subset, sample, and order this parsed data using a set of intuitive functions that take into account the hierarchical nature of the data. Finally, an extremely flexible plotting function enables quantitative representation of up to 4 arbitrary statistics simultaneously in a tree format by mapping statistics to the color and size of tree nodes and edges. Metacoder also allows exploration of barcode primer bias by integrating functions to run digital PCR. Although it has been designed for data from metabarcoding research, metacoder can easily be applied to any data that has a hierarchical component such as gene ontology or geographic location data. Our package complements currently available tools for community analysis and is provided open source with an extensive online user manual.

Portraying an employee performance management system based on multi-criteria decision analysis and visual techniques

Purpose– Performance appraisal is one of the most critical and indispensable human resource practices for organisations. However, it generates dissatisfaction among employees as it is often viewed as complex and ineffective. The purpose of this paper is to present a new performance management system that integrates multi-criteria decision analysis (MCDA) methods – the analytic network process (ANP) and PROMETHEE – with the visual techniques of the GAIA plane and the stacked bar chart. MCDA methods allow a structured and consistent evaluation integrating qualitative and quantitative criteria.Design/methodology/approach– The authors developed a structured and transparent performance management system. It is based on the MCDA methods PROMETHEE and ANP. It also incorporates the visual techniques: GAIA and stacked bar chart. Feedback for trainings and developments can precisely be formulated.Findings– Visual techniques permit clear identification and quantification, for each employee, of the areas that need improvement through training and development, which contributes to the resource-based view of organisations. A real case study has been portrayed to show the added value of the MCDA methods and the visual techniques in employee performance management.Originality/value– The paper describes a new employee performance system adopted in an organisation. The multi-criteria analysis transparently combines qualitative and quantitative decision criteria into a holistic and transparent evaluation. The visual techniques permit us to gain a deep insight into the employees’ skills profile and capture fine details where individuals perform or underperform.

Assessing Inequality Using Percentile Shares

At least since Thomas Piketty's best-selling "Capital in the Twenty-First Century" (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distribution may also be of interest. In this paper I present a new Stata command called -pshare- that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts. A shorter version of this paper has been published in The Stata Journal 16(2): 264-300 (see: http://www.stata-journal.com/article.html?article=st0432).

Assessing Inequality Using Percentile Shares

At least since Thomas Piketty's best-selling Capital in the Twenty-First Century (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distribution may also be of interest. In this article, I present a new command, pshare, that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts.

VisArchive: a time and relevance based visual interface for searching, browsing, and exploring project archives

Abstract Background Project archives are becoming increasingly large and complex. On construction projects in particular, the increasing amount of information and the increasing complexity of its structure make searching and exploring information in the project archive challenging and time-consuming. Methods This research investigates a query-driven approach that represents new forms of contextual information to help users understand the set of documents resulting from queries of construction project archives. Specifically, this research extends query-driven interface research by representing three types of contextual information: (1) the temporal context is represented in the form of a timeline to show when each document was created; (2) the search-relevance context shows exactly which of the entered keywords matched each document; and (3) the usage context shows which project participants have accessed or modified a file. Results We implemented and tested these ideas within a prototype query-driven interface we call VisArchive. VisArchive employs a combination of multi-scale and multi-dimensional timelines, color-coded stacked bar charts, additional supporting visual cues and filters to support searching and exploring historical project archives. The timeline-based interface integrates three interactive timelines as focus + context visualizations. Conclusions The feasibility of using these visual design principles is tested in two types of project archives: searching construction project archives of an educational building project and tracking of software defects in the Mozilla Thunderbird project. These case studies demonstrate the applicability, usefulness and generality of the design principles implemented.

Full-Scale Controlled Comparison of Alternative Information Formats for Stated Choice Exercises

Stated choice experiments are used regularly as part of the evaluation of major transport infrastructure investments, and their design can have an important effect on the accuracy of demand forecasts. Research in the field of behavioral economics has shown that format details can significantly influence how people respond to questions. The increasing ubiquity of the Internet and smartphones has produced rapid changes in survey data collection methods and has implications for stated choice experiment formats. The foregoing trends suggest that there may be opportunities to improve the stated choice formats in common use. However, it will be difficult to improve these formats without systematic, controlled experiments. Four existing publications reporting full-scale controlled comparisons of alternative formats for stated choice exercises were found, and this study presents another such experiment. Timelines are a graphical way of presenting travel time components, similar to a stacked bar chart. The use of timelines was tested as part of an Internet-based stated choice survey of car drivers in Lima, Peru. The questionnaire included a route choice exercise and a mode choice exercise. The respondents were assigned randomly to one of two formats: the timeline format and a text-only format. The timeline and the text-only choice models are similar for both choice exercises. There are indications that the timeline format makes complex choices easier. The time to choose was significantly lower with the timeline format in the mode choice exercise, and the timeline format produced significant constants for the new modes, unlike the text-only format.

Design Implications for the Presentation of Eco-feedback Data

Background : Four decades of eco-feedback studies have produced promising energy conservation results. Up-to-date smart electricity meters allow eco-feedback strategies such as real-time monitoring with appliance-specific data, social comparison, multiple metrics (e.g., cost, CO₂, trees), and personalized feedback. Unfortunately, eco-feedback presentation methods for such strategies have been under-studied. Methods : We have investigated the design of energy chart components (i.e., chart type, time range, time intervals), and supportive eco-feedback information (i.e., metrics, personalized advice). Each component was evaluated by measuring the performance of thirty-five participants (e.g., response time and correctness) and their preferences (e.g., understandability and attractiveness). Result : Whereas most of the energy-monitoring applications show the current day’s energy usage information, it is found that users preferred to see the current week’s energy usage with hourly intervals because this better reveals opportunities for saving energy. Users also preferred a stacked area chart over a stacked bar chart to view the disaggregated individual’s data, and a line chart over a bar chart for social comparison data. The environmental metrics (i.e., CO₂ and tree) confused the users, but cost ($) and KWh were preferred. Conclusions : This study investigates the eco-feedback presentation methods based on survey measures. The next step in this research is to quantify the amount of energy saved using these methods.