data visualization bachelor thesis

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Data Analysis & Visualization Master’s Theses and Capstone Projects

Dissertations/theses/capstones from 2024 2024.

The Charge Forward: An Assessment of Electric Vehicle Charging Infrastructure in New York City , Christopher S. Cali

Visualizing a Life, Uprooted: An Interactive, Web-Map and Scroll-Driven Exploration of the Oral History of my Great-Grandfather – from Ottoman Cilicia to Lebanon and Beyond , Alyssa Campbell

Examining the Health Risks of Particulate Matter 2.5 in New York City: How it Affects Marginalized Groups and the Steps Needed to Reduce Air Pollution , Freddy Castro

Clustering of Patients with Heart Disease , Mukadder Cinar

Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors , Livia Clarete

Invisible Hand of Socioeconomic Factors in Rising Trend of Maternal Mortality Rates in the U.S. , Disha Kanada

Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model , Ethan Lo

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth , William Mahoney Luckman

Making Sense of Making Parole in New York , Alexandra McGlinchy

Employment Outcomes in Higher Education , Yunxia Wei

Dissertations/Theses/Capstones from 2023 2023

Phantom Shootings , Allan Ambris

Naming Venus: An Exploration of Goddesses, Heroines, and Famous Women , Kavya Beheraj

Social Impacts of Robotics on the Labor and Employment Market , Kelvin Espinal

Fighting the Invisibility of Domestic Violence , Yesenny Fernandez

Navigating Through World’s Military Spending Data with Scroll-Event Driven Visualization , Hong Beom Hur

Evocative Visualization of Void and Fluidity , Tomiko Karino

Analyzing Relationships with Machine Learning , Oscar Ko

Analyzing ‘Fight the Power’ Part 1: Music and Longevity Across Evolving Marketing Eras , Shokolatte Tachikawa

Stand-up Comedy Visualized , Berna Yenidogan

Dissertations/Theses/Capstones from 2022 2022

El Ritmo del Westside: Exploring the Musical Landscape of San Antonio’s Historic Westside , Valeria Alderete

A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics , Alexander Avdeev

A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women , Meriem Benarbia

Disrepair, Displacement and Distress: Finding Housing Stories Through Data Visualizations , Jennifer Cheng

Blockchain: Key Principles , Nadezda Chikurova

Data for Power: A Visual Tool for Organizing Unions , Shay Culpepper

Happiness From a Different Perspective , Suparna Das

Happiness and Policy Implications: A Sociological View , Sarah M. Kahl

Heating Fire Incidents in New York City , Merissa K. Lissade

NYC vs. Covid-19: The Human and Financial Resources Deployed to Fight the Most Expensive Health Emergency in History in NYC during the Year 2020 , Elmer A. Maldonado Ramirez

Slices of the Big Apple: A Visual Explanation and Analysis of the New York City Budget , Joanne Ramadani

The Value of NFTs , Angelina Tham

Air Pollution, Climate Change, and Our Health , Kathia Vargas Feliz

Peru's Fishmeal Industry: Its Societal and Environmental Impact , Angel Vizurraga

Why, New York City? Gauging the Quality of Life Through the Thoughts of Tweeters , Sheryl Williams

Dissertations/Theses/Capstones from 2021 2021

Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology , Quinn Bolewicki

Remaking Cinema: Black Hollywood Films, Filmmakers, and Finances , Kiana A. Carrington

Detecting Stance on Covid-19 Vaccine in a Polarized Media , Rodica Ceslov

Dota 2 Hero Selection Analysis , Zhan Gong

An Analysis of Machine Learning Techniques for Economic Recession Prediction , Sheridan Kamal

Black Women in Romance , Vianny C. Lugo Aracena

The Public Innovations Explorer: A Geo-Spatial & Linked-Data Visualization Platform For Publicly Funded Innovation Research In The United States , Seth Schimmel

Making Space for Unquantifiable Data: Hand-drawn Data Visualization , Eva Sibinga

Who Pays? New York State Political Donor Matching with Machine Learning , Annalisa Wilde

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• Teaching with Data

Data Visualization for Interdisciplinary Thesis

Teaching with data: data visualization for interdisciplinary thesis.

• Data Sources
• Teaching Resources
• Finding Trends in Environmental Data
• Test Hypotheses about Political Scandals
• International Economic Data
• Organic Search Keyword Analysis
• Neighborhood Wiki
• Time Series Data Analysis Portfolio
• Company Inventory Data Analysis
• Evaluate Hypotheses using R-Studio and Excel
• Use Data to Inform Policy Decisions
• Challenge Conventional Wisdom
• Use Social Media Data to Inform Organizational Strategy

Faculty Author: Virginia Kuhn

Course: IML 440: Interdisciplinary Thesis

Department or School: Media Arts and Practice, School of Cinematic Arts

Student Population: Undergraduate seniors

Duration: semester

Deliverables:

• Project plan with chosen data set
• Data set visualized in 3 ways
• Written report describing the visualizations

Keywords: comparative data visualization, thesis, visual literacy, images, infographics

Summary:   Students identify a data set that serves their senior thesis project’s main research question, visualize the data in 3 ways (e.g. pie chart word cloud, scatter plot, bar graph), and write a 750-­‐1,000-­‐word report describing how each visualization changes the meaning of the information based on the way it looks.

Assignment Goals:   The information visualization assignment asks you to explore the ways in which data become information and, further, the ways in which information shifts its meaning depending on its context and presentation. This assignment helps students to do the following:  

• move from research into production
• critically decode and encode digital, image-­‐based media
• acquire data literacy, as well as visual literacy

For full instructions, see:  https://virginiakuhn.net/

Recommended Tools:

• LA City Open Data
• Periodic Table of Visualization types
• Plot.ly (beta freeware tool for analysis and visualization)
• Text mining tools overview
• Wordl word clouds
• Information is Beautiful
• Info Aesthetics
• Visual Complexity
• NodeXl (works with Excel)
• Visualizing economics
• Flowing Data
• Tableau Public

Faculty Author Advice:   Allow more time for students to cull data between project plan and when it’s due; about a week more should be spent on this part. The perception is that data is very easy to get at and that it is cleaned up in the way you want it. For example, a student couldn’t find data on her topic and needed to create a data set. It was hard for students to get the data they needed from sources like the New York Times from the 1960s because they weren’t searchable. There are numerous cases like that for web scraping for APIs. That was the best lesson and that’s why the data literacy aspect was so key and led them to think about who decides what a data point is, and what is data, really.

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• URL: https://libguides.usc.edu/teachingwithdata

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BS in Applied Data Analytics and Visualization (STEM)

The BS in Applied Data Analytics and Visualization will prepare you to transform data into valued insight for a variety of decision makers. You will learn techniques to set-up systems to retrieve, aggregate, and process large data sets; separate big data sets into manageable and logical components; and eliminate “noise” by cleaning data. You also will learn different methods of data analysis and visualization, aided by statistical and graphics software.

Applied Skills

Gain broad-based skills in information systems, quantitative and qualitative analysis, and data visualization and be able apply that expertise to translate data into information.

Faculty Contact

Dr. Bri Newland, Assistant Dean, Division of Applied Undergraduate Studies (212) 998-7201 [email protected]

STEM Designation

The STEM (Science, Technology, Engineering, Math) designation is assigned to programs that have quantitative/technical focus and enable students to participate in a longer optional practical training post-graduation.

Program Structure

Core courses.

Core courses provide an in-depth exploration of the liberal arts that expands your critical thinking and analytical skills, increases your knowledge, and develops your intellect.

Students select elective credits in consultation with their adviser.

Internships

Earn academic credit while gaining industry experience. Work with coaches at the Wasserman Center to learn how to land an internship that will let you put what you have learned in the classroom into action.

Work in close consultation with a faculty advisor on a senior thesis or project in your field of study.

Core Requirements

The degree is a 128 credit program consisting of a required set of core courses (32 credits), liberal arts electives (16 credits), required foundation courses (32 credits), data science and visualization courses (32 credits), data science and visualization electives (12 credits), and a graduation project (4 credits).

The following courses may be required based on a writing placement assessment, and should be successfully completed within the first three semesters.

• EXWR1-UC7501 Introduction to Creative and Expository Writing 2
• EXWR1-UC7502 Writing Workshop I 4
• EXWR1-UC7503 Writing Workshop II 4

Critical Thinking

Students are required to take the following course.

• HUMN1-UC6401 Critical Thinking 4

Quantitative Reasoning

Students, in close consultation with their advisor, select Math 1 and Math II or one of the following other courses based on a math placement assessment.

• MATH1-UC1101 Math I 2
• MATH1-UC1141 Math II 2
• MATH1-UC1105 Mathematical Reasoning 4
• MATH1-UC1171 Precalculus 4
• MATH1-UC1174 Calculus W/Applications to Business & Economics 4

Scientific Issues

Students select one of the following courses in consultation with their advisor.

• SCNC1-UC2001 Human Biology 4
• SCNC1-UC3203 Environmental Sustainability 4
• SCNC1-UC3207 Stars, Planets, & Life 4
• SCNC1-UC3215 Biology of Hunger & Population 4

Historical Perspectives

• HIST1-UC5804 Renaissance to Revolutn 4
• HIST1-UC5820 The American Experience 4
• HIST1-UC5821 Classical & Medieval World 4
• HIST1-UC5822 Contemporary World 4

Global Perspectives

• ANTH1-UC5011 World Cultures: Africa 4
• ANTH1-UC5012 World Cultures: Middle East 4
• ANTH1-UC5013 World Cultures: Asia 4
• ANTH1-UC5014 World Cultures: Latin America & The Caribbean 4

Literary and Artistic Expressions

• ARTS1-UC5438 History of Music 4
• ARTH1-UC5443 Visual Expressions in Society 4
• LITR1-UC6201 Contemporary Global Literature 4
• LITR1-UC6209 Oral Traditions in Literature 4

Liberal Arts Electives

In addition to the core curriculum requirements, bachelor of science degree students select an additional 16 credits of liberal arts courses in consultation with their advisor.

Foundation: Quantitative Courses

Students are required to take all of the following courses.

• MATH1-UC1172 Statistical Methods 4
• MATH1-UC1180 Linear algebra 4

Foundation: Information Systems Courses

• ISMM1-UC0746 Fundamentals of Computing 4
• ISMM1-UC0702 Database Design 4
• ISMM1-UC0752 Systems Analysis 4

Data Science and Visualization Courses

• ADAV1-UC1000 Applied Data Analytics I 4
• ADAV1-UC1001 Applied Data Analytics II 4
• ISMM1-UC0742 Business Intelligence 4
• ISMM1-UC0731 Introduction to Cloud Computing 4
• MKAN1-UC5100 Cultural and Legal Implications of Digital Technology 4
• ADAV1-UC1005 Data Visualization 4
• ADAV1-UC1010 Designing Data: Infographics 4
• ADAV1-UC1015 Visual Analytics 4

Electives in Applied Data Analytics and Visualization

In consultation with their advisor and based on their special areas of interest, students select an additional 16 units from the marketing, information technology, and digital media courses from McGhee's Bachelor of Science programs. Courses chosen may include Web Analytics, Marketing Analytics, Information Security Management, and Web Architecture.

• ADAV1-UC7990 Special Topics in Applied Data Analytics and Visualization 2-4

Graduation Project

Students select one of the following courses.

• ADAV1-UC7991 Senior Project: Seminar 4
• ADAV1-UC7992 Senior Project: Internship 4
• ADAV1-UC7993 Independent Study 4

APPLICATION DEADLINES

Visit the Admissions Deadlines page to view the application deadlines.

Admissions Criteria

The NYU SPS Admissions team carefully weighs each component of your application during the admissions review process to evaluate your ability to benefit from and contribute to the dynamic learning environment and the challenging curriculum that the NYU School of Professional Studies offers.

CONTACT ADMISSIONS

The NYU SPS Admissions team is here to help you navigate the admissions process and ensure that all of your questions and/or concerns are addressed. Call or email to set up a Zoom or Skype appointment.

212-998-7100 •  [email protected]

Financing Your Education

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Data Visualization

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On this page: Major Description | Requirements | Learning Objectives | Faculty & Staff | Courses

Students in the Data Visualization major will enter the workforce with the ability to identify trends, provide insights, and illustrate the societal impacts of different forms of data while critically engaging with data visualization software and programming languages. Data Visualization emphasizes fundamental elements of database management, data analysis and visualization, information systems, and quantitative and qualitative analysis.

Offering both a Bachelor of Arts and a Bachelor of Science in Data Visualization beginning Autumn 2024 Quarter. Completed the prerequisite courses? Current UW Bothell Pre-Major students can submit a declaration form, follow this link to the  IAS Major Declaration Form .

Students who are passionate about using data to solve real-world problems as well as communicating across disciplines to data-based solutions.

Students will gain high demand skills in data analysis, visualization, and representation through the Data Visualization major.

Graduates will find careers as a data analyst, in data science, with GIS (Geographic Information Systems) and continue on in graduate programs in Data.

DEGREE OPTIONS

Bachelor of art.

A student with a Bachelor of Arts (BA) in Data Visualization may enter the variety of academic and career fields focused on data analysis and visualization, including statistics, visual analytics, and geographic information systems and sciences. A Bachelor of Arts in Data Visualization emphasizes fundamental elements of database management, data analysis and visualization, information systems, and quantitative and qualitative analysis.

BACHELOR OF SCIENCE

A student with a Bachelor of Science (BS) in Data Visualization will enter into the wide variety of academic and career fields focused on data analysis and visualization, including statistics, visual analytics, and geographic information systems and sciences. A Bachelor of Science in Data Visualization degree emphasizes fundamental elements of data science, visualization, and analytics, advanced research, advanced graduate programs, or other data programing and geographic information systems and sciences programs and careers.

Learning objectives

The Data Visualization Curriculum advances the five core IAS learning objectives . Students taking courses and/or majoring in Data Visualization:

• Acquire critical competence in different ways to address real-world, quantitative concerns and to find solutions that are both efficient and equitable.
• Learn to apply statistical and mathematical tools and critique their applications, including building and evaluating arguments based on quantitative data.
• Generate reliable data and choose appropriate methods to apply to a given data set.
• Gain experience creating visual representations of problems and data, and communicate these ideas, results, and analyses in multiple formats.
• Learn to work in interdisciplinary teams to communicate and to understand a range of issues, especially those around social and planetary justice, that have quantitative underpinnings.
• Synthesize quantitative research with other ways of knowing.

Recommended preparation

Interested in exploring this major but not ready to commit? Consider taking one of the below courses! Any of these selections will help familiarize you with the academic program and prepare you for advanced coursework in the major.

• BIS 111/CSS 101 Digital Thinking
• BIS 115 Digital Cultures
• BIS 140 Numbers in News Media
• BIS 215 Understanding Statistics
• BIS 232 Introduction to Data Visualization
• B DATA 200 Introduction to Data Studies
• CSS 107 Introduction to Programming through Animated Storytelling
• CSS 112 Introduction to Programming for Scientific Applications

Degree prerequisites

Bachelor of Arts Prerequisites

• B WRIT 133 or B WRIT 134 or ENGL 131 or equivalent Composition Course
• B WRIT 135 or ENGL 141 or equivalent Composition course
• BIS 215 Understanding Statistics or B MATH 215 Health Statistics or B BUS 215 Business Statistics or STAT 220 Statistical Reasoning or equivalent introduction to statistics course
• B MATH 123 Precalculus II or MATH 120 Precalculus or minimum score of 400 on the MTHDSP

Bachelor of Science Prerequisites

• STMATH 124 Calculus 1 or MATH 124 Calculus 1
• CSS 142 Computer Programming I or CSE 142 Computer Programming I or CSE 122 Intro to Computer Programming II
• CSS 143 Computer Programming II or CSE 143 Computer Programming II or CSE 123 Intro to Computer Programming III

Degree requirements

Bachelor of arts degree requirements.

• B DATA 200 Introduction to Data Studies (5 credits)
• BIS 232 Introduction to Data Visualization (5 credits)
• Either BIS 218 Power of Maps OR BIS 342 Geographic Information Systems (5 credits)
• Either BES 301 Science Methods and Practice OR BST 301 Scientific Writing (5 credits)
• Advanced Data Visualization and Analysis Methods (15 Credits)
• Spatial Data Analysis (15 credits)
• Data Visualization Electives (25 credits)

Bachelor of Arts- 75 credits total

Bachelor of Science Degree Requirements

• Either STMATH 125 or MATH 125 Calculus 2 (5 credits)
• Either STMATH 126 or MATH 126 Calculus 3 (5 credits)
• Either BIS 231 Linear Algebra or STMATH 208 Matrix Algebra (5 credits)

Bachelor of Science- 90 credits total

School of IAS Requirements and Policies

• Residency Requirement: 30 credits must be completed in residency at UW Bothell
• Cumulative GPA Requirement: Major GPA must be at a cumulative of 2.00 or higher
• Interdisciplinary Practice & Reflection (IPR): The IPR requirement can be completed through elective credits or it can overlap with major coursework. Please see the IPR website for course options
• Upper Division Credit Policy: Of the credits applying to the Data Visualization major requirements, a minimum of 45 credits must be completed at the Upper Division (300-400) level
• Joe Ferrare Faculty Coordinator
• Baska Anderson
• Carrie Bodle
• Shauna Carlisle
• Charlie Collins
• Colin Danby
• Cinnamon Hillyard
• Jin-Kyu Jung
• Santiago Lopez
• Sara Maxwell
• Rebecca Price
• Caleb Trujillo

Academic Advisor

Research librarian.

• Alyssa Berger

A. Core Courses

• Either BIS 231 Linear Algebra or STMATH 208 Matrix Algebra (5 credits).

B. Advanced Data Visualization and Analysis Methods Courses (15 credits)

• BIS 411 Network Analysis & Visualization (5 credits)
• BIS 412 Advanced Data Visualization (5 credits)
• BIS 447 Topics in Quantitative Inquiry (5 credits)
• BISMCS 473 Visual Communication (5 credits)
• B BUS 301 Data Management (5 credits)

C. Spatial Data Analysis Courses (15 credits)

• BIS 343 Geovisualization (5 credits)
• BIS 344 Intermediate Geographic Information Systems (5 credits)
• BIS 352 Mapping Communities (5 credits)
• BIS 442 Advanced Geographic Information Systems (5 credits)
• BES 303 Environmental Monitoring Practicum (5 credits)
• BES 440 Remote Sensing of the Environment (5 credits)

D. Data Visualization Elective Courses (25 credits)

• BIS 111/CSS 101 Digital Thinking (5 credits)
• BIS 115 Digital Cultures (5 credits)
• BIS 140 Numbers in News Media (5 credits)
• BIS 218 Power of Maps (5 credits)
• BIS 235 Critical Media Literacy (5 credits)
• BIS 236 Introduction to Interactive Media (5 credits)
• BIS 331 Journal and Media History (5 credits)
• BIS 312 Approaches to Social Research Methods (5 credits)
• BIS 332 Digital Global Industries (5 credits)
• BIS 340 Approaches to Cultural Research Methods (5 credits)
• BIS 342 Geographic Information Systems (5 credits)
• BIS 372 Representation, Colonialism, and the Tropical World (5 credits)
• BIS 380 Bioethics (5 credits)
• BIS 382 the Visual Art of Biology (5 credits)
• BIS 406 Urban Planning and Geography (5 credits)
• BIS 410 Topics in Qualitative Inquiry (5 credits)
• BIS 421 Technology Policy (5 credits)
• BEARTH 201 Mapping the Earth System (5 credits)
• BEARTH 202 Modeling Global Systems (5 credits)
• BISIA 244 Time-Based Media Art
• BISIA 250 Photography as Art
• BISIA 344 Video Art (5 credits)
• BISIA 350 Photography and Digital Art (5 credits)
• BISIA 444 Video Installation Art (5 credits)
• BISIA 450 Image and Imagination (5 credits)
• BISLEP 302 Policy Analysis (5 credits)
• BISSTS 307 Science, Technology, and Society (5 credits)
• BISSTS 355 History of Science and Technology (5 credits)
• CSS 211 Computers and Society
• CSS 342 Data Structures, Algorithms, and Discrete Mathematics I
• CSS 343 Data Structures, Algorithms, and Discrete Mathematics II
• CSS 385 Introduction to Game Development
• CSS 411 Computing Technology and Policy
• STMATH 300 Foundations of Modern Math
• STMATH 341 Introduction to Statistical Inference
• B IMD 233 Fundamentals of Web Media Technology (5 credits)
• B IMD 250 Introduction to Interaction Design (5 credits)
• In addition to the Bachelor of Science electives listed above, Bachelor of Arts students can take:
• BIS 231 Linear Algebra (5 credits)
• CSS 110 Intro to Cybersecurity
• CSS 112 Introduction to Programming for Scientific Application
• CSS 123 Programming for Data Science
• CSS 142 Computer Programming I
• CSS 143 Computer Programming II
• B MATH 144 Calculus for the Life Sciences
• STMATH 124 Calculus I
• STMATH 125 Calculus II
• STMATH 126 Calculus III
• STMATH 208 Matrix Algebra

Looking to fulfill a requirement?

How do I make a data analysis for my bachelor, master or PhD thesis?

A data analysis is an evaluation of formal data to gain knowledge for the bachelor’s, master’s or doctoral thesis. The aim is to identify patterns in the data, i.e. regularities, irregularities or at least anomalies.

Data can come in many forms, from numbers to the extensive descriptions of objects. As a rule, this data is always in numerical form such as time series or numerical sequences or statistics of all kinds. However, statistics are already processed data.

Data analysis requires some creativity because the solution is usually not obvious. After all, no one has conducted an analysis like this before, or at least you haven't found anything about it in the literature.

The results of a data analysis are answers to initial questions and detailed questions. The answers are numbers and graphics and the interpretation of these numbers and graphics.

What are the advantages of data analysis compared to other methods?

• Numbers are universal
• The data is tangible.
• There are algorithms for calculations and it is easier than a text evaluation.
• The addressees quickly understand the results.
• You can really do magic and impress the addressees.
• It’s easier to visualize the results.

What are the disadvantages of data analysis?

• Garbage in, garbage out. If the quality of the data is poor, it’s impossible to obtain reliable results.
• The dependency in data retrieval can be quite annoying. Here are some tips for attracting participants for a survey.
• You have to know or learn methods or find someone who can help you.
• Mistakes can be devastating.
• Missing substance can be detected quickly.
• Pictures say more than a thousand words. Therefore, if you can’t fill the pages with words, at least throw in graphics. However, usually only the words count.

Under what conditions can or should I conduct a data analysis?

• If I have to.
• You must be able to get the right data.
• If I can perform the calculations myself or at least understand, explain and repeat the calculated evaluations of others.
• You want a clear personal contribution right from the start.

How do I create the evaluation design for the data analysis?

The most important thing is to ask the right questions, enough questions and also clearly formulated questions. Here are some techniques for asking the right questions:

Good formulation: What is the relationship between Alpha and Beta?

Poor formulation: How are Alpha and Beta related?

Now it’s time for the methods for the calculation. There are dozens of statistical methods, but as always, most calculations can be done with only a handful of statistical methods.

• Which detailed questions can be formulated as the research question?
• What data is available? In what format? How is the data prepared?
• Which key figures allow statements?
• What methods are available to calculate such indicators? Do my details match? By type (scales), by size (number of records).
• Do I not need to have a lot of data for a data analysis?

It depends on the media, the questions and the methods I want to use.

A fixed rule is that I need at least 30 data sets for a statistical analysis in order to be able to make representative statements about the population. So statistically it doesn't matter if I have 30 or 30 million records. That's why statistics were invented...

What mistakes do I need to watch out for?

• Don't do the analysis at the last minute.
• Formulate questions and hypotheses for evaluation BEFORE data collection!
• Stay persistent, keep going.
• Leave the results for a while then revise them.
• You have to combine theory and the state of research with your results.
• You must have the time under control

Which tools can I use?

You can use programs of all kinds for calculations. But asking questions is your most powerful aide.

Who can legally help me with a data analysis?

The great intellectual challenge is to develop the research design, to obtain the data and to interpret the results in the end.

Am I allowed to let others perform the calculations?

That's a thing. In the end, every program is useful. If someone else is operating a program, then they can simply be seen as an extension of the program. But this is a comfortable view... Of course, it’s better if you do your own calculations.

A good compromise is to find some help, do a practical calculation then follow the calculation steps meticulously so next time you can do the math yourself. Basically, this functions as a permitted training. One can then justify each step of the calculation in the defense.

What's the best place to start?

Clearly with the detailed questions and hypotheses. These two guide the entire data analysis. So formulate as many detailed questions as possible to answer your main question or research question. You can find detailed instructions and examples for the formulation of these so-called detailed questions in the Thesis Guide.

How does the Aristolo Guide help with data evaluation for the bachelor’s or master’s thesis or dissertation?

The Thesis Guide or Dissertation Guide has instructions for data collection, data preparation, data analysis and interpretation. The guide can also teach you how to formulate questions and answer them with data to create your own experiment. We also have many templates for questionnaires and analyses of all kinds. Good luck writing your text! Silvio and the Aristolo Team PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.

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The effects of visualization on judgment and decision-making: a systematic literature review

• Open access
• Published: 25 August 2021
• Volume 73 , pages 167–214, ( 2023 )

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• Karin Eberhard   ORCID: orcid.org/0000-0001-6676-8889 1  

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The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

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1 Introduction

A visualization is defined as a visual representation of information or concepts designed to effectively communicate the content or message (Padilla et al. 2018 ) and improve understanding in the audience (Alhadad 2018 ). This representation can manifest in a range of imagery, from quantitative graphs (Tang et al. 2014 ) to qualitative diagrams (Yildiz and Boehme 2017 ), to abstract visual metaphors (Eppler and Aeschimann 2009 ) or artistic imagery. Visualization design may also intend to promote a specific behavior in the audience (Correll and Gleicher 2014 ). The visualization of information is associated with effective communication in terms of clarity (Suwa and Tversky 2002 ), speed (Perdana et al. 2018 ), and the understanding of complex concepts (Wang et al. 2017 ). Research shows, for example, that visualized risk data require less cognitive effort in interpretation than textual alternatives and are therefore comprehended more easily (Smerecnik et al. 2010 ), and complex sentiment data visualized in a scatterplot improve the accuracy in law enforcement decisions compared to raw data (Cassenti et al. 2019 ).

Visual experiences are the dominant sensory input for cognitive reasoning in everyday life, business, and science (Gooding 2006 ). As Davis ( 1986 ) points out, image creation and perception are part of the “unique and quintessential competencies of homo sapiens sapiens”. Hence, the visualization of information is an integral research subject in the domains of cognitive psychology, education (Alfred and Kraemer 2017 ), management (Tang et al. 2014 ) including financial reporting, strategic management, and controlling, marketing (Hutchinson et al. 2010 ), as well as information science (Correll and Gleicher 2014 ).

Management researchers study visualizations from a business perspective. First, the field of financial reporting considers the effect of financial graphs on investor perception (Beattie and Jones 2008 ; Pennington and Tuttle 2009 ). Second, the potential consequences of visualizations on decision-making are examined in the area of managerial decision support, with a focus on judgments based on quantitative data such as financial decisions (Tang et al. 2014 ) and performance controlling (Ballard 2020 ). Finally, a small number of works investigate more complex decision-making based on qualitative, multivariate, and relational information (Platts and Tan 2004 ). Altogether visualizations fulfill a variety of functions, from focusing attention to sharing thoughts to identifying data structures, trends, and patterns (Platts and Tan 2004 ).

The vast majority of existing research in visualization, however, arises from the two domains of information science and cognitive psychology. Information science research on how to design visualizations for effective user cognition stretches back almost one century (Washburne 1927 ). While early research focuses on comparing tables and simple graphs, newer research on human–computer interfaces covers advanced data visualizations facilitated by computing power (Conati et al. 2014 ). For example, interactive visualization software enables users to manipulate data directly. While promising in terms of analytic capability, the potential for biases and overconfidence is suggested as a downside (Ajayi 2014 ). Equally, cognitive psychology research notes that visual information may be superior over verbal alternatives in certain cognitive tasks since they can be encoded in their original form, where spatial and relational data is preserved. Thereby, visual input is inherently richer than verbal and symbolic information, which is automatically reductionistic (Meyer 1991 ), but more suited for discrete information retrieval due to its simplicity (Vessey and Galletta 1991 ). However, the processes behind visual cognition remain largely unclear (Vila and Gomez 2016 ).

Despite the ubiquity of visualizations in research and practice, there is no comprehensive understanding of the potential and limits of information visualization for decision-making. Although at times converging, insights from research of different areas are seldom synthesized (Padilla et al. 2018 ), and there has been no effort for a systematic review or overarching framework (Zabukovec and Jaklič 2015 ). However, a synthesis of existing research is essential and timely due to three reasons. First, information visualization is ubiquitous both in the scientific and business community, yet there are conflicting findings on its powers and limits in support of judgment and decision-making. Second, cognitive psychology research provides several promising suggestions to explain observable effects of visualizations, yet these are rarely integrated into research in other domains, including strategic decision-making. Third, the barriers to using information visualization software have fallen to a minimum, making it available to a wide range of producers and users. This raises the issue of the validity of positive effects for various task and user configurations. The goal of this paper is therefore to provide an overview of the fragmented existing research on visualizations across the social and information sciences and generate insights and a timely research agenda for its applicability to strategic management decisions.

My study advances visualization research on three paths. First, I establish a framework to summarize the numerous effects and variable interactions surrounding the use of visualizations. Second, I conduct a systematic literature review across the social and information sciences and summarize and discuss this plethora of findings along with the aforementioned structure. Third, I utilize this work as a basis for identifying and debating gaps in existing research and resulting potential avenues for future research, with a focus on the area of strategic management decisions.

The structure of the article is as follows. The next chapter briefly describes the research field, followed by the methodology of my literature search. Next, I analyze the results of my search and discuss common insights. In the ensuing chapter, I develop an agenda for management research by building on particularly relevant ideas with conflicting or incomplete evidence. Finally, I conclude my review and discuss contributions and implications for practice.

2 Definition of the research field

2.1 definition of key terms.

Information visualizations support the exploration, judgment, and communication of ideas and messages (Yildiz and Boehme 2017 ). The term “graph” is often used as a synonym for information visualization in general (Meyer 1991 ) as well as describing quantitative data presentation specifically (Washburne 1927 ). As my review exhibits, these graphs constitute the prevalent form of information visualization. Common quantitative visualizations are line and bar charts, often showcasing a development over time and regularly used in financial reporting (Cardoso et al. 2018 ) and controlling (Hutchinson et al. 2010 ). In scientific literature, probabilistic charts such as scatterplots, boxplots, and probability distribution charts (Allen et al. 2014 ) frequently depict risk and uncertainty. More specialized charts include decision trees to depict conditional logic (Subramanian et al. 1992 ), radar charts to display complex multivariate information (Peebles 2008 ), or cluster charts and perceptual maps for marketing decision support (Cornelius et al. 2010 ).

Despite the breadth of existing visualization research, its application to strategic decisions is narrow and there is an abundance of research limited to elementary tasks and choices. To provide a clear distinction, I focus my search on decisions, judgments, and inferential reasoning as more advanced forms of cognitive processing. Decision-making can be broadly defined as choosing between several alternative courses of action (Padilla et al. 2018 ). On the other hand, reasoning and judgment refer to the evaluation of a set of alternatives (Reani et al. 2019 ), without actions necessarily being attached as for decision-making. Such efforts are cognitively demanding and complex when compared to more elementary tasks, such as a choice between options (Tuttle and Kershaw 1998 ), and include the rigorous evaluation of alternatives across a range of attributes, which is characteristic for strategic decisions (Bajracharya et al. 2014 ). For this reason, I include studies that examine the influence of visualizations on some form of decision or judgment outcome. Mason and Mitroff ( 1981 ) highlight that strategic decisions, in management and elsewhere, involve complex and ambiguous information environments. Information visualization may relate to decision quality in this context since one critical factor in the effectiveness of strategic decisions is the objective and comprehensive acquisition and analysis of relevant information to define and evaluate alternatives (Dean and Sharfman 1996 ).

2.2 Perspectives in literature

Visualization research exists within a range of domains in the social and information sciences, which reflects the diversity of the empirical application. I identify psychology (cognitive and educational), management (financial reporting, strategic management decisions, and controlling), marketing, and information science as the primary areas of research. This heterogeneity in terms of application area provides the first dimension in my literature review. Second, I classify existing studies along the type of variable interaction they primarily investigate. Based on the framework first introduced by DeSanctis ( 1984 ), I hereby differentiate four categories: Works principally focused on (1) the effects of visualizations on comprehension and decisions as dependent variables provide the basis of all research. This relationship is then investigated through: (2) User characteristics as moderators; (3) task and format characteristics as moderators; and (4) cognitive processing as mediator. An overview of this classification, including the prevalence of extant findings across domains, is given in Fig.  1 .

Visualization research structured by domain and variables primarily investigated

First, the investigation of visualization effects on decisions and judgments is established across all research areas mentioned, and primarily studies outcome variables such as decision accuracy (Sen and Boe 1991 ), speed (Falschlunger et al. 2015a ), and confidence (Correll and Gleicher 2014 ). While these studies contribute examples for graphs influencing observable decision effectiveness and efficiency across a range of contexts, they do not investigate moderating or mediating factors.

Second, psychology research pushes this investigation further towards including moderating effects of user characteristics , such as domain expertise and training (Hegarty 2013 ), and measures of cognitive ability such as numeracy (Honda et al. 2015 ) or literacy (Okan et al. 2018a ). The relevance of these moderating factors is validated both in studies focusing on cognition as well as experiments in educational research, for example by providing evidence that the quality of a judgment made based on a graph may depend more on the user than the format itself (Mayer and Gallini 1990 ).

Similarly, human–computer interface research spearheads further insights into moderating factors of task and format characteristics, such as task type (Porat et al. 2009 ), task complexity (Meyer et al. 1997 ), data structure (Meyer et al. 1999 ), and the graphical saliency of features (Fabrikant et al. 2010 ) through rigorous user testing. At the same time, Vessey ( 1991 ) developed the theory of cognitive fit as a concept bridging cognitive and information systems research, stating that positive effects of graphs depend on a fit between task type and format type, differentiating between symbolic and spatial archetypes.

Finally, cognitive psychology research aims at explaining the observable effects of visualization in terms of mediating cognitive mechanisms . Here, cognitive load theory provides the foundation, stating that an individual’s working memory capacity is limited, and performance in a task or judgment depends on the cognitive load they experience while assessing information. According to this logic, cognitive load that is too high damages performance (Chandler and Sweller 1991 ). Reducing cognitive load by providing visualizations in complex environments is therefore often stated as a key goal of graph design (Smerecnik et al. 2010 ).

Importantly, the boundaries between these variable categories are fluid. Many studies investigate more than one relationship and the inclusion of moderating variables has become common. Various application areas covering these interdependencies attest to the heterogeneous nature of visualization research. However, previous reviews highlight that insights are seldom shared across fields and call for the integration of findings into new studies (Padilla et al. 2018 ). In particular, strategic management research does not yet follow such a holistic approach.

3 Method of literature search

3.1 search design.

The methodological basis of this paper is a systematic literature search as a means to collect and evaluate the existing findings in a systematic, transparent, and reproducible way on the specified topic (Fisch and Block 2018 ) in order to produce a more complete and objective knowledge presentation than in traditional reviews (Clark et al. 2021 ). I conduct a keyword search on the online search engines EBSCOhost and ProQuest, limited to English-language works that have been peer-reviewed, in order to ensure the quality of the sources. Gusenbauer and Haddaway ( 2020 ) identify both search engines as principal academic search systems as they fulfill all essential performance requirements for systematic reviews. On EBSCOhost, I use the databases Business Source Premier , Education Research Complete , EconLit , APA PsycInfo , APA PsycArticles , and OpenDissertations to search for empirical works; on ProQuest, I use the databases British Periodicals , International Bibliography of the Social Sciences (IBSS) , Periodicals Archive Online , and Periodicals Index Online with a filter on articles to cover the social sciences comprehensively. The keyword used is the concatenated term “(visualization OR graph OR chart) AND (decision OR judgment OR reasoning)”, searched for in abstracts. Footnote 1 The terms were chosen as “visualization” is commonly used as a category name for visualized information (Brodlie et al. 2012 ), and the “graph” is the focus of traditional visualization research (Vessey 1991 ). The term “chart” is a synonym for both quantitative and qualitative graphs which has seen increasing use particularly in the 2000s (Semmler and Brewer 2002 ). The terms “judgment OR decision OR reasoning” were added to ensure that studies examining observable outcomes of visualization use, as opposed to cognitive processes such as comprehension only, were highlighted. After a review of the evolution of visualization research over time, I focus my search to articles published from the year 1990 in order to capture the recent advancements covering modern modes of information visualization. Footnote 2 This search results in 1658 articles combined, after removing duplicates 1505 articles remain.

Next, I review all article abstracts based on the three content criteria defined in the following. I include all articles rooted in the (1) social sciences or information sciences , where the focus of the study lies on (2) how a visualization per se or a variation within related visualizations affects a user's or audience's decision or judgment in a given task , and the topic is studied through (3) original empirical works. Most articles are excluded in this process and 116 studies remain due to the prevalence of graphs as auxiliary means, not the subject of research, in various domains, particularly in medical research. I repeat this exclusion process by reading the full texts of all articles and narrow down the selection further to 81 papers.

Building on this systematic search, I conducted a supplementary search through citation and reference tracking, as well as supplementary search engines, such as JSTOR (Gusenbauer and Haddaway 2020 ). Footnote 3 This includes gray literature such as conference proceedings or dissertations, which lie outside of traditional academic publishing. In addition, I limit the inclusion of gray literature to studies by researchers included in my systematic search and completed within the last 10 years in order to gather a comprehensive and up-to-date overview of the findings of working groups particularly relevant to visualization research. Thereby I identify 52 additional articles, resulting in a total of 133 articles included.

3.2 Limitations of search

Due to the plethora of existing literature mentioning the topic of visualization in various contexts and degrees of quality, I subject my search to well-defined limitations. First, I only include peer-reviewed articles in my systematic search. These are studies that have been thoroughly validated and represent the major theories within a field (Podsakoff et al. 2005 ). However, I incorporate gray literature of comparable quality as part of my additional exploratory search.

Second, I limit the search to information and social sciences to deliberately omit results from the broad areas of medicine and natural sciences. In these, various specific concepts are visualized as a means within research, yet not investigating the visualization itself. For the same reason, I only apply the search terms to article abstracts, since the terms “graph” and “chart” in particular will result in a high number of results when searched for in the full text, due to the common use of graphs in presenting concepts and results.

Third, I only include original empirical work in order to enable the synthesis and critical validation of empirical findings across research areas. At the same time, I acknowledge the existence of several highly relevant theoretical works, which inform my search design and structure while being excluded from the systematic literature search and analysis.

4.1 Overview of results

I identify a total of 133 articles, published between 1990 and 2020. Interest in visualization research gained initial momentum in the early 1990s (Fig.  2 ). More recently, the number of studies rises starting around 2008, with the continued publication of five to ten papers per year since and a visible peak in interest around 2014/15. A significant share of recent works stems from the information science literature, and the wealth of publications around 2014 coincides with the advent of mainstream interest in big data (Arunachalam et al. 2018 ), which is closely linked to information visualization for subsequent analysis and decision-making (Keahey 2013 ). In addition, a cluster of publications by one group of authors (Falschlunger et al. 2014 , 2015a , c, b) in the financial reporting domain enhances the observed peak in publications, which is therefore not indicative of a larger trend. Instead, the continued wealth of publications in the last decade shows the contemporary relevance of and interest in visualization research.

Articles included in systematic search by publication year and area of research

Next to the information sciences, the largest share of the studies identified originates in cognitive psychology research. Furthermore, management literature discusses visualization and graphs continuously throughout the last three decades, with notable peaks in interest around the year 2000 in the domain of annual reporting (Beattie and Jones 2000 , 2002a , b ; Arunachalam et al. 2002 ; Amer 2005 ; Xu 2005 ) and internal management reporting with classic bar and line graphs around the year 2015 (Falschlunger et al. 2014 , 2015a , c ; Tang et al. 2014 ; Hirsch et al. 2015 ; Zabukovec and Jaklič 2015 ). Consumer research in marketing constitutes a further domain regularly discussing visualizations and their effect on decisions and judgment (Symmank 2019 ), albeit to a smaller extent. This heterogeneity in research areas is reflected by the journals identified in my search, where the 133 articles spread across 83 different journals, complemented by ten studies from conference proceedings and three papers included in doctoral dissertations (Table 1 ). Apart from the articles in conference proceedings added through the supplementary exploratory search, the studies were published in journals with a SCIMAGO Journal Rank indicator ranging from 0.253 (Informing Science) to 8.916 (Journal of Consumer Research). All but four journals received Q1 and Q2 ratings, which equals the top half of all SCImago rated journals. The h-index ranges from 6 (Journal of Education for Library and Information Science) to 332 (PLoS ONE) (Scimago Lab 2021 ).

In the 133 articles identified, experiments are by far the most common method for data collection, with 113 (85%) of studies conducting a total of 182 controlled experiments with over 28,000 participants (Fig.  3 ). In addition, I find seven instances of archival research covering over 600 companies, six instances of surveys with almost 1000 participants in total, four quasi experiments, two natural experiments, and one field experiment to complete the picture.

Articles included in systematic search by methodology

Of the 182 experiments conducted, the majority works with students as subjects (125 or 69%). The largest remaining share investigates a sample of the general (online) population (32 or 18%) and only 13% study the effect of visualization with practitioners in their respective domain (24). In contrast, four out of the six surveys were conducted with practitioners that were addressed explicitly. Besides, one survey each was conducted with students and subjects from the general population.

Following the advice by Fisch and Block ( 2018 ), I categorize the results from literature in a concept-centric manner, based on the primary variable interaction investigated. I further distinguish by the four application domains and seven subdomains discussed and present a structured overview at the end of each subchapter. The independent variable in all cases is the use of a visual representation designed for a specific use case, either as opposed to non-visual representation methods such as verbal descriptions [e.g. Vessey and Galletta ( 1991 )], or traditional visualizations that the research aims to improve on [e.g. Dull and Tegarden ( 1999 )].

4.2 Effects of visualizations on decisions and judgments

4.2.1 judgment/decision accuracy.

The most common dependent variable investigated in visualization research is the accuracy of the subjects on a given comprehension, judgment, or decision task. Most studies are in psychology research, with positive effects dominating. In cognitive psychology, experiments show that well-designed visualizations can improve problem comprehension (Chandler and Sweller 1991 ; Huang and Eades 2005 ; Nadav-Greenberg et al. 2008 ; Okan et al. 2018b ). For example, Dong and Hayes ( 2012 ) show in their experiment with 22 practitioners that a decision support system visualizing uncertainty improves the identification and understanding of ambiguous decision situations. Likewise, visualizations improve decision (Pfaff et al. 2013 ) and judgment accuracy (Semmler and Brewer 2002 ; Tak et al. 2015 ; Wu et al. 2017 ) and improve the quality of inferences made from data (Sato et al. 2019 ). Findings in educational psychology support this claim. In teaching, visual materials improve understanding and retention (Dori and Belcher 2005 ; Brusilovsky et al. 2010 ; Binder et al. 2015 ; Chen et al. 2018 ) in students, and support the judgment accuracy of educators when analyzing learning progress quantitatively (Lefebre et al. 2008 ; Van Norman et al. 2013 ; Géryk 2017 ; Nelson et al. 2017 ). Furthermore, Yoon’s longitudinal classroom intervention (2011) using social network graphs enables students to make more reflected and information-driven strategic decisions. However, other studies arrive at more mixed or opposing findings. In their experiment, Rebotier et al. ( 2003 ) find that visual cues do not improve judgment accuracy over verbal cues in imagery processing. Other experiments even demonstrate verbal information to be superior over graphs in comprehension (Parrott et al. 2005 ) as well as judgment accuracy (Sanfey and Hastie 1998 ). Some graphs appear unsuitable for specific content, such as bar graphs depicting probabilities (Newman and Scholl 2012 ) and bubble charts encoding information in circle area size (Raidvee et al. 2020 ). In addition, more complex charts like boxplots, histograms (Lem et al. 2013 ), and tree charts (Bruckmaier et al. 2019 ) appear less effective for the accurate interpretation of statistical data in some experiments, presumably as they elicit errors and confusion in insufficiently trained students.

Studies in management and business research arrive at further, more pessimistic results. While Dull and Tegarden ( 1999 ) find in their experiment with students that three-dimensional visuals can improve the prediction accuracy in financial reporting contexts, and Yildiz and Boehme ( 2017 ) observe in their practitioner survey that a graphical model of a corporate security decision problem improves risk perception when compared to a textual description, most other studies present a less positive picture. Several studies do not find graphs superior over tables in financial judgments (Chan 2001 ; Tang et al. 2014 ; Volkov and Laing 2012 ), and in consumer research (Artacho-Ramírez et al. 2008 ). In financial reporting, a dedicated school of research investigates the effect of distorted graphs lowering financial judgment accuracy (Arunachalam et al. 2002 ; Beattie and Jones 2002a , b ; Amer 2005 ; Xu 2005 ; Pennington and Tuttle 2009 ; Falschlunger et al. 2014 ), irrespective of whether the distortion is intended by the designer. Chandar et al. ( 2012 ) elaborate on the positive effect of the introduction of graphs and statistics in performance management for AT&T in the 1920s, but more recent case study examples are rare.

By contrast, several experimental studies from human–computer interaction research largely contribute evidence for a positive effect. Targeted visual designs lead to higher judgment accuracy in specific tasks (Subramanian et al. 1992 ; Butavicius and Lee 2007 ; Van der Linden et al. 2014 ; Perdana et al. 2018 ) and improve decision-making (Peng et al. 2019 ). For example, probabilistic gradient plots and violin plots enable higher accuracy in statistical inference judgments in the online experiment by Correll and Gleicher ( 2014 ) than traditional bar charts. However, experiments by Sen and Boe ( 1991 ) and Hutchinson et al. ( 2010 ) equally lack a significant effect on data-based decision-making quality. Amer and Ravindran ( 2010 ) find a potential for visual illusions degrading judgment accuracy similar to results from financial reporting, and McBride and Caldara ( 2013 ) find that visuals lower accuracy in law enforcement judgments when compared to raw data presentation (Table 2 ).

4.2.2 Response time

The next most common outcome variable investigated in visualization research is response time , often referred to as efficiency. Across the board, experimenters observe that information visualization lowers response time in various judgment and decision tasks. In psychology, this includes decision-making in complex information environments (Sun et al. 2016 ; Géryk 2017 ). The opposite effect emerges from only one study, where Pfaff et al. ( 2013 ) find that a decision support system visualizing complex uncertainty information requires a longer time to use than one omitting this graphical information. In management research, Falschlunger et al. ( 2015a ) find that visually optimized financial reports can speed up judgment both for students and practitioners. Studies originating in information science validate this picture, observing that well-designed visualizations reduce response time in quantitative (Perdana et al. 2018 ) as well as geospatial judgment tasks (MacEachren 1992 ). Furthermore, McBride and Caldara ( 2013 ) observe that students in their experiments arrive at faster judgments when provided with a network graph as opposed to a table (Table 3 ).

4.2.3 Decision confidence

Next to these directly observable metrics, experimenters regularly elicit measures of decision confidence in visualization research based on subjects’ self-assessment. From a cognitive psychology perspective, Andrade ( 2011 ) finds that subjects display excessive confidence in estimates based on visualizations, which biases subsequent decision-making. On the other hand, Dong and Hayes ( 2012 ) show that a visual decision support system depicting uncertainty in engineering design leads to marginally lower decision confidence, compared to traditional methods omitting uncertainty information. In management research, Tang et al. ( 2014 ) present an increase in confidence in the context of financial decision-making, and Yildiz and Böhme (2017) find in their practitioner survey that an appealing visual increases decision confidence in a managerial setting without changing the actual decision outcome. Similarly, further experiments in information science provide evidence for increased confidence with a link to increased judgment accuracy (Butavicius and Lee 2007 ) or without (Sen and Boe 1991 ; Wesslen et al. 2019 ). In the context of uncertainty, Arshad et al. ( 2015 ) once again report novice subjects having lower confidence in the use of graphs with uncertainty visualized, however, this effect does not occur for practitioners (Table 4 ).

4.2.4 Prevalence of biases

Several studies investigate the prevalence of biases by searching for distinct patterns of deviations in judgment and decision accuracy with largely mixed results. Through a total of seven cognitive psychology experiments, Sun et al. ( 2010 , 2016 ) and Radley et al. ( 2018 ) find that varying scale proportions in graphs change the resulting decision-making since data points are evaluated in a cognitively biased manner based on their distance to other chart elements. Furthermore, Padilla et al. ( 2015 ) demonstrate that uncertainty is understood to a disparate extent when it is encoded through spatial glyphs, color, or brightness. In human–computer interaction research, experiments observe similar framing biases through salient graphical features (Diamond and Lerch 1992 ) such as color schemes (Klockow-McClain et al. 2020 ). Lawrence and O’Connor ( 1993 ) also show that graph scaling affects judgment and relate this to the anchoring heuristic. Finally, financial reporting research extensively dedicates its field of impression management on the observation that such biases are prevalent and possibly intended in annual report graphics, including through distorted graph axes (Falschlunger et al. 2015b ) and an intentional selection of information to visualize (Beattie and Jones 1992 , 2000 ; Dilla and Janvrin 2010 ; Jones 2011 ; Cho et al. 2012a , b ). Two further experiments compare the prevalence of cognitive biases with graphs compared to text directly and find no difference for the recency bias in financial reporting (Hellmann et al. 2017 ) as well as for other heuristics in data-based managerial decision-making (Hutchinson et al. 2010 ) (Table 5 ).

4.2.5 Attitude change and willingness to act

Observations on attitude change and the willingness to act on information constitute the final category of outcome variables found in visualization research. Cognitive psychology research observes an effect of visualizations on risk attitude, where salient graphs can either enhance risk aversion (Dambacher et al. 2016 ) or risk-seeking (Okan et al. 2018b ), depending on the information that is highlighted most saliently. Similarly, varied financial graphs change investors’ risk perception and subsequent investment recommendations (Diacon and Hasseldine 2007 ). In the area of performance management, the visualization of KPIs motivates managers’ intention to act on the information when compared to text (Ballard 2020 ). Consumer research investigates such phenomena commonly, where brand attitude and the intention to purchase a product represent specific cases of judgment and decision-making. Miniard et al. ( 1991 ) were among the first to show that different pictures can result in different attitudes, while Gkiouzepas and Hogg ( 2011 ) extend this investigation to visual metaphors. Finally, information science research provides further insights. King Jr et al. (1991) find that visualizations are more persuasive in attitude change than text, and Perdana et al. ( 2018 ) increase student subjects’ willingness to invest in their experimental setting through visualization software. On the other hand, Phillips et al. ( 2014 ) find their subjects to be less willing to seek out additional information in ambiguous decision settings (Table 6 ).

4.3 User characteristics as moderating variables

4.3.1 expertise and training.

Common moderating variables investigated both in psychological and information science research are the users’ expertise or training experience in a given domain. Experimenters widely encounter a positive impact of experience on the influence of visualizations on judgment accuracy and efficiency. In cognitive psychology, Hilton et al. ( 2017 ) find that graphs of statistical risk improve decision quality for more experienced practitioners alone. On the other hand, some results from educational psychology point towards the opposite effect of experience. Mayer and Gallini ( 1990 ) find in their student experiments that learners with higher pre-test performance benefit less from visual aids than learners on a lower level. In the information sciences, Conati et al. ( 2014 ) find in their testing of computer interfaces that experience with visualizations leads to a pronounced advantage in judgment accuracy. Training sessions (Raschke and Steinbart 2008 ) and experience through task repetition (Meyer 2000 ) enhance the positive effects of graphs (Table 7 ).

4.3.2 Cognitive ability

Another user characteristic regularly investigated in the social sciences is the measurement of cognitive ability . In psychology studies, Honda et al. ( 2015 ) and Cardoso et al. ( 2018 ) find that reflective ability determines in part how well subjects translate visualizations into accurate judgments. Visual working memory (Tintarev and Masthoff 2016 ) and numeracy (Honda et al. 2015 ) are further traits related to cognitive ability in dealing with visualizations and found to enhance the benefits of visualizations on judgment effectiveness and efficiency. The only study presenting contrary results consists of three experiments by Okan et al. ( 2018a ), where subjects with higher graph literacy are more prone to specific biases when shown bar graphs of health risk data, and thereby make less accurate judgments. On the other hand, experiments in financial reporting (Cardoso et al. 2018 ) confirm the positive effect of the reflective ability. Conati and Maclaren ( 2008 ) and Conati et al. ( 2014 ) extend this idea to perceptual speed in the area of consumer research (Table 8 ).

4.3.3 User preferences

Finally, experimenters investigate user preferences at times. In the adjacent field of musical education, for example, Korenman and Peynircioglu ( 2007 ) demonstrate that the visual presentation of learning material is only helpful to students with the respective learning style. In cognitive psychology, Daron et al. ( 2015 ) observe a variation in user preferences when presented with visualization options, however without a significant effect on decision performance. This result is replicated in an online survey on human–computer interaction by Lorenz et al. ( 2015 ). O’Keefe and Pitt ( 1991 ) operationalize cognitive style from the MBTI framework and find a weak association with the subjects’ reported preferences for text or specific chart types. However, no relation to actual judgment accuracy or efficiency is found (Table 9 ).

4.4 Task and format characteristics as moderating variables

4.4.1 task type.

One common task characteristic identified as a moderating variable is the task type , originally defined in the information sciences. In her seminal theoretical paper, Vessey ( 1991 ) identifies spatial and symbolic tasks as the two archetypes, which correspond to spatial and symbolic types of cognitive processing and spatial (graphical) and symbolic (textual/numerical) representations. She hypothesizes that visualizations improve judgment effectiveness where these three manifestations align, which she defines as cognitive fit and validates through experiments (Vessey and Galletta 1991 ), including in the sphere of multiattribute management decisions (Umanath and Vessey 1994 ). Further research in information science widely supports this moderating effect by comparing tables and standard quantitative graphs in judgment tasks of increasing complexity (Coll et al. 1994 ; Tuttle and Kershaw 1998 ; Speier 2006 ; Porat et al. 2009 ). On the other hand, experiments in managerial forecasting (Carey and White 1991 ) and financial reporting (Hirsch et al. 2015 ) present the effectiveness of graphical displays in spatial decisions, based on cognitive fit theory. Fischer et al. ( 2005 ) provide further evidence from the domain of cognitive psychology, showing that bar graphs support spatial-numerical judgments particularly well when the chart orientation equals the cognitive processing by following a left-to-right direction (Table 10 ).

4.4.2 Level of data structure

I identify two other task characteristics investigated in the literature, albeit infrequently. First, the level of data structure has been investigated only once in the information science domain. Meyer et al. ( 1999 ) find line charts superior over tables in judgment tasks when the underlying data is structured, with the opposite effect for unstructured data (Table 11 ).

4.4.3 Task complexity

Second, two further experiments observe task complexity as a moderating effect. Meyer et al. ( 1997 ) demonstrate that the speed advantage they find for tables over bar graphs in their computer interface tasks becomes more pronounced with increasing task complexity. However, the same effect does not occur for line graphs. On the other hand, Falschlunger et al. ( 2015c ) find task complexity to be the main factor in predicting task efficiency and effectiveness in handling financial reports but do not observe interaction effects with the visualization (Table 12 ).

4.4.4 Graphical saliency of relevant data

Finally, various studies investigate modifications in the graph format as a variable, with a focus on the graphical saliency of relevant data . This area of research is bridging the two domains of cognitive psychology and information science with widely overlapping results. For example, Verovszek et al. (2013) observe in their information science experiment that colored visualizations are less effective in supporting laypeople’s judgments on urban planning than simple black-and-white line drawings since colorful, irrelevant features distract from the core information. Van den Berg et al. ( 2007 ) identify color as a more powerful feature to highlight salient information in graphs than other variables, such as size. Spence et al. ( 1999 ) find that variations in brightness lead to faster response times in comparison tasks than variations in color. Breslow et al ( 2009 ) demonstrate that the moderating effect of the use of color on judgment speed depends on the task type, with multicolored visuals ideal for identification tasks and black-and-white brightness scales preferable for comparison tasks. Finally, MacEachran et al. (2012) find colorless suited to represent uncertainty when compared to features such as fuzziness or transparency in their surveys with students and practitioners.

Next to color, three-dimensional depth cues have received attention in research. Several psychology experiments find that three-dimensional depth cues irrelevant to the information visualized lower judgment accuracy (Zacks et al. 1998 ; Edwards et al. 2012 ) as well as speed (Fischer 2000 ). Negative effects occur equally for other irrelevant visual cues lowering the saliency of actually relevant information (Fischer 2000 ). Further studies show that increasing the saliency of relevant features can enhance the tendency to make compensatory choices (Dilla and Steinbart 2005 ) and shorten response time (Fabrikant et al. 2010 ), while visual clutter decreases judgment accuracy and boosts response times (Ognjanovic et al. 2019 ). Several other studies test the suitability of a specific set of graphs for unique judgment areas such as uncertainty simulation in urban development (Aerts et al. 2003 ), risk communication (Stone et al. 2017 ; Stone et al. 2018 ), and performance management (Peebles 2008 ) (Table 13 ).

4.5 Cognitive aspects as mediating variables

4.5.1 cognitive load.

Cognitive psychology research introduces the idea of cognitive processes mediating the influence of visualizations on judgment performance, with a focus on cognitive load . Jolicœur and Dell’Acqua ( 1999 ) show in their experiment that the perception of visualizations is subject to structural constraints in working memory capacity, and Allen et al. ( 2014 ) manipulate cognitive load as a dependent variable to demonstrate that judgment accuracy and speed using visualizations decrease under higher cognitive load. Subsequently, psychology experiments provide evidence that visualizations improve decision performance by reducing cognitive load as a mediating factor, operationalized and measured either through pupil size and dilation (Smerecnik et al. 2010 ; Toker and Conati 2017 ) or self-reported load (Cassenti et al. 2019 ). In management research, Ajayi ( 2014 ) investigates this relationship in the context of a proprietary visualization tool for financial data but finds no effect of the visualization component on cognitive load or judgment accuracy. Two further experiments in human–computer interface research operationalize cognitive load based on subjective reporting (Anderson et al. 2011 ) and performance in a secondary task (Block 2013 ) and demonstrate that cognitive load mediates the relationship between visualization use and judgment accuracy and speed, with some types of graphics better suited than others (Table 14 ).

4.5.2 Gazing behavior

Another concept frequently operationalized to represent working memory capacity is gazing behavior , which more recent experiments observe through the use of eye-tracking technology, pioneered by the information sciences. Reani et al. ( 2019 ) observe in their experiment with 49 students that gazing behavior is associated with judgment accuracy, where subjects that pay more attention to relevant visual areas deliver more accurate answers. Similarly, Lohse ( 1997 ) finds that in the more complex decision environment of a budget allocation simulation, decision accuracy is related to efficient gazing behavior and can be improved through the use of colors to reduce the time subjects spend looking at the chart legend. Psychology experiments validate that well-designed graphs enable subjects to focus their attention on relevant information and subsequently improve decision accuracy (Huestegge and Pötzsch 2018 ) and response time (Vila and Gomez 2016 ) (Table 15 ).

4.5.3 Attention

Another variable operationalized at times in eye-tracking experiments is attention, which is elicited through metrics such as the average gazing duration on a specific visual element (Pieters et al. 2010 ). In their cognitive psychology experiment, Smerecnik et al. ( 2010 ) observe that graphs attract more attention in risk communication compared to tables and text and are associated with more accurate judgments. Applying this idea to consumer research, Pieters et al. ( 2010 ) study the consumer’s attention towards visual advertisements and observe that visual complexity based on features such as decorative color can hurt attention, while well-structured complexity such as arrangements of relevant information enhances attention and the attitude toward the brand (Table 16 ).

4.5.4 Affect

Finally, some research emerges into the potential mediating role of affect . Harrison ( 2013 ) shows in her large-scale online experiment that affective priming can significantly influence judgment accuracy in tasks supported visually and that the graphs themselves can cause a change in affect valence. Similarly, Plass et al. ( 2014 ) demonstrate in their educational research that color and shape in visualizations can evoke positive affect and are associated with better student learning (Table 17 ).

5 Discussion

In this paper, I have presented a systematic and integrative review of the current state of research on the effect of information visualization in the social and information sciences. I structured and summarized the results of my systematic literature review along the type of variable interactions present in experimental research. In order to discuss and synthesize the variety of literature insights, I categorize them into three groups: Descriptions of the positive effects for visualizations within decision-making, elaborations on moderators of this potential, and insights into negative effects of misguided visualization use. Table 18 highlights this categorization of results by application domain.

5.1 Positive Effect 1: Information visualization improves decision accuracy and quality

Research findings overwhelmingly confirm the hypothesis that visualizations enable the user to comprehend information more effectively, subsequently improving performance in judgments and decisions. The reason behind this effect is most commonly attributed to cognitive mechanisms. Suwa and Tversky ( 2002 ) point out that based on cognitive load theory, less working memory is needed when visuals provide external representations of concepts, which one can easily refer back to and thereby need not keep in mind, leading to improved judgments. Allen et al. ( 2014 ) show in their experiment that under externally induced cognitive load, well-designed charts suffer less than cluttered ones. Furthermore, graphs enable a simpler gazing pattern than text, which can be used as an indicator of cognitive effort (Smerecnik et al. 2010 ). Based on the concept of cognitive load reduction, visualizations are effectively used in various application areas including management research (Falschlunger et al. 2014 ) and more specifically managerial decision-making (Yildiz and Boehme 2017 ), next to psychology and information sciences more broadly.

5.2 Positive Effect 2: Information visualization steers attention towards uncertainty

A large share of studies identified points towards the strength of visualizations in enhancing uncertainty and risk features in a data set. Beyond increasing the awareness of uncertainty (Dong and Hayes 2012 ), the question of whether visualizations can also improve the reasoning with probabilistic information is studied extensively. Various studies show that visualizations can reduce typical comprehension issues, resulting in the more accurate use of probabilities from a statistical perspective (Allen et al. 2014 ; Wu et al. 2017 ; Stone et al. 2018 ). Positive effects in risk understanding are evaluated particularly in the contexts of safety, such as food safety (Honda et al. 2015 ) and violence risk (Hilton et al. 2017 ). Studies investigating the cognitive processes more closely provide evidence that simpler charts indeed perform best (Edwards et al. 2012 ) since they can reduce cognitive load (Anderson et al. 2011 ) and ultimately improve the internal processing of probabilistic models (Tak et al. 2015 ). As Quattrone ( 2017 ) points out, ambiguity and uncertainty are inherent in managerial decision-making and should be embraced by information visualization, but research on this insight in management is scarce.

5.3 Positive Effect 3: Information Visualization Speeds Up Cognitive Processing

There is evidence that graphs lead to faster processing, learning, and decision-making (Block 2013 ), as judgment and decision efficiency are measured and operationalized as the response time in various experiments. Utilizing eye-tracking technology, Reani et al. ( 2019 ) point out that different types of graphs result in varying gazing patterns in users and hypothesize a link to the reasoning processes. Based on the principle of saliency, multiple studies show that graphs optimally designed to focus attention on the most relevant information lead to more efficient and thereby faster gazing (Falschlunger et al. 2014 , 2015a ), since more time can be spent focusing on highly relevant information (Vila and Gomez 2016 ). Much of this existing work stems from the area of management reporting, investigating quantitative financial data. Overall, the evidence for visual aids speeding up cognitive processing and decision-making appears robust and applicable to management research.

5.4 Moderator 1: The effects of visualization depend on cognitive fit within the decision context

Cognitive fit is a moderator in the effectiveness of visualizations that has been well validated across psychological, management, and information science. Introducing cognitive fit theory, Vessey ( 1991 ) explains many existing research findings in the graph versus table literature claiming that graphs are not (always) more effective, most notably by DeSanctis ( 1984 ). Cognitive fit theory is validated widely (Vessey and Galletta 1991 ; Carey and White 1991 ; Coll et al. 1994 ; Meyer et al. 1997 ; Meyer 2000 ; Porat et al. 2009 ; Perdana et al. 2019 ). Padilla (2018) recognizes that this well-documented effect arises because a cognitive mismatch between data, task, and approach (format) requires more working memory, which negatively affects cognitive processing effectiveness and efficiency. Though highly reliable, many studies investigate elementary processing tasks with limited external validity for more complex decision-making in practice. Umanath and Vessey ( 1994 ) and others (Tuttle and Kershaw 1998 ; Hirsch et al. 2015 ) extend the original cognitive fit theory and successfully apply it to multi-attribute judgments—though at a potential time-accuracy tradeoff. Finally, the idea of matching task and format complexity can be seen as an extension to cognitive fit theory, where graphs are only helpful when they represent as much data complexity as necessary to complete the respective task, but as little as possible (Pieters et al. 2010 ; Van der Linden et al. 2014 ; Géryk 2017 ).

5.5 Moderator 2: Differences within users can be more relevant than the visualization design

Task complexity in relation to user ability needs to be strictly controlled for as a moderator of positive visualization effects. Early studies including individual differences hypothesize that graph potential may be limited to users with a high level of ability (Subramanian et al. 1992 ). Other studies claim that the positive effects of visualizations may be more significant for (McIntire et al. 2014 ) or even limited to (Mayer and Gallini 1990 ) less-skilled individuals. However, these seemingly conflicting results can be explained by the idea that since graphs are effective by requiring less working memory than other formats, improvements are only visible where working memory capacity is limited and needed elsewhere (Lohse 1997 ).

Furthermore, the majority of studies including user factors emphasize the importance of training and expertise, as opposed to inherent ability. Various studies support the claim that experience significantly enhances the contribution of visuals (Porat et al. 2009 ; Edwards et al. 2012 ; Falschlunger et al. 2015a ; Ognjanovic et al. 2019 ), with some claiming that training constitutes a requirement (Géryk 2017 ; Hilton et al. 2017 ) or that users without training are subject to stronger biases (Raschke and Steinbart 2008 ). Consequently, the training factor needs to be closely monitored particularly for a novel or complex visualization. However, extensive training of users is frequently time-consuming and costly. Therefore, the imperative arises for interactive visualization interfaces to accommodate for varying user needs in demanding decision situations. Interactive data visualization software is shown to improve investment decisions (Perdana et al. 2018 ) and judgments by reducing cognitive load (Ajayi 2014 ), for example with flexible performance management dashboards that reduce information load while hosting a full set of KPIs (Yigitbasioglu and Velcu 2012 ). Contrary to much of the early research on static visualizations, the progress in interactivity studies has been driven by practice and case studies, with calls for science to follow suit (Marchak 1994 ; McInerny et al. 2014 ). Overall, I conclude that a match in ability and training with format complexity and novelty, respectively, is a significant determinant of the effectiveness of visualizations. However, there has been little to no empirical research on the subject in the domain of management.

5.6 Negative Effect 1: Visualizations May Not Always Be Helpful: Risk to Impair Decision Making by Misguiding Attention

Several studies, including in management research, argue that visualizations misguide attention even in the presence of cognitive and user fit. For example, Hutchinson (2010) finds graphs to be as exposed to cognitive biases as tables in data-based managerial decision-making. Similarly, other studies identify graphical representations as equally or less effective than verbal formats in financial reports (Volkov and Laing 2012 ), forecasting (Chan 2001 ), probabilistic comprehension (Parrott et al. 2005 ), evidence evaluation (Sanfey and Hastie 1998 ), and communication (Rose 1966 ). The common denominator in these studies is the suboptimal use of salient visual elements, leading to distraction. For example, overly realistic visualizations encompassing color and higher complexity (DeSanctis 1984 ), may lead to visual clutter that decreases performance (Alhadad 2018 ). As Padilla et al. ( 2018 ) argue, visualizations are powerful because they attract fast cognitive bottom-up processing. However, when this superficial processing is focused on irrelevant elements, decision quality can suffer. A well-studied example of this effect is the addition of superfluous three-dimensional cues to quantitative graphs, which lowers accuracy in using the graph (Zacks et al. 1998 ; Fischer 2000 ).

5.7 Negative Effect 2: Visualizations can increase decision-maker overconfidence

The most documented cognitive bias in my review is overconfidence, which can be aggravated by the use of visualizations (O’Keefe and Pitt 1991 ). Multiple studies demonstrate that graph use can increase decision confidence without enhancing decision quality to the same extent in the context of management and finance (Tang et al. 2014 ; Yildiz and Boehme 2017 ; Wesslen et al. 2019 ). This may result from the perception that visualizations show more information at once (Miettinen 2014 ), thereby seemingly requiring less search for additional information (Phillips et al. 2014 ). In particular, this can be the case when graphs appear to visually simplify a problem and the decision-maker fails to adjust his confidence to the underlying complexity (Sen and Boe 1991 ). There is some research with inconclusive results (Pfaff et al. 2013 ), showing no difference in confidence (Hirsch et al. 2015 ) or even lowered confidence (Dong and Hayes 2012 ; Arshad et al. 2015 ). However, the majority of these studies deal with uncertainty communication, which is inherently tied to a decrease in confidence (Watkins 2000 ). Overall, the evidence demonstrates that unless highlighting uncertainty, visual aids result in higher decision confidence. The case of overconfidence is particularly well established in the area of management controlling and financial reporting but understudied for strategic decisions.

6 Research agenda

In summary, there is ample evidence for the potential of information visualization to improve decision-making in terms of effectiveness and efficiency, yet my review highlights possible limitations and risks where its use is misguided or inappropriate. I argue that several of these are particularly critical for further research since there is little to no application to the domain of strategic management decisions, despite the ubiquity of visualizations to support these in practice. Based on the summary of my insights by application domain in Table 18, I identify five research gaps in the field of strategic management decisions.

First, there is conflicting evidence regarding the effect of information visualization on decision-making under uncertainty, and existing research is mostly limited to information science (Aerts et al. 2003 ). Depending on the context and design, visualization use can increase or reduce risk-taking (Dambacher et al. 2016 ) but has the potential to improve probabilistic reasoning in an objective manner (Allen et al. 2014 ). Given the importance of uncertainty as a defining factor of strategic management decisions (Quattrone 2017 ), the possibility of information visualizations to improve risk understanding in the management context deserves closer evaluation. For example, the framing bias is a well-documented phenomenon in strategic decision-making (Hodgkinson et al. 1999 ), leading to different subjective risk interpretations and subsequent decisions based on the presentation of information. Naturally, the question arises whether information visualization can mitigate this bias and which salient visual features are beneficial. I suggest exploring this question through experiments with strategic management decision vignettes.

Research Gap 1: How can information visualization mitigate the framing bias and improve risk understanding in strategic management decisions?

Second, my review has made clear that the effectiveness of information visualization depends in large parts on user characteristics such as expertise (Hilton et al. 2017 ), numeracy (Honda et al. 2015 ), and graph literacy (Okan et al. 2018b ), yet there exists no transfer of this insight towards individual managerial traits. At the same time, well-established concepts such as the Upper Echelons Theory (Hambrick 2007 ) highlight the relevance of CEO characteristics, both observable and psychological for strategic managerial choices and, subsequently, company performance. While some concepts such as experience may be transferrable from existing visualization research (Falschlunger et al. 2015c ) requiring validation only, others, such as group position or individual values, present opportunities to extend theory substantially. I suggest exploring this area through a dedicated analysis of relevant CEO characteristics and corresponding empirical research with practitioner subjects.

Research Gap 2: How do CEO characteristics influence the effectiveness of information visualization in strategic management decisions?

Third, while the prevalence of visualization use for impression management in financial reporting is well-established (Falschlunger et al. 2015b ), there is a complete lack of transfer of this phenomenon to the realm of strategic management decisions. As Whittington et al. ( 2016 ) highlight, strategy presentations can be seen as an effective tool for CEO impression management. Given the popularity of visualizations in this communication medium – both through quantitative charts and schematic diagrams (Zelazny 2001 ), the question arises to what degree impression management also takes place in this case, for example through the reporting bias (Beattie and Jones 2000 ). I suggest investigating this subject empirically, for example through archival studies.

Research Gap 3: To what extent does CEO impression management occur through visualization use in strategy presentations?

Fourth, while overconfidence in managerial decision-making is a commonly reported issue with significant efforts to develop corrective feedback as a remedy (Chen et al. 2015 ), there is little understanding of the role of information visualization in this matter. My review has demonstrated that visual aids often increase decision confidence as much as they improve the judgment itself (Yildiz and Boehme 2017 ) or even more (Sen and Boe 1991 ), but can also reduce confidence, particularly where uncertainty information is depicted (Dong and Hayes 2012 ). However, the latter effect was only studied for topics unrelated to management. Therefore, there is a complete lack of understanding of the effects of visualizations on managerial overconfidence, and I suggest exploring this research gap empirically with practitioners.

Research Gap 4: How do visual aids influence overconfidence in managerial decision-making?

Finally, a large share of cognitive psychology research discusses the effectiveness of visualization use through the reduction of cognitive load, yet they usually start off with low-load contexts, which is the opposite of high-stress managerial decision-making (Laamanen et al. 2018 ). Allen et al. ( 2014 ) find evidence that the effectiveness of distinct graph types changes with the level of externally induced cognitive load, raising the question to what extent previous insights on helpful visual aids are applicable to managerial decisions in a high-stakes environment filled with distractions and parallel issues requiring attention. Therefore, I suggest studying visualization use in experimental environments with varying levels of cognitive load as the independent variable, ideally with management practitioners and a realistic strategic task setting.

Research Gap 5: How does cognitive load influence the effectiveness of information visualization in strategic management decisions?

7 Conclusion

Information visualization has become ubiquitous in our daily professional and private lives, even more so with the advent of accessible and powerful computer graphics. However, the impact that visualizations have on human cognition and ultimately decisions stills remains unclear to a large extent. While the prevalence of visualization research across a plethora of application domains shows its pertinence, the decentralized approach has led to a scattered and unstructured field of theories and empirical evidence. My literature review thus sought to provide a far-reaching overview of this work and a detailed research agenda. As a result, three contributions arise from my review.

First, I provide an overarching structure to summarize the range of effects and interacting variables that can be found surrounding visualization research. This includes a wide set of dependent variables ranging from decision quality and speed to confidence and attitudes, as well as complex moderating and mediating effects that are crucial to understanding the overall power of visualizations. This precise framework is paramount to a holistic and comprehensive review of the scattered existing literature.

Second, to the best of my knowledge, my systematic literature review is the first on visualizations spanning the whole of social and information sciences simultaneously. While some previous reviews such as the one by Yigitbasioglu and Velcu ( 2012 ) utilize a multidisciplinary approach, they usually define the visualization type investigated more narrowly, for example by focusing on dashboards only. I believe that my integrative overview provides a valid contribution to the ongoing work to synthesize the mixed results in visualization research.

Third, I demonstrate that despite the plethora of evidence at first sight, visualization research is far from complete due to its multitude of moderating variables and at times conflicting results. Building on my systematic review of existing literature, I specify an agenda of potential research directions for future studies to follow in order to advance our understanding of the cognitive implications of visualizations in the context of managerial decision making in particular.

This paper also has direct implications for management practice. As Zhang ( 1998 ) points out, managerial decision-making is particularly well-positioned to profit from good visualizations since it often utilizes unstructured, large sets of information that are computer-centered, dynamic, and need to be interpreted constantly under time pressure. However, the interaction of visualization use with various factors should not be underestimated in the design of computer graphics for decision support. The high validity of the cognitive fit theory and the contingency on user characteristics found in the literature demonstrates that the designer should spend extensive time on clarifying for whom and what the visualization is intended. Furthermore, the potential for overconfidence and automatic processing based on visualized information may result in decision-makers skipping on more elaborate thought, which may be desirable in some, but certainly not all situations.

Availability of data and material

Not applicable.

Code availability

Thanks to the anonymous reviewer for encouraging me to extend my keyword search.

Thanks to the anonymous reviewer for this valuable impulse.

Thanks to the anonymous reviewer for pointing me towards additional, highly relevant articles.

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Eberhard, K. The effects of visualization on judgment and decision-making: a systematic literature review. Manag Rev Q 73 , 167–214 (2023). https://doi.org/10.1007/s11301-021-00235-8

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Data Visualization

You are here, weekly hours, teaching methodology, evaluation methodology, bibliography, previous capacities.

• MEI: Elective
• MDS: Elective

Person in charge

Transversal competences, information literacy.

• CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
• CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
• CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
• CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Generic Technical Competences

• CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

Technical Competences

• CE5 - Model, design, and implement complex data systems, including data visualization
• CE11 - Analyze and extract knowledge from unstructured information using natural language processing techniques, text and image mining
• Goals of visualization systems
• Basic concepts
• History of visualization
• Data, tasks, and users
• Preattentive variables
• Ranking of visual variables
• Application of perception in visualization
• Proportions
• Distribuciones
• Relationships
• Other techniques
• Advanced visualization techniques Related competences: CT4 , CG2 , CE5 , CE11 , CB7 , CB8 , CB9 ,
• Geospatial visualization Related competences: CT4 , CG2 , CE5 , CE11 , CB7 , CB8 ,
• Exploratory data analysis
• Explanatory visualizations
• Trees and graphs visualization Related competences: CT4 , CG2 , CE5 , CE11 , CB7 , CB8 , CB9 ,
• Time-oriented visualization Related competences: CT4 , CG2 , CE5 , CE11 , CB9 ,
• Text visualization Related competences: CT4 , CG2 , CE5 , CE11 , CB7 , CB8 , CB9 ,
• Multiple views
• Coordinated views
• Interaction
• Advanced visualization concepts Related competences: CT4 , CG2 , CE5 , CE11 , CB8 , CB9 ,
• Visualization 101 This section will introduce the most important visualization concepts, some bad practices will be described. The history of the display will also be discussed.
• Data visualization idioms This topic will show the most basic data visualization techniques and also present some more advanced techniques for visualizing complex data, such as multi-variable visualization or geospatial visualization.
• Perception The basic operation of the visual perception system will be explained. Some important concepts such as attentional variables, the importance of color, and the most important principles of perception will also be described. It will also describe which visual variables are perceived more carefully than others.
• Multiple view design To represent highly complex information, it is very common to need multiple variables and views. This section will cover how to design complex systems using multiple views: how to organize views, separate data, and how to create linked interactions.
• Implementation of data visualization applications There are many tools and technologies developed that allow the programming of data visualization systems. There are tools that do not require any programming such as Tableau, Vega, Lyra or that provide more control over the result using programming languages and libraries such as Altair for Python, Matplotlib for R, or D3 for JavaScript. The aim of this topic is for students to be able to assess the needs of a project in order to be able to choose the right tool. In addition, it will also be essential for students to learn how to make interactive data visualization applications using a modern library, such as Altair or Vega.
• Visualization for specialized data This section will deal with data that have a specific nature, such as geospatial data, temporal data, textual data, etc.
• Advanced concepts In this section, we will deal with advanced visualization concepts, that may include areas such as the visualization of scientific data, dimensionality reduction algorithms, etc.

Activity Evaluation act

Introduction to visualization and data visualization systems

• Theory: Display definition. Importance and impact. Introduction to display systems.
• Problems: Examples of good and bad practices.
• 1 . Visualization 101

Visualization techniques

• Laboratory: Design of effective visualizations. Data cleaning. Implementation of basic data visualizations.
• Guided learning: Practical exercises for visualizing simple data sets.
• Autonomous learning: Data cleaning exercises. Practical exercises for visualizing simple data sets.
• 2 . Data visualization idioms
• Theory: Perception and color. Ranking of visual variables. Concepts of perception: attentive variables. Principles of perception. Brands and channels. Use of color and color palettes.
• Problems: Perception and color. Ranking of visual variables. Concepts of perception: attentive variables. Principles of perception. Brands and channels. Use of color and color palettes.
• 3 . Perception

Techniques for specialized data visualization

• 6 . Visualization for specialized data

Multiple view design

• Theory: Multiple view design. Organization of multiple views. Coordinated views. Interaction. Exploratory data analysis.
• Problems: Multiple view design. Organization of multiple views. Coordinated views. Interaction. Exploratory data analysis.
• Laboratory: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
• Guided learning: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
• Autonomous learning: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
• 4 . Multiple view design

Advanced visualization concepts

• Theory: In this section, advanced concepts will be introduced, such as dimensionality reduction algorithms, visualization of scientific data, etc.
• 7 . Advanced concepts

Implementation of data visualization applications.

• Laboratory: Learning a data visualization tool or library. Data visualization project.
• Guided learning: Learning a data visualization tool or library. Data visualization project development.
• Autonomous learning: Learning a data visualization tool or library. Data visualization project development.
• 5 . Implementation of data visualization applications

Lab1 delivery

Lab2 delivery.

• Visualization analysis and design - Munzner, Tamara; Maguire, Eamonn, CRC Press, Taylor & Francis Group, 2015. ISBN: 9781466508910 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004067699706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Show me the numbers : designing tables and graphs to enlighten - Few, Stephen, Analytics Press, 2012. ISBN: 9780970601971 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004067739706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Analítica visual : cómo explorar, analizar y comunicar datos - Pascual Cid, Víctor; Rovira Samblancat, Pere, Ediciones Anaya Multimedia, [2020]. ISBN: 9788441541986 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004213959706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Data visualisation : a handbook for data driven design - Kirk, Andy, Sage Publications Ltd, 2019. ISBN: 9781526468925 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004173629706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Better data visualizations : a guide for scholars, researchers, and wonks - Schwabish, Jonathan A, Columbia University Press, [2021]. ISBN: 9780231550154 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991001811849706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Storytelling with data : a data visualization guide for business professionals - Knaflic, Cole Nussbaumer, John Wiley & Sons, Inc, cop.2015. ISBN: 978-1119002253 https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004091419706711&context=L&vid=34CSUC_UPC:VU1

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Completed Bachelor and Master Theses

Here, we show an excerpt of bachelor and master theses that have been completed since 2017.

Master Thesis: Be the teacher! - Viewport Sharing in Collaborative Virtual Environments [in progress]

Have you ever tried to show somebody a star in the night sky by pointing towards it? Conveying viewport relative information to the people surrounding us is always challenging, which often leads to difficulties and misunderstandings when trying to teach content to others. This issue also occurs in collaborative virtual environments, especially when used in an educational setting. However, virtual reality allows us to manipulate the otherwise constrained space to perceive the viewports of collaborators in a more direct manner. The goal of this thesis is to develop, analyze, implement and evaluate techniques on how students in a collaborative virtual environment can better perceive the instructor’s viewport. Considerations such as cybersickness, personal space, and performance need to be weighed and compared. The thesis should be implemented in Unity (Unreal can be discussed), and you should be interested in working with networked collaborative environments. Further details will be discussed in a meeting, but experience with Unity or more general Netcode would be very helpful.

Master Thesis: Impact of Framerate in Virtual Reality [in progress]

When talking about rendering in virtual reality high framerates and low latency are said to be crucial. While there is a lot of research regarding the impact of latency on the user this master thesis aims to focus on the impact of the framerate. The goal of the thesis is to design and evaluate a VR application that measures the influence of the framerate on the user. The solution should be evaluated in an expert study. Further details will be discussed in a meeting. Contact: Marcel Krüger, M.Sc. Simon Oehrl, M. Sc.

Bachelor Thesis: Interaction with Biological Neural Networks in the Context of Brain Simulation [in progress]

The ability to explore and analyze data generated by brain simulations can give various new insights about the inner workings of neural networks. One of the biggest challenges is to find interaction techniques and user interfaces that allow scientists to easily explore these types of data. Immersive technology can aid in this task and support the user in finding relevant information in large-scale neural networks simulations. The goal of this thesis is to explore techniques to view and interact with biological neural networks from a brain simulation in Unreal Engine 4. The solution should provide easy-to-use and intuitive abilities to explore the network and access neuron-specific properties/data. A strong background in C++ is needed, experience with UE4 would be very helpful. Since this thesis focuses on the visualization of such networks, an interest in immersive visualization is needed, knowledge about neural networks or brain simulation is not necessary. Contact: Marcel Krüger, M.Sc.

Bachelor/Master Thesis: Exploring Immersive Visualization of Artificial Neural Networks with the ANNtoNIA Framework [in progress]

Bachelor Thesis: Group Navigation with Virtual Agents [in progress]

Bachelor Thesis: Walking and Talking Side-by-Side with a Virtual Agent [in progress]

• Common head-mounted displays (HMDs) only provide a small field of view , thus limiting the peripheral view of the user. To this end, seeing an agent in a side-by-side alignment is either hampered or not possible at all without constantly turning one’s head. For room-mounted displays such as CAVEs with at least three projection screens, the alignment itself is possible.
• Which interaction partner aligns with the other? Influencing aspects here are, e.g., is the goal of the joint locomotion known by both walkers, or just by one?
• For the fine-grained alignment , the agent’s animation or the user’s navigation strategy needs to allow many nuances, trajectory- and speed-wise.

Master Thesis: Exploring a Virtual City with an Accompanying Guide [in progress]

Master Thesis: Unaided Scene Exploration while being Guided by Pedestrians-as-Cues [in progress]

Bachelor Thesis: Teacher Training System to Experience how own Behavior Influences Student Behavior [in progress]

Bachelor/Master Thesis: Immersive Node Link Visualization of Invertible Neural Networks

Neural networks have the ability to approximate arbitrary functions. For example, neural networks can model manufacturing processes, i.e., given the machine parameters, a neural network can predict the properties of the resulting work piece. In practice, however, we are more interested in the inverse problem, i.e., given the desired work piece properties, generate the optimal machine parameters. Invertible neural networks (INNs) have shown to be well suited to address this challenge. However, like almost all kinds of neural networks, they are an opaque model. This means that humans cannot easily interpret the inner workings of INNs. To gain insights into the underlying process and the reasons for the model’s decisions, an immersive visualization should be developed in this thesis. The visualization should make use of the ANNtoNIA framework (developed at VCI), which is based on Python and Unreal Engine 4. Requirements are a basic understanding of Machine Learning and Neural Networks as well as good Python programming skills. Understanding of C++ and Unreal Engine 4 is a bonus but not necessary. Contact: Martin Bellgardt, M. Sc.

Master Thesis: Automatic Gazing Behavior for Virtual Agents Based on the Visible Scene Content [in progress]

Master Thesis: Active Bezel Correction to Reduce the Transparency Illusion of Visible Bezels Behind Opaque Virtual Objects

Bachelor/Master Thesis: Augmented Reality for Process Documentation in Textile Engineering [in progress]

Bachelor Thesis: Fast Body Avatar Calibration Based on Limited Sensor Input

Bachelor Thesis: Benchmarking Interactive Crowd Simulations for Virtual Environments in HMD and CAVE Settings

Bachelor Thesis: Investigating the effect of incorrect lighting on the user

Master Thesis: Frame extrapolation to enhance rendering framerate

Bachelor Thesis: The grid processing library

Scalar, vector, tensor and higher-order fields are commonly used to represent scientific data in various disciplines including geology, physics and medicine. Although standards for storage of such data exists (e.g. HDF5), almost every application has its custom in-memory format. The core idea of this engineering-oriented work is to develop a library to standardize in-memory representation of such fields, and providing functionality for (parallel) per-cell and per-region operations on them (e.g. computation of gradients/Jacobian/Hessian). Contact: Ali Can Demiralp, M. Sc.

Bachelor Thesis: Scalar and vector field compression for GPUs based on ASTC texture compression [in progress]

Scalar and vector fields are N-dimensional, potentially non-regular, grids commonly used to store scientific data. Adaptive Scalable Texture Compression (ASTC) is a lossy block-based texture compression method, which covers the features of all texture compression approaches to date and more. The limited memory space of GPUs pose a challenge to interactive compute and visualization on large datasets. The core idea of this work is to explore the potential uses of ASTC for compression of large 2D/3D scalar and vector fields, attempting the minimize and bound the errors introduced by lossiness. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: The multi-device ray tracing library

There are various solutions for ray tracing on CPUs and GPUs today: Intel Embree for shared parallelism on the CPU, Intel Ospray for distributed parallelism on the CPU, NVIDIA OptiX for shared and distributed parallelism on the GPU. Each of these libraries have their pros and cons. Intel Ospray scales to distributed settings for large data visualization, however is bound by the performance of the CPU which is subpar to the GPU for the embarassingly-parallel problem of ray tracing. NVIDIA OptiX provides a powerful programmable pipeline similar to OpenGL but is bound by the memory limitations of the GPU. The core idea of this engineering-oriented work is to develop a library (a) enabling development of ray tracing algorithms without explicit knowledge of the device the algorithm will run on, (b) bringing ease-of-use of Intel Ospray and functional programming concepts of NVIDIA OptiX together. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Numerical relativity library

Numerical relativity is one of the branches of general relativity that uses numerical methods to analyze problems. The primary goal of numerical relativity is to study spacetimes whose exact form is not known. Within this context the geodesic equation generalizes the notion of a straight line to curved spacetime. The core idea of this work is to develop a library for solving the geodesic equation, which in turn enables 4-dimensional spacetime ray tracing. The implementation should at least provide the Schwarzschild and Kerr solutions to the Einstein Field Equations, providing visualizations of non-rotating and rotating uncharged black holes. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Mean curvature flow for truncated spherical harmonics expansions

Curvature flows produce successively smoother approximations of a given piece of geometry, by reducing a fairing energy. Within this context, mean curvature flow is a curvature flow defined for hypersurfaces in a Riemannian manifold (e.g. smooth 3D surfaces in Euclidean space), which emphasizes regions of higher frequency and converges to a sphere. Truncated spherical harmonics expansions are commonly used to represent scientific data as well as arbitrary geometric shapes. The core idea of this work is to establish the mathematical concept of mean curvature flow within the spherical harmonics basis, which is empirically done through interpolation of the harmonic coefficients to the coefficient 0,0. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Orientation distribution function topology

Topological data analysis methods have been applied extensively to scalar and vector fields for revealing features such as critical and saddle points. There is recent effort on generalizing these approaches to tensor fields, although limited to 2D. Orientation distribution functions, which are the spherical analogue to a tensor, are often represented using truncated spherical harmonics expansions and are commonly used in visualization of medical and chemistry datasets. The core idea of this work is to establish the mathematical framework for extraction of topological skeletons from an orientation distribution function field. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Variational inference tractography

Tractography is a method for estimation of nerve tracts from discrete brain data, often obtained through Magnetic Resonance Imaging. The family of Markov Chain Monte Carlo (MCMC) methods form the current standard to (global) tractography, and have been extensively researched to date. Yet, Variational Inference (VI) methods originating in Machine Learning provide a quicker alternative to statistical inference. Stein Variational Gradient Descent (SVGD) is one such method which not only extracts minima/maxima but is able to estimate the complete distribution. The core idea of this work is to apply SVGD to tractography, working with both Magnetic Resonance and 3D-Polarized Light Imaging data. Contact: Ali Can Demiralp, M. Sc.

Master Thesis: Block connectivity matrices

Connectivity matrices are square matrices for describing structural and functional connections between distinct brain regions. Traditionally, connectivity matrices are computed for segmented brain data, describing the connectivity e.g. among Brodmann areas in order to provide context to the neuroscientist. The core idea in this work is to take an alternative approach, dividing the data into a regular grid and computing the connectivity between each block, in a hierarchical manner. The presentation of such data as a matrix is non-trivial, since the blocks are in 3D and the matrix is bound to 2D, hence it is necessary to (a) reorder the data using space filling curves so that the spatial relationship between the blocks are preserved (b) seek alternative visualization techniques to replace the matrix (e.g. volume rendering). Contact: Ali Can Demiralp, M. Sc.

Bachelor Thesis: Lip Sync in Unreal Engine 4 [in progress]

Computer-controlled, embodied, intelligent virtual agents are increasingly often embedded in various applications to enliven the virtual sceneries. Thereby, conversational virtual agents are of prime importance. To this end, adequate mimics and lip sync is required to show realistic and plausible talking characters. The goal of this bachelor thesis is to enable an effective however easy-to-integrate lip sync in our Unreal projects for text-to-speech input as well as recorded speech. Contact: Jonathan Ehret, M.Sc.

Master Thesis: Meaningful and Self-Reliant Spare Time Activities of Virtual Agents

Bachelor Thesis: Joining Social Groups of Conversational Virtual Agents

Bachelor Thesis: Integrating Human Users into Crowd Simulations

Bachelor Thesis: Supporting Scene Exploration in the Realm of Social Virtual Reality

Master Thesis: Efficient Terrain Rendering for Virtual Reality Applications with Level of Detail via Progressive Meshes

Terrain rendering is a major and widely researched field. It has a variety of applications, from software that allows the user to interactively explore a surface, like NASA World Wind or Google Earth, over flight simulators to computer games. It is not surprising that terrain rendering is also interesting for virtual reality applications as virtual reality can also be a tool to support the solution of difficult problems by providing natural ways of interaction. In combination virtual reality and terrain rendering can combine their solution promoting potential. In this work the terrain representing LOD structure will be a Progressive Mesh (PM) constructed from an unconnected point cloud. A preprocessing step first connects the point cloud so that it forms a triangle mesh which approximates the underlying surface. Then the mesh can be decimated so that the data volume of the PM can be reduced if needed. When the mesh has the desired complexity the actual Progressive Mesh structure is build. Afterwards, real-time rendering just needs to read the PM structure from a file and can perform a selective refinement on it to match the current viewer position and direction. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Voxel-based Transparency: Comparing Rasterization and Volume Ray Casting

This thesis deals with a performance visualization scheme, which represents the load factor of single threads on a high performance computer through colored voxels. The voxels are arranged in a three-dimensional grid so only the shell of the grid is initially visible. The aim of this thesis is to introduce transparent voxels to the visualization in order to let the user look also into the inside of the grid and thereby display more information at once. First, an intuitive user interface for assigning transparencies to each voxel is presented. For the actual rendering of transparent voxels, two different approaches are then examined: rasterization and volume ray casting. Efficient implementations of both transparency rendering techniques are realized by exploiting the special structure of the voxel grid. The resulting algorithms are able to render even very large grids of transparent voxels in real-time. A more detailed comparison of both approaches eventually points out the better suited of the two methods and shows to what extent the transparency rendering enhances the performance visualization. Contact: Prof. Dr. Tom Vierjahn

Bachelor Thesis: Designing and Implementing Data Structures and Graphical Tools for Data Flow Networks Controlling Virtual Environments

In this bachelor thesis a software tool for editing graphs is designed and implemented. There exist some interactive tools but this new tool is specifically aimed at creating, editing and working with graphs representing data flow networks like they appear in virtual environments. This thesis compares existing tools and documents the implementation process of VistaViz (the application). In the current stage of development, VistaViz is able to create new graphs, load existing ones and make them editable interactively. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Raycasting of Hybrid Scenes for Interactive Virtual Environments

Scientific virtual reality applications often make use of both geometry and volume data. For example in medical applications, a three dimensional scan of the patient such as a CT scan results in a volume dataset. Ray casting could make the algorithms needed to handle these hybrid scenes significantly simpler than the more traditional rasterizing algorithms. It is a very flexible and powerful way of generating images of virtual environments. Also there are many effects that can be easily realized using ray-based algorithms such as shadows and ambient occlusion. This thesis describes a ray casting renderer that was implemented in order to measure how well a ray casting based renderer performs and if it is feasible to use it to visualize interactive virtual environments. Having a performance baseline for an implementation of a modern ray caster has multiple advantages. The renderer itself could be used to measure how different techniques could improve the performance of the ray casting. Also with such a renderer it is possible to test hardware. This helps to estimate how much the available hardware would have to improve in order to make ray casting a sensible choice for rendering virtual environments. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: CPU Ray-Tracing in ViSTA

Scientific data visualization is an inherent tool in modern science. Virtual reality (VR) is one of the areas where data visualization constitutes an indispensable part. Current advances in VR as well as the growing ubiquitousness of the VR tools bring the necessity to visualize large data volumes in real time to the forefront. However, it also presents new challenges to the visualization software used in high performance computer clusters. CPU-based real-time rendering algorithms can be used in such visualization tasks. However, they only recently started to achieve real-time performance, mostly due to the progress in hardware development. Currently ray tracing is one of the most promising algorithms for CPU-based real-time rendering. This work aims at studying the possibility to use CPU-based ray tracing in VR scenarios. In particular, we consider the CPU-based rendering algorithm implemented in the Intel OSPRay framework. For VR tasks, the ViSTA Virtual Reality Toolkit, developed at the Virtual Reality and Immersive Visualization Group at RWTH Aachen University is used. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Streaming Interactive 3D-Applications to Web Environments

This thesis develops a framework for streaming interactive 3D-applications to web environments. The framework uses a classical client-server architecture where the client is implemented as a web application. The framework aims at providing a flexible and scalable solution for streaming an application inside a local network as well as remotely over the internet. It supports streaming to multiple clients simultaneously and provides solutions for handling the input of multiple users as well as streaming at multiple resolutions. Its main focus lies on reducing the latency perceived by the user. The thesis evaluates the image-based compression standards JPEG and ETC as well as the video-based compression standards H.264 and H.265 for use in the framework. The communication between the client and the server was implemented using standardized web technologies such as WebSockets and WebRTC. The framework was integrated into a real-world application and was able to stream it locally and remotely achieving satisfying latencies. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Benchmarking interactive Rendering Frameworks for virtual Environments

In recent years virtual reality applications that utilize head mounted displays have become more popular due to the release of head mounted displays such as the Oculus Rift and the HTC Vive. Applications that utilize head mounted displays, however, require very fast rendering algorithms. Traditionally, the most common way to achieve real time rendering is triangle rasterization; another approach is ray tracing. In order to provide insight into the performance behavior of rasterization and raytracing, in this Master thesis a toolkit for benchmarking the performance of different rendering frameworks under different conditions was implemented. It was used to benchmark the performance of CPU-ray-tracing-based, GPU-ray-tracing-based and rasterization-based rendering in order to identify the influence of different factors on the rendering time for different rendering schemes. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: Generating co-verbal Gestures for a Virtual Human using Recurrent Neural Networks [in progress]

Master Thesis: Extraction and Interactive Rendering of Dissipation Element Geometry – A ParaView Plugin

After approximate Dissipation Elements (DE) were introduced by Vierjahn et al., the goal of this work is to make their results available for users in the field of fluid mechanics in form of a ParaView plugin, a standard software in this field. It allows to convert DE trajectories into the approximated, tube- like, form and render it via ray tracing and classic OpenGL rendering. The results are suggesting it is ready to be tested by engineers working with ParaView, as interactive frame rates and fast loading times are achieved. By using approximated DEs instead of DE trajectories, significant amounts of data storage can be saved. Contact: Prof. Dr. Tom Vierjahn

Bachelor Thesis: Comparison and Evaluation of two Frameworks for in situ Visualization

On the road to exa-scale computing, the widening gap between I/O-capabilities and compute power of today’s compute clusters encourages the use of in situ methods. This thesis evaluates two frameworks designed to simplify in situ coupling, namely SENSEI and Conduit, and compares them in terms of runtime overhead, memory footprint, and implementation complexity. The frameworks were used to implement in situ pipelines between a proxy simulation and an analysis based on the OSPRay ray tracing framework. The frameworks facilitate a low-complexity integration of in situ analysis methods, providing considerable speedups with an acceptable memory footprint. The use of general-purpose in situ coupling frameworks allows for an easy integration of simulations with analysis methods, providing the advantages of in situ methods with little effort. Contact: Prof. Dr. Tom Vierjahn

Master Thesis: An Intelligent Recommendation System for an Efficient and Effective Control of Virtual Agents in a Wizard-of-Oz paradigm.

In this work, techniques were studied to control virtual agents embedded as interaction partners in immersive, virtual environments. He implemented a graphical user interface (GUI) for a Wizard-of-Oz paradigm, allowing to select and control individual virtual agents manually. The key component of the GUI is an intelligent recommendation system predicting which virtual agents are very likely to be the next interaction partners based on the user’s actions in order to allow an efficient and effective control. Published as poster at VRST 2017. Contact: Andrea Bönsch, M. Sc.

Master Thesis: Automatic Virtual Tour Generation for Immersive Virtual Environments based on Viewpoint Quality

The exploration of a virtual environment is often the first and one of the most important actions a user performs when experiencing it for the first time, as knowledge of the scene and a cognitive map of the environment are prerequisites for many other tasks. However, as the user does not know the environment, their exploration path is likely to be flawed, taking longer than necessary, missing important parts of the scene and visiting other parts multiple times by accident. This can be remedied by virtual tours that provide an efficient path through the environment that visits all important places. However, for most virtual environments, manually created virtual tours are not available. Furthermore, most scenes are not provided with the information of where the most important locations are, such that automatic generation of good virtual tours is challenging. However, the informativeness of a position in a virtual environment can be computed automatically using viewpoint quality estimation techniques. In addition to providing interesting places as waypoints, this concept also allows the evaluation of the quality of the tour between waypoints. Therefore, in this thesis, an automatic method to compute efficient and informative virtual tours through a virtual scenery is designed and developed, based on an evolutionary approach that aims at maximizing the quality of the viewpoints encountered during the tour. Contact: Dr. Sebastian Freitag

Master Thesis: Fluid Sketching - 3D Sketching Based on Fluid Flow in Immersive Virtual Environments

Bachelor Thesis: Conformal Mapping of the Cortical Surface

The cerebral cortex holds most of the cerebrum’s functional processing ability. To visualize functional areas on the cortical surface, the cortical surface is usually mapped to a representation which makes the convoluted areas of the brain visible. This work focuses on mapping the surface into the 2D domain. For this purpose, two parameterization algorithms have been implemented: Linear Angle Based Parameterization (Zayer et al., 2007) and Least Squares Conformal Maps (Lévy et al., 2002). The results of the two algorithms are then compared to the iterative flattening approach by Fischl et al. regarding computational time and introduced distortions. Contact: Dr. Claudia Hänel