phd thesis visual analytics

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• Brian Keith Norambuena , "Narrative Maps: A Computational Model to Support Analysts in Narrative Sensemaking", July 2023. https://vtechworks.lib.vt.edu/handle/10919/116006 Assistant Professor, Department of Computing and Systems Engineering, Universidad Católica del Norte in Antofagasta, Chile. https://briankeithn.github.io
• Yali Bian , "Human-AI Sensemaking with Semantic Interaction and Deep Learning", Feb 2022. https://vtechworks.lib.vt.edu/handle/10919/109213 Senior Research Scientist, Intel Labs. https://www.yalibian.com
• Mai Dahshan , "Visual Analytics for High Dimensional Simulation Ensembles", May 2021 (co-advised with Nicholas Polys). Assistant Professor, School of Computing, University of North Florida. https://www.linkedin.com/in/m-dahshan
• Tianyi Li , "Solving Mysteries with Crowds: Supporting Crowdsourced Sensemaking with a Modularized Pipeline and Context Slices", June 2020. https://vtechworks.lib.vt.edu/handle/10919/99937 Assistant Professor, Department of Computer and Information Technology, Purdue University. https://polytechnic.purdue.edu/profile/li4251
• Moeti Masiane , "Insight Driven Sampling for Interactive Data Intensive Computing", May 2020. https://vtechworks.lib.vt.edu/handle/10919/107087 Technical Analyst, Department of Defense.
• Michelle Dowling , "Semantic Interaction for Symmetrical Analysis and Automated Foraging of Documents and Terms", May 2020. https://vtechworks.lib.vt.edu/handle/10919/104682 Data Scientist, Pacific Northwest National Laboratory. https://www.linkedin.com/in/michelle-dowling-91410a79/
• John Wenskovitch , "Dimension Reduction and Clustering for Interactive Visual Analytics", August 2019. https://vtechworks.lib.vt.edu/handle/10919/96599 Research Scientist, Pacific Northwest National Laboratory. http://johnwenskovitch.com
• Caleb Reach , "Smooth Interactive Visualization", July 2017. https://vtechworks.lib.vt.edu/handle/10919/78848 Google.
• Maoyuan Sun , "Visual Analytics with Biclusters: Exploring Coordinated Relationships in Context", August 2016. https://vtechworks.lib.vt.edu/handle/10919/72890 Assistant Professor, Department of Computer Science, Northern Illinois University. http://faculty.cs.niu.edu/~smaoyuan/
• Jessica Zeitz Self , "Designing and Evaluating Object-Level Interaction to Support Human-Model Communication in Data Analysis", May 2016. https://vtechworks.lib.vt.edu/handle/10919/70950 Associate Professor, Department of Computer Science, University of Mary Washington. http://www.umw.edu/directory/employee/jessica-self-jzeitz/
• Lauren Bradel , "Multi-Model Semantic Interaction for Scalable Text Analytics", May 2015. http://vtechworks.lib.vt.edu/handle/10919/52785 Department of Defense.
• Haeyong Chung , "Designing Display Ecologies for Visual Analysis", May 2015. http://infovis.cs.vt.edu/publications/designing-display-ecologies-visual... Associate Professor, Department of Computer Science, University of Alabama in Huntsville. http://www.cs.uah.edu/~hchung/hchung.html
• Alex Endert , "Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering", July 2012. [IEEE VGTC Dissertation Award 2013] http://scholar.lib.vt.edu/theses/available/etd-07112012-123927/ Associate Professor, School of Interactive Computing, Georgia Tech. http://www.cc.gatech.edu/~aendert3/
• Christopher Andrews , “Space to Think: Sensemaking and Large, High-Resolution Displays”, August 2011. http://scholar.lib.vt.edu/theses/available/etd-08232011-160350/ Associate Professor, Department of Computer Science, Middlebury College. http://www.cs.middlebury.edu/~candrews/
• Tao Ni , “A Framework of Freehand Gesture Interaction: Techniques, Guidelines, and Applications”, August 2011 (co-advised with Doug Bowman). http://scholar.lib.vt.edu/theses/available/etd-09212011-230923/ Founder, Sproutup.co. https://www.linkedin.com/in/tao-ni-design/
• Beth Yost , “The Visual Scalability of Integrated and Multiple View Visualizations for Large, High Resolution Displays”, May 2007. http://scholar.lib.vt.edu/theses/available/etd-04182007-143033/ Principal Human-Centered Engineer, MITRE Corporation.
• Robert Ball , “Effects of Large, High-Resolution Displays for Geospatial Information Visualization”, August 2006. http://scholar.lib.vt.edu/theses/available/etd-08252006-103122/ Associate Professor, Department of Computer Science, Weber State University. http://icarus.cs.weber.edu/~rball/
• Glenn Fink , “Visual Correlation of Network Traffic and Host Processes for Computer Security”, August 2006. https://vtechworks.lib.vt.edu/handle/10919/28770 Senior Research Scientist, Pacific Northwest National Laboratory. https://www.linkedin.com/in/glennfink/
• Nicholas Polys , “Display Techniques in Information-Rich Virtual Environments”, August 2006, (co-advised with Doug Bowman). http://scholar.lib.vt.edu/theses/available/etd-06152006-024611/ Director of Visual Computing, Virginia Tech. http://people.cs.vt.edu/npolys/
• Purvi Saraiya , “Insight-Based Studies for Pathway and Microarray Visualization Tools”, August 2006. http://scholar.lib.vt.edu/theses/available/etd-07092006-170904/ Program Manager, Microsoft. https://www.linkedin.com/in/purvi-saraiya-27441048/

Details found at: https://vtechworks.lib.vt.edu/handle/10919/11041

InfoVis Taxomony:

• Daniel Enriquez, "Investigating Asymmetric Collaboration and Interaction in Immersive Environments", Dec 2023. https://www.danielenriquez.com/
• Elizabeth Christman, "2D Jupyter: Design and Evaluation of 2D Computational Notebooks", May 2023. https://vtechworks.lib.vt.edu/handle/10919/115412
• Sahil Hamal, "Interpreting Dimension Reductions through Gradient Visualization", May 2023. https://vtechworks.lib.vt.edu/handle/10919/115225
• Huimin Han, "Explainable Interactive Projections for Image Data", December 2022. https://vtechworks.lib.vt.edu/handle/10919/113157
• Mia Taylor, "Andromeda in Education: Studies on Student Collaboration and Insight Generation with Interactive Dimensionality Reduction", October 2022. https://vtechworks.lib.vt.edu/handle/10919/112073
• Han Liu, "Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations", October 2022. https://vtechworks.lib.vt.edu/handle/10919/111975
• Payel Bandyopadhyay, "Immersive Space to Think: the Role of 3D Immersive Space in Sensemaking of Textual Data", July 2020. https://vtechworks.lib.vt.edu/handle/10919/108232
• Ming Wang, “Bridging Cognitive Gaps Between User and Model in Interactive Dimension Reduction”, May 2020. https://vtechworks.lib.vt.edu/handle/10919/106390
• Sidney Holman, “Entropy and Insight: Exploring how information theory can quantify sensemaking in visual analytics”, June 2018. https://vtechworks.lib.vt.edu/handle/10919/83821
• Adam Binford, “A Bidirectional Pipeline for Semantic Interaction in Visual Analytics”, August 2016. https://vtechworks.lib.vt.edu/handle/10919/72981
• Xin Chen, “Be the Data: Embodied Visual Analytics”, August 2016. (Co-advised with Leanna House) https://vtechworks.lib.vt.edu/handle/10919/72287
• Peng Mi, “GPU Based Methods for Interactive Information Visualization of Big Data”, December 2015. (Co-advised with Yong Cao) https://vtechworks.lib.vt.edu/handle/10919/64473
• Andre Esakia, “Large Display Interaction via Multiple Acceleration Curves on a Touchpad”, December 2013. https://vtechworks.lib.vt.edu/handle/10919/76941
• Kevin Logan, "SpatialHistory: Using Spatial Memory to Recall Information", Dec 2012. https://vtechworks.lib.vt.edu/handle/10919/19211
• Ji Wang, “Clustered Layout Word Cloud for User Generated Online Reviews”, May 2012. https://vtechworks.lib.vt.edu/handle/10919/19193
• Patrick Fiaux, “Solving Intelligence Analysis Problems using Biclusters”, Jan 2012. https://vtechworks.lib.vt.edu/handle/10919/31293
• David Machaj, “Co-Located Many-Player Gaming on Large High-Resolution Displays”, May 2009. https://vtechworks.lib.vt.edu/handle/10919/32764
• Sarah Peck, “A Multiscale Interaction Technique for Large, High-Resolution Displays”, May 2008. https://vtechworks.lib.vt.edu/handle/10919/42863
• Mehmet Celal Dasiyici, “Multi-Scale Cursor : Optimizing Mouse Interaction for Large Personal Workspaces”, May 2008. https://vtechworks.lib.vt.edu/handle/10919/32706
• Lauren Shupp, “The Effects of Curving Large, High-Resolution Displays on User Performance”, August 2006. [Outstanding Master’s Thesis Award by the VT Computer Science Department, May 2007] [nominated by Virginia Tech for the Conference of Southern Graduate Schools 2007 Innovative Application of Technology in a Master's Thesis Award.] https://vtechworks.lib.vt.edu/handle/10919/34294
• Sujatha Krishnamoorthy, “Designing Interactive Visualizations for First-time Novice Users”, December 2005. https://vtechworks.lib.vt.edu/handle/10919/46174
• Chris Catanzaro, “Vizability: Visualizing Usability Evaluation Data Based on the User Action Framework”, Spring 2005. https://vtechworks.lib.vt.edu/handle/10919/32575
• Kiran Indukuri, “Fusion: A Visualization Framework for Interactive Rule Mining with Applications to Bioinformatics”, Fall 2004. https://vtechworks.lib.vt.edu/handle/10919/36326
• John Costigan, “Applying Information Visualization Techniques to Visual Debugging”, Spring 2003. https://vtechworks.lib.vt.edu/handle/10919/33633
• Varun Saini, “Visualization Schemas: A User Interface Extending Relational Data Schemas for Flexible, Multiple-View Visualization of Diverse Databases”, Spring 2003. https://vtechworks.lib.vt.edu/handle/10919/32621
• Sanjini Jayaraman, “PolarEyez: A Radial Focus+Context Visualization for Multidimensional Functions”, Fall 2002. https://vtechworks.lib.vt.edu/handle/10919/31034
• Nathan Conklin, “A web-based, run-time extensible architecture for interactive visualization and exploration of diverse data”, Fall 2002. [Received the “Outstanding Graduate Research” award from the Computer Science Department, 2002.] https://vtechworks.lib.vt.edu/handle/10919/35998

Details available at: https://vtechworks.lib.vt.edu/handle/10919/9291

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PhD Position in AI and Visual Analytics for Multi-Modal Summarization

University of Amsterdam

• Department: Faculty of Science
• City: Amsterdam
• Country: Netherlands
• Posted on: Friday, 08 December 2023
• Application Deadline: Thursday, 01 February 2024

Job Description

Are you interested in performing cutting-edge research in Artificial Intelligence (AI) and visual analytics for social good? Visual analytics is the science of making sense of data using visualization and modeling techniques. The Informatics Institute at the University of Amsterdam is looking for an ambitious PhD student to integrate AI and visual analytics in summarizing multi-modal data in the public health domain. Your research is part of the Multimedia Analytics (MultiX) lab with a strong focus on dealing with multi-modal data.

Nowadays, there is a lot of multi-modal data related to public health in local regions, such as citizen reports/complaints, social media streams, camera monitoring footage, and environmental sensor readings. There is a strong need to build AI-driven visual analytics tools that can summarize these data to help local stakeholders understand the patterns and events that are currently happening, such as air pollution, disease outbreaks, and flooding. Besides, summarization can be used to encourage citizens to monitor their surroundings, which can lead to a sense of community, autonomy, and empowerment. For example, when citizens receive an event notification via apps/emails, they can click on the notification to go to an AI-driven visual analytics tool that shows the summary with different ways for users to provide feedback interactively.

This research topic poses at least the following challenges:

• Summarizing evolving multi-modal data is difficult.  Public health data are often multi-modal. Traditional techniques typically use different pipelines for different types of data, making it difficult to connect findings. Moreover, public health data is dynamic and evolving, which means the patterns and events can shift quickly over time (i.e., concept drift). Most techniques deal with a fixed dataset, which may not be suitable for evolving data.
• Improving summarization based on stakeholder feedback is hard.  Public health data depends on context. Thus, the summarization model must consider knowledge and feedback from stakeholders. However, stakeholders can provide different feedback interactively (e.g., labels, comparisons, scalars, rankings, corrections, natural language). It is hard to let the system learn from those and have the right balance between their richness and efficiency.
• Representing diverse stakeholder values is challenging.  Public health data can be biased due to limitations in sampling strategies or stakeholders’ prior beliefs. Moreover, stakeholders can have misaligned and even conflicting values when providing feedback. There is a need to provide bias and fairness measurements for the patterns and events in the summarization for laypeople who have limited technical backgrounds.

What are you going to do?

You will conduct research, experiments, and empirical studies to address the challenges that are mentioned above (or other related challenges).

Your tasks and responsibilities:

• Conduct research and experiments in integrating AI and visual analytics to summarize evolving multi-modal data;
• Create and deploy a web-based visual analytics tool that can show the summarized insights to stakeholders and enable them to provide different forms of feedback interactively;
• Conduct research and experiments in investigating human-in-the-loop AI techniques to improve the summarization model using continuous stakeholder feedback;
• Inspect how AI and visual analytics can be used to help stakeholders identify bias and fairness issues in the summarization;
• Conduct empirical studies using a mix-method approach (i.e., both qualitative and quantitative) to evaluate the visual analytics tool with stakeholders;
• Publish and present research in international peer-reviewed conferences (e.g., ACM WWW, AMC MM, IEEE VIS, ACM CHI, ACM KDD, ACM IUI, AAAI) and/or journals (e.g., ACM TIIS, IEEE TVCG);
• Pursue and complete a PhD thesis within the appointed duration of four years;
• Assist in teaching activities, such as teaching labs/tutorials in courses and supervising bachelor/master students;
• Carry out administrative tasks in the research group, such as scheduling and planning activities for group meetings.

What do you have to offer?

Your experience and profile:

• A relevant master’s degree to the PhD topic of interest;
• Research experiences in artificial intelligence, visual analytics, or related topics;
• Solid programming skills with experience using Python and machine learning frameworks;
• The willingness to work collaboratively with other researchers and external stakeholders;
• Professional command of English (both verbal and written).

Our ideal candidate has an artificial intelligence and/or visual analytics background. It is a preference if you additionally speak professional Dutch, have experience in developing/deploying web-based applications, or have co-designed tools with stakeholders.

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is as soon as possible. This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The gross monthly salary, based on 38 hours per week, ranges between € 2,770 in the first year to € 3,539 in the last year (scale P). UvA additionally offers an extensive package of secondary benefits, including 8% holiday allowance and a year-end bonus of 8.3%. The UFO profile PhD Candidate is applicable. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The  Collective Labour Agreement of Universities of the Netherlands  is applicable.

Besides the salary and a vibrant and challenging environment at Science Park we offer you multiple fringe benefits:

• 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January;
• multiple courses to follow from our Teaching and Learning Centre;
• a complete educational program for PhD students;
• multiple courses on topics such as leadership for academic staff;
• multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses;
• 7 weeks birth leave (partner leave) with 100% salary;
• partly paid parental leave;
• the possibility to set up a workplace at home;
• a pension at ABP for which UvA pays two third part of the contribution;
• the possibility to follow courses to learn Dutch;
• help with housing for a studio or small apartment when you’re moving from abroad.

Are you curious to read more about our extensive package of secondary employment benefits, take a look  here .

The  University of Amsterdam  (UvA) is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 42,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

The  Faculty of Science  (FNWI) has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the  Informatics Institute  (IvI) is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.

The  Multimedia Analytics Lab Amsterdam  (MultiX) performs research on multimedia analytics by developing AI techniques for getting the richest information possible from the data, visualizations, and interactions surpassing human and machine intelligence. We blend multi-modal data in effective interfaces for applications and social impact in public health, forensics and law enforcement, cultural heritage, and data-driven business.

Want to know more about our organisation? Read more about  working at  the University of Amsterdam.

Any questions?

Do you have any questions or do you require additional information? Please contact:

• E:  Yen-Chia Hsu , Assistant Professor
• E:  Marcel Worring , Full Professor

Application Instructions

If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. You can apply online via the button below. We accept applications until and including 1 February 2023.

Applications should include the following information (all files apart from your CV should be submitted in  one single pdf file ):

• A detailed CV including the months (not just years) when referring to your education and work experience;
• A letter of motivation (at most two pages) explaining why you are interested in this position, how your experience fits into this position, and how you would approach the PhD project;
• A complete transcript of records for all university Bachelor and Master courses that you have taken (including grades and explanation of the grading system);
• A link to your master’s thesis if it is available online (else, please include an abstract);
• A list of projects or publications you have worked on, with brief descriptions about them and your contributions (at most one page);
• A link to one writing sample available online (e.g., in the university library, open online repository, Google Drive), such as your master’s thesis, term paper, or publication.

We do not ask for a referee list or reference letters in the initial application (please do not include them in your application document), but we may ask for them at the later stage of the interview.

Please make sure to provide ALL requested documents mentioned above. You can use the CV field to upload your resume as a separate pdf document. Use the Cover Letter field to upload the other requested documents, including the motivation letter, as  one single pdf file .

A knowledge security check may be part of the selection procedure. (for details:  National knowledge security guidelines ) .

Only complete applications received within the response period via the link below will be considered. Please don’t send any applications by email.

We will invite potential candidates for interviews soon after the expiration of the vacancy.

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A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data

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Visual Computing BLOG

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Visual Analytics of Spatial Events: Methods for the Interactive Analysis of Spatio-Temporal Data Abstractions

I’m happy to share that I’ve successfully defended my Ph.D. thesis with the title “Visual Analytics of Spatial Events: Methods for the Interactive Analysis of Spatio-Temporal Data Abstractions”.

Technological advances, especially in remote sensing, GPS sensors, and computer vision and camera-based tracking, now allow for a collection of spatio-temporal data on an unprecedented scale. These massive datasets raise the problem of how subject matter experts can derive useful knowledge from them and how these datasets can be visualized without leading to overcrowded and cluttered displays. For that, suitable data abstractions are required on the one hand and the integration of subject matter experts in the analysis process instead of solely relying on automatic methods on the other hand.

My dissertation addresses both of the aforementioned problems two-fold: First, spatial events, which are objects with a limited temporal existence with an additional associated spatial position, are identified as a suitable data abstraction for visualization and further analysis. Additionally, complex spatial events are introduced, which occur in domains where events not only have a spatial and temporal location, but where the events can have semantic interrelationships, have interdependencies with other objects on other objects, or are constricted by outside rules and influences. Finally, Visual analytics is employed to ensure the integration of subject matter experts in the analysis process of (complex) spatial events via a combination of automatic and visual analysis methods with a tight coupling through human interaction.

The suitability of visual analytics to analyze spatial events is successfully demonstrated in a diverse range of domains and the current state of the art is broadened by several contributions:

• Via traditional methods such as glyph-based map representations that are extended to enable the subject matter experts to use their insights from the visualizations to steer the model building process further.
• With completely newly developed methods such as query-by-sketch interfaces based on real-world metaphors that offer model visualizations that enable the subject matter experts to evaluate findings of the underlying models.
• Furthermore, new approaches are presented that enable the users to offload the complexity of defining complex spatial events and query construction unto the system with the help of visual query languages.

Our visual query interface is based on the real-world metaphor of a magnetic tactic board, widely used in practice for team coaching and analysis in team sports including soccer. Users efficiently and effectively specify team situations by dragging & dropping magnets representing players, such as G oalkeeper or D efender, or the ball to pitch positions and simplified sketch interactions. The above example shows two aggregated slow build-up play situations retrieved with our approach. Aggressive playing behavior of the F orward to immediately pressure the defender is clearly visible in both.

You can find my thesis in KOPS: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-2vs55lce7cin0

Daniel Seebacher

Im a postdoctoral researcher at the Data Analysis and Visualization group of Daniel Keim at the University of Konstanz since 2022. My research interests include the visual analysis of spatio-temporal events and their context. Concrete research examples include the analysis of the spread of invasive species, the study of intra-city meteorological phenomena, or the study of actions and interactions in sports such as soccer or tennis.

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Map-based Dashboard for Social Environment Understanding (2022)

Contact: Dr.-Ing. Chenyu Zuo

Electronic Version of PhD Thesis

This thesis aims to support stakeholders in understanding social environments with map-based dashboards, representing an overview of the related factors and their spatial distributions and enabling users to explore for insights. To design effective and efficient dashboards, the author proposed a design framework including five components: design goals, users' cognitive tasks, data, interface, and users' feedback.

Data-driven design and analysis of map-based storytelling (2022)

Contact: Dr.-Ing. Edyta Bogucka

This thesis is dedicated to the development of map-based storytelling. It involves two essential parts of data-driven explorations. The first part explores the most and the least prevalent patterns in map-based storytelling in several representative news media. The second part investigates the practical implications of storytelling theories through hands-on design. These two directions of top-down and bottom-up exploration demonstrate a synergetic effect between cartography and journalism.

Spatial Learning with Mixed Reality-based Navigation (2022)

Contact: Dr.-Ing. Bing, Liu

The emerging mixed reality (MR) is promising for navigation, especially indoor navigation. However, the detrimental effect of navigation apps on spatial learning is criticized. This thesis explores and confirms the possibility to balance navigation efficiency and spatial learning during MR-based navigation from users’ perception, the interface design, and the environment’s influence perspectives.

Social Sensing (2020)

Perception of social-event-induced human behavior from geotagged social media data.

Contact: Dr.-Ing. Ruoxin Zhu

The population coverage of public transit systems is an important indicator of public transit accessibility. Traditionally, the assessment of transit catchment areas is mainly focused on walking as the access mode. The recent emerging dockless shared bikes are widely used for connecting with public transit systems and provide new chances to expand the population coverage of public transit. This project aims to assess how dockless shared bikes could expand the transit catchment areas using massive bike trajectories. To achieve this aim, the project has three objectives: 1) proposing a fast method to generate network-based transit catchment areas for non-motorized transport; 2) proposing methods to measure biking distances from high-detailed bike trajectories; 3) conducting cases studies to evaluate the effectiveness of the proposed methods and discussing policy implications for the planning of public transit and dockless shared bikes.

Modeling of Public Transit Accessibility Driven by Spatial Movement Data (2020)

Contact: Dr. Ing. Diao Lin

Abstract: 

This dissertation focuses on investigating the bike-metro integration using spatial movement data and supporting a systematic assessment of accessibility to public transit. Specifically, it serves three research objectives: 1) exploration of biking distances at individual transit stations from trajectory and smart card data, 2) investigation of transit catchment area to raise the public awareness of the transit accessibility at a general level, and 3) inspection of accessibility constrained by crowdedness at a fine-grained level.

Approaching a collective place definition from street-level images using deep learning methods (2019)

Contact: Dr. Ing. Hao Lyu

This work addresses the challenge of understanding a place in GIScience by investigating visual and spatial (semantic) property in voluntarily collected images with deep learning methods. Two place representations are proposed to unify these properties in a probabilistic perspective of understanding places. The proposed computational models which are based on comparative learning and variational autoencoder are proved to be able to learn the probabilistic place representations from image data.

Visualizing Uncertainty in Reasoning - A Bayesian Network-enabled Visual Analytics Approach for Geospatial Data (2019)

Contact: Dr. Ing. Ekaterina Chuprikova

The growing importance of data-driven science and advances in computational capacity offer new opportunities for the analysis and visualization of geospatial and heterogeneous data. This dissertation addresses the challenges of analytical reasoning under conditions of uncertainty when working with spatial data. It serves three research objectives: (1) to evaluate the feasibility of the Bayesian Network in representing conditional dependencies among heterogeneous spatial data; (2) to implement visual analytics scenarios that can demonstrate human-data discourses; (3) to build a prototype of a Bayesian Network-enabled visual analytical system dedicated to geospatial data classification tasks.

Conflation of Road Networks from Digital Maps (2016)

Contact: Dr. Ing. Andreas Philipp Hackelöer

Road Network Conflation is concerned with finding accurate mappings between geographical structures of road networks. This thesis establishes Road Network Conflation within a taxonomy of georeferencing methods and classifies and compares common approaches to the problem. A novel approach called Iterative Hierarchical Conflation (IHC) is introduced, which systematically accounts for the resolution of ambiguities. The IHC is evaluated using several samples from digital maps of different vendors. Results show that the IHC offers advantages especially in terms of correctness, speed and complexity.

Labeling Spatial Trajectories in Road Network using Probabilistic Graphical Models (2016)

Contact: Dr. Ing. Jian Yang

Labeling spatial trajectories, such as map matching, activity recognition, can ease the utilization of the imprecise and semantic poor spatial trajectories for location-aware applica-tions. This thesis studies the problem from a unified perspective using map matching and taxi status inference. Comprehensive probabilistic models are learned from the training data using a chain structure graphical model with feature selection, which are tested to be effective and feasible on a real world dataset.

Visual Analysis of Large Floating Car Data - A Bridge-Maker between Thematic Mapping and Scientific Visualization (2016)

Contact: Dr. Ing. Linfang Ding

This thesis aims to bridge the gaps between thematic mapping and scientific visualization and to achieve their synergetic effects for the visual analysis of big data. Firstly, a systematical comparative study of thematic cartography and scientific visualization is conducted. The study shows that these two disciplines reveal different visual analytical levels  and are mutually complementary. Next, extensive experiments of visually analyzing massive real-world taxi floating car data (FCD) have been carried out. The experiment results demonstrate that the techniques from thematic mapping and scientific visualization can strongly support users to win insight into the movement data.

Event Cartography: A New Perspective in Mapping (2016)

Contact: Dr.-Ing. Nina Polous

In this research, the concept of mapping goes beyond the principle of mapping an object as a conceptual geographic entity with a distinct spatial, temporal and attributive identity. The main goal is to present a conceptual model for managing geo-knowledge which handles real world dynamisms. It uses a generic event-oriented perspective to implicitly represent causal relationships among different components of a Spatio-Temporal Information System. From this new perspective, the objects in space and time are considered merely as information elements of the events, which are connected to other event elements through internal or external processes.

Dynamics of spatially extended phenomena (2014)

Contact: Dr.-Ing. Stefan Peters

This thesis focuses on the visual exploration of a specific type of moving geoobjects, namely the spatially extended objects or phenomena. Visual analytical approaches are developed and implemented to study the dynamics of the spatio-temporally evolving polygons. The lightning data are chosen as a real-world case. In addition to a generic concept for the movement analysis of spatially extended objects, the thesis put forward a number of synchronized cartographic and non-cartographic visual analytical approaches for the clusters.

Concise Image Maps - A Design Approach (2014)

Contact: Dr.-Ing. Christian E. Murphy

An image map is a composition of remote sensing imagery and cartographic symbolisation. This work revisits the concept of image maps and shows that the conventional two-tiered structure can be extended by the concise image map design, according to which a differentiated visual hierarchy can be established. Therefore, design strategies are developed that address the radiometric design of raster imagery in the same manner as the graphical design of map symbols. User tests evaluate several concise image map design strategies that prove to be more effective and user friendly.

A Congruent Hybrid Model for Conflation of Satellite Image and Road Database (2013)

Contact: Dr.-Ing. Jiantong Zhang

This thesis is devoted to the conflation of two heterogeneous data sources - road vectors and geo-referenced images. The contributions of the Congruent Hybrid Model (CHM) include:1) a linear feature extraction approach, which consists of Elastic Circular Mask (ECM) algorithm and the Genetic Algorithm (GA)-based grouping approach;2) a Sparse Matching Algorithm (SMA) approach; and 3) a performance evaluation of two transformation functions. The CHM model can be used directly in the geovisualization applications, and with some modifications it is also suitable for the classical georeferencing problem.

Nicht-Photorealismus in der Stadtmodellvisualisierung für mobile Nutzungskontexte (2013)

Contact: Dr.-Ing. Mathias Jahnke

Die Visualisierungen dreidimensionaler Stadtmodelle erschließen bisher nicht das Potential, der kombinierten geometrisch semantischen Informationsdarstellung. Der aus dem Nicht-Photorealistischen-Rendering bekannte Ansatz der Informationsreduzierung durch Abstraktion lässt sich mit kartographischen Gestaltungsprinzipien kombinieren und liefert unter Einbeziehung von Nutzerpräferenzen neue Formen der Geo-Informationskommunikation auf der Basis von Stadtmodelldaten.

Enrichment of routing map and its visualization for multimodal navigation (2012)

Contact: Dr.-Ing. Yueqin Zhu

This thesis is dedicated to a novel approach for the design of customized routing maps which demonstrate the route along with the fast rendering of a right amount of routing-relevant information that matches the cognitive capacity of the user on route. Given a route with mono- or multimodality, the proposed approach first estimates the salience of individual mapping objects by combining the passive salience from internal characteristic of spatial data and the active salience from the participant. The achieved results are visualized in multi-scale routing map with a dynamic labeling algorithm. The proposed approach is implemented as web-based services. Its feasibility has been verified in several experiments.

Kartenicons im interkulturellen Vergleich (2011)

Contact: Dr. rer. nat. Stephan Angsüsser

Electronic Version of PhD Theses

Das übergeordnete Ziel dieser Arbeit ist ein Beitrag zur kulturvergleichenden Kartographie. Ausgehend von der Annahme, dass Unterschiede zwischen den Karten verschiedener Kulturen bestehen, ist eine bestimmte Art von Kartenzeichen als Beispiel herangezogen worden. Dabei handelt es sich um Kartenicons, deren Gemeinsamkeit normalerweise in ihrer geringen Größe und weitgehenden Isoliertheit besteht. Zu deren Analyse wurde auf der Basis bestehender Ansätze ein neues Zeichenmodell entwickelt. Für jedes der 1016 Kartenicons (540 deutsche und 476 chinesische) wurden 8 Attribute bestimmt, deren Vergleich schließlich 25 wesentliche Unterschiede zwischen den beiden Länderauswahlen ergab. Um diese zu erklären, wurde versucht, kulturelle Eigenschaften heranzuziehen. Schließlich führte dies zur Formulierung von 38 Hypothesen über mögliche Beziehungen zwischen Eigenheiten der deutschen bzw. chinesischen Kultur und Eigenheiten der in diesen beiden Ländern produzierten Kartenicons.

Kartographische Anreicherung von Gebäudefassaden mit thermalen Bilddaten (2011)

Contact: Dr.-Ing. Holger Kumke

Die vorliegende Arbeit behandelt die visuelle Anreicherung thermaler Daten auf Gebäudefassaden imstädtischen Raum. Wärmestrahlung, messtechnisch als bildhafte Thermogramme erfasst, liefern neuenicht sichtbare Informationen über den Gebäudezustand und dienen als Rohdaten für diekartographische Aufbereitung zu thermalen Fassadenkarten für planare 2D wie auch kartenverwandteräumlich virtuelle 3D Darstellungen. Aus einer Kombination der bekannten Gestaltungsregeln und derneuen kartographischen Perspektive entstanden system-unabhängige thermale Fassadenkarten, dieals Anregung, Denkanstöße und Ausblick auf temperaturbezogene Darstellungsformen im städtischenRaum und Grundlage für weitere Forschungsarbeit dienen sollen.

Distributed geo-services based on Wireless GIS - a case study for post-quake rescue information system (2011)

Contact: Dr.- Ing. Yimei Liu

A useful application of Wireless GIS is the handling of natural disasters such as earthquakes. This thesis is dedicated to the construction and implementation of a post-quake rescue information system based on open source data and software programs. The emphasis is laid on the assessment of losses in the disaster area, estimation of collapsing buildings and trapped population, and efficient transmission of all the rescue-relevant information. The realized workflow of using open source data and software programs to develop distributed geo-services for rescue purposes is independent of official data sources, therefore, flexible enough to react on emergency situations.

Generalization of Road Network for an Embedded Car Navigation System (2011)

Contact: Dr.- Ing. Hongbo Gong

Automatic map generalization serves to reduce the amount of data to speed up the mapping process or to ensure the legibility of small scale maps. This thesis deals with the task of automatic selection of road networks for the application of visualization and route planning in an embedded car navigation system. Based on an intensive analysis of the embedded system in terms of storage capacity, the display screen and the necessary computing power in real time, two special constraints - connectivity and network density – are introduced. A concept for the semantic-driven path selection for the map display and the optimal route planning is developed and implemented with test data from Germany and China.

Data Model and Algorithms for Multimodal Route Planning with Transportation Networks (2011)

Contact: Dr.- Ing. Lu Liu

Determining a best route in highly developed complex transportation networks is not a trivial task, especially for those who are unfamiliar with the local transportation system. Multimodal route planning that aims to find an optimal route between the source and the target of a trip while utilizing several transportation modes is essential to intelligent multimodal navigation services. Although the task originates from the field of transportation, it can be abstracted as a general form independent of the domain-specific details on the underlying data model and algorithms. This research work is therefore dedicated to a general approach of modeling the multimodal network data and performing optimal path queries on it. The weighted digraph structure can well represent the fundamental static networks. For each mode, there is one corresponding mode graph. These graphs constitute the Multimodal Graph Set as a key component of the overall multimodal network data model. In comparison with the traditional mono-modal problem, another key component necessary in the modeling of multimodal route-planning problem is mode-switching actions. In this work, such actions are described by Switch Points which are somewhat analog to plugs and sockets between different mode graphs. Consequently, it is possible to plug-and-play a Multimodal Graph Set by means of Switch Points. On the basis of the multimodal network data model, the multimodal route-planning problem is categorized into two types and formalized as the multimodal shortest path problem on the Plug-and-Play Multimodal Graph Set. It turns out that the solutions for these two types of problem are equivalent if the input mode list for the first type is transformed into its matrix expression. When applying the general multimodal route-planning approach to a specific application domain, a rule-based inferring process is necessary to determine whether a mode sequence is reasonable or not. Performance evaluations on the integrated navigation dataset have verified the efficiency of the proposed approach.

Driver Behaviors on Different Presentation Styles of Traffic Information (2010)

Contact: Dr.- Ing. Masria Mustafa

Road traffic information has been one of the important elements for traffic information systems. The information can be found on the road where Variable Message Sign (VMS) and other platforms have been widely utilized for such application. These overwhelming majorities of traffic information sources provide real-time traffic information, aiming at helping drivers make better decisions on choosing a correct traffic route on the basis of current traffic state. However, it is an unusual sight to view the full scenarios of the system with the view from the drivers themselves. Therefore, smartly presenting the information to the road user has a potential to support the road user to receive and interpret the information in a more effective way. Traffic information presented in different styles may enhance the attractiveness of the information itself. Considering this, we design this study to find out whether there is a refined relationship between the specific presentation style and the driving behavior.  As a starting point, this study tested the assumption that the probe vehicle could provide reliable and sufficient amount of data that could represent travel time information which then can be transformed into various presentation style. VISSIM 5.0 is used to generate travel time data on a hypothetical network. Average travel time on links are analyzed for various percentages of probe vehicles and compared to the ‘true’ average travel time using ‘bootstrapping’ technique. ArcGIS designed for use by transportation professionals to display the results of travel time provided by probe in a more understandable visual fashion (color coded design). Later, user testing upon preference of the drivers towards different types of traffic information presentation style is conducted. A picture is often cited to be worth a thousands words and, for some tasks it is clear that a visual presentation such as map is dramatically easier to be used than other textual or spoken description. Visual displays become even more attractive to provide orientation or context, to enable selection of regions and to provide dynamic feedback for identifying change such as dynamic traffic congestion map. In what concerns the visual information, systems can present information using graphics, symbols or even written messages. A stated preference user test is conducted and questionnaires with different types of traffic information presentation style are distributed to the respondents. An underlying question is basically about whether and how the presentation styles of traffic information affect the driver in making their decision. The study addresses a wide range of alternative styles of in-vehicle traffic information as well as stationary information in different driving scenarios (stop and go and congested). The analysis is carried out which contains various trip variables, including route selection characteristics, travel purpose and actual observable traffic conditions en route such as level of congestion, variables pertaining to the information to which is being displayed and also psychological factors based on personal attributes and the experience of the individual drivers. It is assumed that drivers are influenced by these variables and factors of making decisions whether to acquire and refer to traffic information in choosing their route. Our results revealed that in case of in-vehicle information, presentation style of traffic information does not play a significant role for driver’s behavior. As to the preference of presentation style, ‘map with detail building’ came out to be the highest rank. The main reason for this preference is the presence of the buildings which provides additional orientation information. Different behavior patterns could be observed when confronted with more realistic situations. Our observations demonstrate that the drivers are more likely to divert their route only in rush trip. In case of stationary information, again, we found no evidence that presentation style of traffic information does play a significant role for driver’s behavior. As to the preference of presentation style, ‘combination of graphic and text information’ came out to be the highest rank. Our observations demonstrate that the drivers are more likely to divert their route only in rush trip and congested route.

Integration of time-dependent features within 3D city model (2010)

Contact: Dr.- Ing. Hongchao Fan

This thesis presents an object-oriented event-state spatiotemporal data model for storage and management of both semantic and geometric changes of 3D building objects in a city. The data model is mainly composed of two parts: an event model that describes events happened to building objects; and a hierarchical spatial data model that describes 3D geometries and semantics of building objects including their valid time span. In this way, histories of building objects are modeled. The data model can be “double indexed” by events happened to objects and by objects involved in events. Correspondingly, queries can be triggered by both events and objects. On this base, a set of spatiotemporal queries are proposed. The spatiotemporal data model proposed in this work combines the advantages of event-based model and object-based spatiotemporal data model. On one hand, dynamic processes are modeled as events with their types/classes, locations, time points/durations, modes of the processes, and the involved city objects. On the other hand, the life of an object is represented by a time-ordered sequence of its states and the dynamic processes indicating how the object changes from one state to another. The approach of storing events and city objects separately reveals a number of benefits: (i) the multiple storage due to n-to-m relations among events and objects are avoided, (ii) the spatiotemporal data model is double-indexed. Events and 3D objects can be queried independently and efficiently. In addition, the proposed spatiotemporal data model takes the hierarchy and inherent relations between events and objects into account, so that both events and 3D objects can be represented at different levels of detail. 

Methods and Implementations of Road-Network Matching (2009)

Contact: Dr.-Ing. Meng Zhang

Data matching is one of the fundamental measures that helps make different data sets interoperable. This thesis is devoted to a new contextual matching approach for road networks. This automatic matching process is based on the Delimited-Stroke-Oriented algorithm and flanked by three assistant methodologies: matching guided by 'structure', matching guided by 'semantics', and matching guided by 'spatial index': Being supported by the extendable delimited strokes, network-based matching and the three assistant methodologies, the contextual matching approach is able to handle geometrical, topological and semantic information in a large matching environment and provide a considerably improved matching performance in terms of ‘automatic matching rate and certainty', 'high computing speed', and 'robustness and generic nature'. Due to its large potentials of enriching mega data sets, the contextual matching approach is being commercialized.

Attention-Guiding Geovisualisation: A cognitive approach of designing relevant geographic information (2008)

Contact: Dr. rer. nat. Olivier Swienty

It is a delicate task to design suitable geovisualisations that allow users an efficient visual processingof the depicted geographic information. In digital era, such a design task is subject tothree major challenges: the ever growing amount of geospatial data at various levels of detail,the diversified applications of that data, and the continuously expanding range of display sizes.These challenges are guided by the same cognitive scope. Users face an increasing level ofcognitive workload that has a substantial impact on decision-making while processing complexvisual environments.This work tends to enhance the visualisation of relevant geographic information by proposing aconceptual framework for the development of attention-guiding geovisualisation. The mainchallenge is to stimulate a users decision-making and to reduce the cognitive workload by providinghigh responsiveness in specific visual brain areas that are involved in visual geographicinformation processing. Based on theories and research findings in GIScience and cognitiveneuropsychology the research basis of this work is formed by combining utility and usabilityissues of system engineering.The relevance of information is considered as an utility criterion and its cognitively adequatevisualisation as an usability criterion of a system’s acceptability. To enhance utility, irrelevantinformation is separated from relevant information by implementing relevance as a filter. Toenhance usability the design of attention-guiding geovisualisation is adapted to internal visualcharacteristics of visual information processing.Based on the internal structure of visual information processing and biological mechanismsinvolved in visual attention, appropriate cognitive principles and a design methodology arepresented and applied to pixel-based remote sensing satellite image and vectorised maps. Apre-evaluation with a computational attention-model serves as a knowledge base for designingvectorised attention-guiding geovisualisations that are evaluated with a paper and pencil testand the eye-movement recording method.The evaluation results reveal that the proposed attention-guiding design approach significantlyenhances visual geographic information processing and contribute to the overall acceptabilityof geographic information systems and geovisualisations that are needed for fast and accuratedecision-making processes.

Recognition of 3D Settlement Structure for Generalization (2005)

Contact: M.Sc., M.Tech. Jagdish Lal Raheja

This thesis aims at recognizing 3D settlement structures for automatic generalization, an innovative extension to 2D and their simplification based on scale-spaces. The recognition procedure has been divided into three levels namely micro, meso and macro and is based upon individual buildings, buildings in neighborhood and buildings at cluster level having similar properties such as settlement blocks as well as psychophysically perceived groups. Any of these three levels of structure recognition demands that comprehensive information about the buildings should be known a-priori. These buildings, simple as well as complex, are recognized using an Artificial Neural Network (ANN) in a bottom-up approach. It starts with recognizing ground plans of buildings and which in turn, along with other information, are used to recognize different roof types and finally entire buildings are recognized in a similar way. After building recognition, their structure description has been studied in detail, which gives rise to various measurable parameters of individual as well as buildings in neighborhood. These parameters not only characterize individual building but also many spatial relations among them. Structure recognition at clustered level is studied next and it involves the recognition of group of buildings as a whole. The human visual system can detect many clusters of patterns and significant arrangements of image elements. Perceptual grouping refers to the human visual ability to extract significant image relations from lower-level primitive image features without any knowledge of the image content and group them to obtain meaningful higher-level structure. Various perceptual grouping principles have been applied to identify these clusters of groups. After a comprehensive study of structure recognition, their findings are then applied to 3D generalization. Among the various generalization algorithms such as aggregation, displacement, simplification, exaggeration, typification, aggregation is chosen here as it almost uses most of the results from structure recognition. Various constraints resulting from spatial relations have been already found in 2D aggregation. However, unlike in 2D, where there is only one view, the third dimension leads to many additional views and these different views become the source of additional conflicts. Apart from various views, color, texture and other small parts (window, chimney, balcony etc.) of the building also add to the existing constraints. Various additional rules have been obtained based upon these constraints. These rules along with the results of structure recognition have been used to trigger the aggregation operation.

Mobile Cartography - Concepts for Adaptive Visualisation of Spatial Information on Mobile Devices (2004)

Contact: Dr. rer. nat. Tumasch Reichenbacher

This PhD project developed the theoretical and conceptual framework of mobile cartography. The main focus is on the elaboration of adaptive methods for the visualisation of spatial information for mobile usage, i.e. on mobile devices (e.g. PDA). The starting point for the adaptation is the mobile user, his activities and goals, as well as the situation these three are placed in. Usage scenarios helped to implement a prototype geo-service for mobile users based on open-standard formats such as XML, GML, and SVG, which serves as a proof of concept.

Team Members: John Stasko , Carsten Görg , Zhicheng Liu , Sakshi Pratap , Anand Sainath Alumni: Meekal Bajaj, Alex Humesky, Mohit Jain, Youn-ah Kang, Jaeyeon Kihm, Vasili Pantazopoulos, Neel Parekh, Roger Pincombe, Kanupriya Singhal, Gennadiy Stepanov, Chad Stolper, Xin Sun, Sarah Williams

Last modified: January 20, 2014

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The research unit "Visual Analytics" # 193-07 (Centre for Visual Analytics Science and Technology (CVAST)) is part of Vienna University of Technology (TU Wien) , Faculty of Informatics , Institute of Visual Computing and Human-Centered Technology . CVAST conducts research and provides teaching in Visualization (Information Visualization, Visual Analytics). Read More

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Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review

Jawad ahmed chishtie.

1 Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

2 Institute of Health Services and Policy Research, Canadian Institutes of Health Research, Ottawa, ON, Canada

3 Ontario Neurotrauma Foundation, Toronto, ON, Canada

Jessica Babineau

4 Library & Information Services, University Health Network, Toronto, ON, Canada

Iwona Anna Bielska

5 Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada

6 Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ON, Canada

7 Canadian Institutes of Health Research, Ottawa, ON, Canada

Monica Cepoiu-Martin

8 University of Calgary, Calgary, AB, Canada

Michael Irvine

9 Department of Mathematics, University of British Columbia, Vancouver, BC, Canada

10 British Columbia Centre for Disease Control, Vancouver, BC, Canada

Andriy Koval

11 Department of Health Management and Informatics, University of Central Florida, Orlando, FL, United States

Jean-Sebastien Marchand

12 Universite de Sherbrooke, Quebec, QC, Canada

Luke Turcotte

13 School of Public Health and Health Systems, Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada

14 Canadian Institute for Health Information, Ottawa, ON, Canada

Susan Jaglal

15 Department of Physical Therapy, University of Toronto, Toronto, ON, Canada

Associated Data

Medical Literature Analysis and Retrieval System Online search strategy.

Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR).

The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care.

We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers.

As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019.

Conclusions

VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally.

International Registered Report Identifier (IRRID)

DERR1-10.2196/14019

Introduction

In the first formal use of the term visual analytics (VA) in 2005, Thomas and Cook defined it as the “science of analytical reasoning facilitated by interactive visual interfaces” in their seminal book, Illuminating the Path [ 1 , 2 ]. VA techniques have proven helpful to professionals in gaining insights into the ever-expanding world of large complex datasets and unstructured big health care data [ 3 , 4 ].

Beyond Traditional Statistical Analysis

Although VA moves from traditional to exploratory data analysis, it brings together fields of data processing, management, mining, analysis, information visualization, and human-computer interaction [ 5 - 7 ]. It takes the power of traditional statistical analysis further by promoting an understanding of data with effective visual interfaces [ 1 , 8 ]. Typically, a VA tool uses a dimensional database model, as opposed to a relational database, whereas the analyst uses visual tools to develop interactive graphic displays that can further drill down to help explore and present summarized data [ 3 ]. These techniques offer an edge over traditional statistical analysis, which is limited because of humans being vulnerable to information overload [ 8 , 9 ]. VA tools offer a combination of analytics and the interactive visualization engines [ 10 ]. The analytics engine component involves data storage, transformation, and analysis, whereas the visualization engine provides functionality toward data manipulation and display [ 10 ].

VA techniques in health care also make use of machine learning for mining and automated analysis [ 4 , 11 ]. As a multidisciplinary field, VA is more than data or information visualization; its approach combines analysis, visualization, and human cognition [ 7 , 12 ]. This enables deeper insights for planning interventions through analytical reasoning, taking advantage of human cognition in processing visual representations and human-information interaction [ 10 ]. Interactivity is an important characteristic of VA interfaces, providing decision makers with the ability to explore data from multiple aspects and allowing for meaningful and enhanced visual representations that can be used toward evidence-informed decision making [ 13 ].

Visual Analytics in Health Care: Advantages and Applications

VA is an increasingly popular method for exploring, analyzing, and communicating results from complex big data in health [ 14 ]. Although it is increasingly applied in the clinical sciences, there is a lack of literature synthesizing VA methods, frameworks, and tools in population health and health services research (HSR) [ 15 ]. This is especially important with the rising demand from clinicians, administrators, patients, and policy makers for innovative means to answer complex questions [ 1 , 16 ]. Through this scoping review protocol, we present our methodology for the exploration of VA in the overlapping areas of population health and HSR to address this gap in the literature. The methodology presented will also be useful for future studies replicating similar concepts and for conducting reviews on related topics.

Given their high volume, variety, and velocity, much of public health data can be categorized as big data [ 10 ]. Ola and Sedig point to 4 major advantages of how VA can meet the needs of diverse users in public health, which can be extended to population health and HSR. These include overall flexibility to select the most suited visualization form, interaction control with data and information, nonlinear exploratory analysis, ability to provide tailored reports according to various audiences, and task adjustment for advanced and nonadvanced users alike [ 10 ].

One of the primary aims of population health and HSR is to better understand disease distribution and barriers to equitable care. Defined as “research with the goal of improving the efficiency and effectiveness of health professionals and the health care system [ 17 ],” HSR encompasses a large area of research. The concepts of HSR and population health are intertwined: first, in the purview of studying problems through an overarching population lens, and second, through a health systems lens. The population health approach brings together the two in their application toward health sector reform, allowing researchers to formulate proposals for the organization and delivery of health care systems [ 18 , 19 ].

The efficiency and effectiveness of VA in data analysis and communicating issues in health care are being increasingly utilized [ 8 ]. VA techniques can be applied to complex and multiple data sources, including administrative databases, text-based electronic medical records (EMRs), and multiple data sources. The value addition of VA can be best illustrated by a few examples: Alberta Health Services’ live dashboards on health service performance shows the service utilization by geography, type, and other variables [ 20 ]; population mobility from various sources for identifying pandemics through large interaction graphs and flow maps [ 15 ]; clustering of disease incidence and prevalence, broken down by seasonality and location [ 21 ]; detecting and promoting the understanding of spatiotemporal hotspots for emerging disease trends and associated factors using multisource complex spatiotemporal data [ 22 ]; complex gene-related data analysis to increase accuracy and avoid errors [ 23 ]; and exploring health events in geographic areas such as cities and towns to prevent hospital admissions [ 24 ].

Gap in the Literature

Despite the increasing and varied use of VA techniques, the term visual analytics lacks an accepted definition in the field of health care and can imply different ideas and applications. We found the use of the term for dashboards in critical care to disease surveillance using spatiotemporal techniques. Considering the fast growth of VA to answer complex health care research questions, clarification and categorization of the term and its application are needed.

Our preliminary literature search revealed various methods, frameworks, and use cases developed primarily by computer scientists working in the fields of advanced data mining, machine learning, and analytics. Shneiderman et al’s seminal “overview first, zoom and filter, then details on demand” mantra lays down the most basic workflow tasks related to the type of data under study [ 25 ]. We similarly considered Chuang et al’s [ 26 ] development of tools for textual analysis and Munzer’s [ 27 ] 4-level nested model for the design and validation of visualization systems [ 28 ]. The field is fast developing, with multiple methods, frameworks, and tools that could have potential applications to health care data.

Recent reviews on VA in the clinical sciences show that the technique is being used for different conditions, specialties, populations, and levels of care [ 3 , 14 , 29 ]. In population health and HSR, VA techniques are being applied to complex questions, with varied applications ranging from hospital stay to decision support on pandemics [ 13 , 15 , 30 ]. While formulating our research objectives, we identified peer-reviewed and gray literature sources on VA methods, frameworks, and strategies in these fields, such as the use of multipanel graphs for epidemiologists [ 8 ], VA methods for studying electronic health records (EHRs) and anesthesiology [ 3 ], and spatiotemporal hotspots [ 22 ]. We also considered recent reviews related to VA. Wu et al’s review presents the various methods and approaches for evaluation of health visualizations and VA while identifying the best practices [ 31 ]. Similarly, Islam et al’s review summarizes data mining applications and theoretical perspectives in health care analytics [ 29 ].

Novelty of the Scoping Review and Protocol

As the number of health-related scoping reviews steadily rise each year, so does the need for protocols that address specific methodological challenges [ 32 , 33 ]. This protocol is of interest because the subject is substantially complex to scope because of the following reasons: (1) the multidisciplinary and intersectional nature of VA, (2) the broad areas and overlapping subject matter that population health and health systems research cover, (3) the nondiscriminatory nature of the terms in searching for literature in databases, and (4) the necessity of formulating research solutions methodologically to address these major challenges. In this protocol, we outline how we overcame these challenges to design an innovative review that was feasible, while encompassing an important subject area that has not been covered in a review so far.

This protocol outlines the scoping review methodology related to examining the state of the science of VA in the areas of population health and HSR. We first define the concepts, objectives, and research questions, followed by the design and methods. We discuss the expected results and contributions from the scoping review. In addition, we outline the challenges and solution we developed, allowing for feasibility, while maintaining rigor in a subject area not covered so far. We also present how we operationalized the search strategy for the 3 major concepts—population health, HSR, and VAs—that was undertaken over a course of 3 months, with the help of a multidisciplinary team and a dedicated information specialist. The search strategy was externally peer reviewed. The protocol is innovative and would prove helpful to researchers working in related areas and other stakeholders as the methods are replicable for other sectors. Through this protocol, we further aim at a higher level of transparency in reporting methods, maximizing rigor through peer review, and avoiding duplication of efforts.

The proposed scoping review is novel in summarizing VA methods that have been applied to cases in population health and HSR, using structured or unstructured, complex big data from single or multiple source(s). Furthermore, we focus on the application, frameworks, and methods that involve actual, proposed, modeled, or simulated data with end products that can be valuable to population health and HSR practitioners. We expect a small degree of overlap with reviews on health informatics and data mining, given that the technique has only been recently taken up in health care sciences [ 29 , 31 ]. However, to the best of our knowledge, there is no synthesis of literature on the use and application of VA as an important and quickly developing method in the interrelated fields of population health and HSR.

Objectives and Research Questions

The overall purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR, using any type of data. The main objectives are to explore, map, and synthesize the literature related to VA, including the use of the term VA and its application in population health and HSR [ 34 ]. We will specifically examine the extent and nature of the literature on use cases of VA tools, techniques, strategies, and frameworks.

On the basis of Joanna Briggs tools for conducting systematic scoping reviews, we defined the major constructs of the review under population, concept, and context [ 32 ]. As this is a review on the methods of data analysis, we do not constrain the review to a population. The concept includes VA in population health and HSR, distinguishing it from conventional data visualization techniques, at different levels of analysis, including health service access and utilization.

Guidance Frameworks

To guide the scoping review methodology, we will primarily use the guidance established by Arksey and O’Malley [ 35 ], with improvements suggested by Levac et al [ 36 ] and Peters et al [ 32 ], with recent adjustments made by Tricco et al [ 37 ]. Methodological steps included identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the results [ 35 ]. The latter 2 groups’ work helps with contextualizing these steps toward the specific review.

Study Outcomes and Eligibility Criteria

We identified the research questions following extensive consultations with the protocol authors to clarify the concept and purpose of the review. We considered the major question of studying VA in population health and HSR with the varied terminology used in the literature. Delineating VA from concepts such as information data visualization, which may or may not be interactive , is considered a major challenge for the review. We sought to limit this challenge through developing a detailed a priori eligibility criteria for the literature, with the types of literature to be included. The eligibility criteria are presented in Textboxes 1 and 2 . The criteria are not considered exhaustive and will be developed further during the review.

Inclusion criteria for literature.

Inclusion criteria

• Peer reviewed or conference papers.
• Articles on population-level metrics: access, utilization, disease/condition distribution, and social/multiple determinants of health.
• Articles that have interactive or exploratory techniques for spatial, temporal, spatiotemporal, and geospatial visualizations.
• Articles on electronic medical and health records but with a clear population level or health services research component.
• Articles with machine learning, natural language processing, automated analysis, data mining techniques, interactive tools, and iterative analysis.

Exclusion criteria for literature.

Exclusion criteria

• Editorials, projects, or reports.
• Studies conducted in clinical settings.
• Articles for individual condition(s) from a single hospital/unit, such as intensive care, surgery, and anesthesia without a population component.
• Articles on device or sensor data without a population component.
• Studies that include static data/information visualization/techniques, including simple bar graphs.
• Studies that do not include an analytic component or do not use big data sources.

As recommended by Levac et al [ 36 ], we considered the intended outcome of the review, which was to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. It was also necessary to define this concept in relation to the search strategy to make the review feasible in terms of its time and scope. We will report the results based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation [ 37 ].

Identifying Relevant Literature

Operationalizing concepts of population health and health services research.

One of the major challenges for this review was operationalizing the concepts for developing the search strategy. To aid with this, the scoping review team includes an information specialist (JB). The search strategy is based on 3 main concepts: population health, HSR, and VA. Population health has only recently been added as a Medical Subject Headings term in 2018 in MEDLINE [ 38 ], whereas health services research and visual analytics are nonspecific search terms. We detail the steps taken for operationalizing these concepts below.

We studied recent systematic and scoping reviews for the search strategies employed for operationalizing the population health, health services, and VA concepts. Table 1 describes the concepts, sources, and search terms extracted. For population and public health, experts have attempted to develop a common language related to what both areas and terms encompass [ 39 ]. Alpi et al described the challenges in searching the literature because of the broad nature and often interchangeable and overlapping use of both terms [ 40 ]. Researchers take varied approaches toward strengthening their search strategies. In a review of the methods and application of EHRs to population health, search concepts ranged from infectious disease to social epidemiology [ 41 ]. Two reviews strengthened the searches using a broad search strategy and filtering studies based on objectives and eligibility criteria during the screening stages [ 42 , 43 ]. Searches were also complemented with citation and cross-reference methods for identifying the relevant literature [ 43 ].

Operationalizing concepts and search terms from reviews on population health, health services research, and visual analytics.

For operationalizing the population health concept, we gathered related terms used by the national public health language created by the National Institute for Health and Care Excellence, UK version 1.2 [ 44 ]. We then identified the relevant search terms from detailed database trees. We also compared search terms from these sources and 5 recent reviews in population health [ 19 , 41 , 45 - 47 ], for example, Fone et al presented a detailed search strategy for population health [ 46 ], which we adapted for our use.

As for HSR, we searched recent reviews for operationalizing the concept in combination with the filters developed by the National Library of Medicine [ 48 ]. We identified and used 4 reviews [ 18 , 49 - 51 ] to translate the concept to the search strategy ( Table 1 ).

Operationalizing Visual Analytics

VA is a specific term denoting specific approaches and techniques [ 1 ]. However, the term is vague, and alternate terms such as data and information visualization are used in the literature. We identified and used 4 major recent reviews related to the subject [ 3 , 29 , 31 , 52 ], along with 9 seminal papers [ 14 , 16 , 24 , 53 - 58 ], to identify the search terms ( Table 1 ).

Defining Interactivity in Visual Analytics

Interactivity is usually stated to be one of the recent hallmarks of VA applications, owing to the manipulation of visual interfaces afforded by computing power [ 10 ]. We borrow from Ola and Sedig’s and Pike et al’s work to define interactivity as the ability to reflect changes in the visual representation of data based on one or more indicators available on the analytic interface to the user [ 10 , 59 ]. Pike et al categorized interaction elements into 2 main types: (1) lower level aimed at change of the visual representation to study patterns, relationships, and (2) higher level offers understanding of the intent of interaction itself toward knowledge discovery [ 59 ]. For selecting appropriate literature as part of this scoping review, we mainly focus on the lower level interaction that would allow tasks such as filtering; determining ranges; and finding anomalies, clusters, and the like by providing menus, dropdowns, and other options on the visualization interface. We expect to increase the accuracy for selecting VA literature, while having minimal overlap with other noninteractive visualizations that typically would not fall under VA. In addition, we will focus on VA literature that uses advanced techniques within the analytic engine, such as machine learning and natural language processing.

Final List of Search Terms

For developing the final list of search terms, the process for each concept was not necessarily linear. We constantly compared the list of terms from each step within each concept with detailed database trees to check that we included relevant concept components, while avoiding duplication of results. Following this methodology, we were able to control the noise in constructing the search strategy, making it manageable and feasible.

Search Strategy: Peer-Reviewed Sources

We have limited this review to formal VA methods, applications, and tools that have been either published as peer-reviewed literature or as full conference papers. Using the 3 main operationalized search concepts, the information specialist (JB) developed a search strategy in MEDLINE ( Multimedia Appendix 1 ). We validated the search strategy by ensuring that it captured the key seminal studies about VA in population health and health care, in general, to ensure that the subject literature was included as broadly as possible. We limited the search to English language articles from 2005 onward to coincide with the formal use of the term visual analytics by Thomas and Cook in their seminal book, Illuminating the Path, in 2005 [ 1 , 2 ].

For fine-tuning and improving the search strategy for peer-reviewed articles in health-related databases, the strategy was peer reviewed by another information specialist at the University of Calgary, using the latest Peer Review of Electronic Search Strategies: 2015 Guideline Statement [ 60 ]. After incorporating the suggested revisions, the MEDLINE search yielded 4563 articles and included all 12 seminal studies. The MEDLINE search strategy will be adapted to EMBASE and other databases. Databases that are not primarily health related, such as from geography, mathematics, computer science, or engineering disciplines, will not be searched.

Capturing Gray Literature and Complementary Searching

During our initial searches, we realized that VA methods and representations such as dashboards are presented at conferences in real time, whereas the proceedings include full papers. Given VA’s fast development, this was considered a rich resource that differs from peer-reviewed literature.

We will capture the gray literature through translating the MEDLINE search to Web of Science, Compendex, and Inspect to identify full conference papers. Conference abstracts were excluded from the search for reasons of clarity and completeness of information. An abbreviated search will also be conducted in IEEE Xplore, a subject-specific database. In addition, we will complement our strategy with the searching of reference lists within peer-reviewed and gray literature sources and hand searching subject-specific journals and conference proceedings. These include Applied Clinical Informatics , Visual Analytics in Healthcare , IEEE Transactions on Information Technology in Biomedicine , Journal of Medical Internet Research , Journal of Medical Systems , Journal of the American Medical Informatics Association , Health Affairs , Journal of Biomedical Informatics , Healthcare Informatics Research , and PLoS ONE . Both the IEEE Xplore search and the list of journals are based on Islam et al’s review on data mining in health care [ 29 ] and a Web search by the authors. A Google Scholar and Google Web search engine review will also be conducted, limited to the first 100 results on both platforms.

We will not include dissertations, theses, and book chapters in the review. Furthermore, we will not search for data visualization websites, as it was deemed impossible to gather this huge body of data, adjudicate on the methods and results, and synthesize findings. In addition, frequent content and hyperlink changes would render these sources unusable to future readers in a short time. All citations retrieved will be amalgamated and managed using Clarivate Analytic’s Endnote citation management software [ 61 ].

Study Selection

A priori selection criteria have been developed, which will be modified during the study selection process, as required. Following the methodology suggested by Levac et al, 2 reviewers will independently review the titles and abstracts to categorize whether the piece of literature is eligible for full review [ 36 ]. We expect at least 8000 articles, each of which will be randomly assigned to 2 reviewers. Studies that do not fall in either category or represent conflicts between the reviewers will be resolved by an independent referee. We will retrieve full-text results for the included studies and any unresolved studies for inclusion. In addition, 2 reviewers will screen the full-text results independently for inclusion in the next stage of the review. An expert third party will adjudicate in case of unresolved decisions for inclusion of studies at any stage. We will use the DistillerSR Web platform for efficiently managing the title and abstract review, full-text screening, and abstraction of data [ 62 ].

We will include published and in-press peer-reviewed articles, conference papers, and relevant gray literature sources that include quantitative, qualitative, and mixed method studies, tools, frameworks, and methods of the use of VA. All studies that mention VA as a method in population health and HSR, and at any level of both of the latter concepts, will be included, for example, VA methods for assessing an emergency room population over time will be included. Single disease visualizations at any facility or geographic level will not be included in the scoping review if these are meant to be used toward clinical decision making. However, any studies dealing with population metrics and health services indicators will be included. Furthermore, we will not include methods that may have an application to health care but were not applied to an actual or hypothetical health care research data. This is important as we limit ourselves to the application of VA to health care. We also include studies on EMR/EHR data if the research question or application is in population health or health services.

VA applications also borrow from and overlap with machine learning and natural language processing and can involve complex datasets, unstructured text data such as from EMR sources, and linked analysis. We will include and focus on articles that include any type of mining, querying, and analysis technique that includes VA application to HSR and population health. However, we will not include articles related to data preparation/harmonization, user experience and preference, and human-information and human-computer interaction. The eligibility criteria are given in Textboxes 1 and 2 .

Data Extraction

A data abstraction form will be developed and pilot tested by 2 teams composed of 2 researchers each, all working independently of each other. The data form will be tested on 5 to 7 articles for consistency and comprehensiveness for capturing relevant data. Changes will be made in a team meeting to discuss and compare the pilot test results. On the basis of the studies used for developing the search strategy, the proposed fields for abstraction include author last name, year, full journal name, reviewer’s initials, study type, article type, setting, geographic location (country and continent), and tools and method type (temporal and spatiotemporal). We will try to draw a distinction in the use of methods within the visual and analytic engines, if possible, especially related to machine learning, natural language processing, and other automated methods. In this regard, we have opted not to use the term artificial intelligence as it is nonspecific. Furthermore, the abstraction fields will include innovation and impact of the VA method/uptake of the method, target user/audiences, settings for the use of the VA solution, and potential application toward knowledge translation. Two reviewers will review and chart the data independently for each article.

Results’ Synthesis and Presentation

Abstracted information from all the included articles will be synthesized, and the results will be presented to capture the extent of the literature. First, tables will provide the basic information on the types of studies included, the use of VA in various areas of population health or HSR, and the major tools and frameworks used. This overview will be followed by a narrative presentation of the synthesized mapping of the included literature. The tables and presentation will be developed considering the abstracted results. We are not limiting the review to being reported against a said framework at this point; however, we intend to use the guidance provided in Levy and Ellis’s paper on reporting reviews on information systems [ 63 ], which was selected on the basis of its subject-specific reporting. The authors cite the potential problems in reporting findings in such reviews, suggesting to place them in the wider context of the body of knowledge and the research itself, while building on a theoretical foundation [ 63 ].

As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019.

Comparison With Prior Work

Recent scoping and systematic reviews on the related subjects of analytics and data mining show that VA is being increasingly taken up as a method of choice for big data in health care [ 1 , 6 , 19 ]. In population health and HSR, VA techniques are being applied to complex questions of service delivery and disease distribution [ 2 , 15 , 20 ]. Recent reviews include methods and approaches for evaluation of health visualizations and VA [ 24 ] as well as data mining applications and theoretical perspectives in health care analytics [ 22 ]. The proposed scoping review is novel in summarizing VA methods that have been either applied or proposed to use cases in population health and HSR, using structured or unstructured, complex big data from any or multiple source(s). To the best of our knowledge, there is no synthesis of literature in this area, which will add to the body of literature on these evolving methods of analysis toward complex health care data.

Limitations

This scoping review methodology does not include book chapters, theses, short papers, editorials, nonpeer-reviewed reports, conference abstracts, and live websites using VA techniques for reasons mentioned above. We also limit the use of VA methods from 2005 onward that have been applied to population health and HSR. Finally, we do not explore subject-specific databases, such as from geography and computer science, which may limit our findings to proposed or established methods that have been either published or presented. However, we focus on casting a wide net to capture relevant methods for use cases in both population health and HSR. We devised the methodology in consultation involving a substantial number of multidisciplinary experts to advise on the rigor and feasibility of the review. We also hope to present the findings in 1 or more articles to illustrate the state of science for this important and emerging method.

This scoping review will attempt to provide a foundational understanding of the current landscape of VA within population health and HSR. VA holds tremendous potential for contributing to the learning health systems approach, allowing complex data analysis, and visualization toward improving practices. Mapping the existing VA tools, strategies, and frameworks to health data will promote the use of these methods, which are being increasingly taken up for embedded research and future initiatives in health services. This scoping review protocol describes the design for the review on VA methods in population health and HSR, and it also lays out methodological challenges and steps taken toward ensuring rigor. The latter can be applied and developed by researchers beyond the subject area.

Acknowledgments

Diane Lorenzetti PhD, Director, Health Sciences Library Adjunct Assistant Professor, Department of Community Health Sciences, University of Calgary reviewed the search strategy as an external expert. This protocol and subsequent review are part of the doctoral work under JAC’s Canadian Institutes of Health Research’s Health System Impact Fellowship 2018-19, hosted by the Ontario Neurotrauma Foundation. JAC, IAB, MCM, MI, AK, JSM, and LT are Health System Impact Fellows cofunded by the Canadian Institutes of Health Research and their host organizations. Fellow’s host organizations are mentioned in author affiliations. SJ holds the Toronto Rehabilitation Institute Chair at the University of Toronto.

Abbreviations

Multimedia appendix 1.

Authors' Contributions: All authors contributed to the manuscript writing, revision, and the search strategy. JB formulated and modified the search strategy.

Conflicts of Interest: None declared.

I am a professor in the School of Software, Tsinghua University. I received a B.S. and M.S. from Harbin Institute of Technology, a Ph.D. from Tsinghua University. Before I joined Tsinghua, I worked as a lead researcher at Microsoft Research Asia and a research staff member and research manager at IBM China Research Lab. I was named an IEEE Fellow in 2021.

• Explainable artificial intelligence Visual analytics techniques for machine learnig, including 1) understand, diagnose, and refine a machine learning model; 2) improve data quality and feature quality.
• Visual text analytics Combine the advantages of text mining and interactive visualization to facilitate visual analytics of large-scale textual data.
• Text mining Develop statistical text mining methods to understand complex text data, including evolutionary text clustering, topic modeling, and sentiment analysis.
• Jun Yuan , 2019~
• Weikai Yang , 2019~
• Zhaowei Wang , 2020~
• Zhen Li (co-advised w/Hui Zhang), 2021~
• Yukai Guo , 2022~
• Haoze Wang , 2022~
• Jiangning Zhu , 2023~
• Duan Li , 2023~
• Changjian Chen , 2017~2022, Assistant Professor at Hunan University
• Mengchen Liu , 2013~2018, Senior Researcher at Microsoft Remond
• Xiting Wang , 2011~2016, Assistant Professor at Renmin University of China

Honors and Awards

• 2022, IEEE VGTC Visualization Technical Achievement Award
• 2021, IEEE Fellow
• 2020, IEEE Visualization Academy
• 2020, Best Mentor Award of Tsinghua University
• 2019, Excellent Class Teacher of Tsinghua university
• 2016, Best Associate Editor Award of IEEE TVCG
• 2012, Microsoft Ship-It Award
• 2011, Microsoft Ship-It Award
• 2010, IBM Research accomplishment
• 2006, IBM Master Inventor

Publications

Professional activities.

• IEEE VIS , 2020~
• VINCI, 2012~2013
• IEEE Transactions on Visualization and Computer Graphics , 2019~
• Visualization in Data Science (VDS) at IEEE VIS, 2018
• IEEE VIS (VAST) 2016, 2017
• Artificial Intelligence , 2021~
• IEEE Transactions on Visualization and Computer Graphics , 2015~2018
• IEEE Transactions on Big data , 2018~
• ACM Transactions on Interactive Intelligent Systems , 2019~
• Information Visualization , 2015~
• Journal of Visualization , 2018~
• Organizing Committee (Tutorials Chair): IEEE VIS 2015
• PacificVis 2015
• Organizing Committee (Meetup Chair): IEEE VIS 2014
• Workshop Co-Chair: IEEE VIS 2013 Workshop on Interactive Visual Text Analytics
• Poster Co-chair: PacificVis 2013
• Workshop Co-chair: IEEE VisWeek 2012 Workshop on Interactive Visual Text Analytics
• Program Co-chair: Visual Information Communication - International Symposium 2012
• Workshop Co-chair: IEEE VisWeek 2011 Workshop on Interactive Visual Text Analytics for Decision Making
• Workshop Co-chair: Intelligent Visual Interfaces for Text Analysis, 2010
• Tsinghua Science and Technology: Special issue on visualization and computer graphics , 2012
• ACM Transactions on Intelligent Systems and Technology: Special Issue on Intelligent Visual Interfaces for Text Analysis, 2012
• InfoVis 2020, 2015, 2014
• VAST 2019, 2018, 2015, 2014
• KDD 2015, 2014, 2013
• PacificVis 2013, 2012, 2011, 2010, 2009, 2008
• ACM IUI 2011, 2009
• VISAPP 2012, 2011
• ACM Multimedia 2009
• Big Data Foundations and Applications, 2014, Tsinghua course, Information Visualization: Connecting the Abstract and Visual Worlds .
• PacificVis 2012, Tutorial, Interactive Visual Text Analytics and its Evaluation .
• VINCI 2011, Keynote, Interactive Visual Text Analytics for Decision Making .
• Xiting Wang, Topic Mining and Visual Topic Analysis of Rich Text Corpora , PhD thesis, 2017.
• Mengchen Liu, Visual Analytics of Machine Learning Models , PhD thesis, 2018.

We are currently offering PhD scholarships in the PhD network  LernMINT (LearnSTEM) !

PhD positions in LernMINT:

• Semi-automatic classification of student drawings and texts in Science Education
• Data Analytics for informal learning in school settings
• Ontology-based modelling of learner profiles and curricula

If you are interested, please contact Prof. Ewerth .

Nomination for Best Full Paper Award at ICALT 2020

The paper "A Recommender System For Open Educational Videos Based On Skill Requirements" by Mohammadreza Tavakoli, Sherzod Hakimov, Ralph Ewerth, and Gabor Kismihok has been nominated for competing the Best Full Paper Award at the 20th IEEE International Conference on Advanced Learning Technologies (ICALT 2020). 

• TIB-PITCH with Matti Stöhr
• Our year 2023 – the TIB Report is online
• 10 Years of the TIB AV-Portal – 10 Years of Science in Video Format
• Newly published: TIB Annual Report 2022

Further information

• Faculty of Electrical Engineering and Computer Science
• Institute of Distributed Systems - Knowledge Based Systems Section
• L3S Research Center 

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AnalyticsDegrees.org

PhD in Data Analytics Programs

On This Page:

You’re an analytics professional with a talent for research. You’re considering a PhD in Data Analytics as the next logical step in your career, but you’d like to know more about the practicals. Explore different types of analytics doctorates . Dig into details on timelines , coursework , and the dissertation process . Learn about admissions requirements and funding options , including fully-funded doctorates. Find answers to questions about online degrees and employment avenues after graduation. Or skip ahead to our listings of all the PhD in Data Analytics programs in the country.

What Are PhD in Data Analytics Programs?

A PhD in Data Analytics or a closely related field is an interdisciplinary doctorate that focuses on cutting-edge research in the realms of advanced analytics, statistical computing, big data, and data science. Doctoral students in analytics:

• Push the boundaries of analytics in order to solve complex societal & organizational problems and transform decision-making
• Train to be expert practitioners in big data technologies, newly developed statistical methods, and “out of the box” analytical thinking
• Become analytics & data science professors at universities, senior analytics consultants in industry, and government advisors

Can You Earn a PhD in Data Analytics?

Yes. Doctoral programs in data analytics are available, but they are rare. The most popular title for a degree in the realm of data is the PhD in Data Science . Data science is a highly inventive field that builds on analytical foundations, so it makes sense to consider a doctoral program that focuses on innovation & self-guided discoveries.

When you do find a PhD with the word “analytics” in the title , you’re still going to be looking at a doctorate that intersects with the field of data science. Massive data sets, complicated analytics processes, sophisticated predictive models—doctoral students in analytics are schooled in all of these areas (and more).

Note: PhD programs are focused on original research and high-level thinking. If you want a workplace qualification, consider a Master’s in Data Analytics .

Types of Data Analytics Doctorate Programs

We’ve listed some common titles for doctorates in analytics, but we recommend you check the curriculum links in our listings and learn which department/s are offering the program. You should also look up the faculty’s research interests to see if they align with your own ideas for PhD projects. For example:

• If the degree is offered by the Department of Computer Science, a PhD in Data Analytics might be heavy on research into ethics, bias, AI, and building intelligent systems.
• If the degree is offered in partnership with the School of Business, a PhD in Data Analytics could be preoccupied with Machine Learning (ML), risk analysis, and econometrics.

The title of the PhD plays second fiddle to the department.

PhD in Analytics

A PhD in Analytics can often cut across multiple data-driven domains. Think of fields like Business Analytics, Data Science, Operations Research, and more. For instance, at the University of Notre Dame , doctoral students in analytics are able to access a large number of analytics research labs (e.g. gaming, human behavior, data & society, business, etc.) and collaborate with all kinds of partners.

PhD in Big Data Analytics

Doctorates in Big Data Analytics tend to focus on advanced systems & technologies that deal with processing big data (e.g. statistical computing, data mining, etc.), as well as their applications to real-world problems. Some universities, like the University of South Florida , are also interested in examining the human & social implications of analytics (e.g. ethical usage).

PhD in Analytics & Data Science

A PhD in Analytics and Data Science or a PhD in Data Science, Analytics & Engineering is a way for universities to combine data expertise from multiple departments. Yes, advanced analytics & big data processes will be addressed in the curriculum. But you’ll also find a strong emphasis on programming, algorithm creation, and systems development.

PhD in Data Science

Doctoral programs in data science may have more of a “design & develop” feel than analytics doctorates. In addition to exploring advanced analytics & big data applications, PhD in Data Science students are often interested in designing new information systems & tools (e.g. dashboards), creating their own algorithms & models, and exploring the boundaries of AI & Machine Learning (ML).

Note: Interested in industry & corporate analytics applications? Check out the guide to the PhD in Business Analytics .

How Doctorates in Data Analytics Work: Curriculum & Dissertation

Degree structure.

PhD programs in data analytics contain 6 key elements that take 4-5 years to complete on a full-time schedule. You will have to tackle each stage (e.g. core coursework) before you can proceed to the next one (e.g. qualifying exam).

Core Coursework

Qualifying/comprehensive exam, dissertation proposal, dissertation, dissertation defense.

• Year 1: Core coursework and first-year research papers. Assignment of a faculty mentor.
• Year 2: Core coursework, electives, second-year research papers, and the qualifying exam.
• Year 3: Any remaining coursework. Preparing research projects for publication. Dissertation proposal.
• Year 4: Dissertation work under the guidance of a dissertation advisor and advisory committee.
• Year 5: Dissertation work. Research papers & conference submissions. Dissertation defense.

Sample Curriculum

A PhD in Data Analytics or a closely related field will always contain a set of courses in advanced analytics & data science subjects. These courses can come from multiple departments (e.g. Computer Science, Mathematics & Statistics, Industrial Engineering, Psychology, etc.). Examples include:

• Big Data Analytics
• Data Mining
• Theoretical Statistics
• Statistical Computing
• Machine Learning
• Database Systems
• Information Assurance & Security

These are just a few sample course titles! Use the curriculum links in our listings to get a feel for each program’s unique flavor.

Once you’ve tackled the fundamentals of core coursework , you’ll usually be able to choose high-level electives in your particular research interests. For instance, the University of Central Florida offers electives in:

• Advanced computing (e.g. Parallel & Cloud Computation)
• Sophisticated analytics applications (e.g. Interactive Data Visualization)
• Industries (e.g. Industrial Engineering Analytics for Healthcare)

With some programs, you can customize your doctorate to a remarkable extent.

A qualifying exam is designed to test your knowledge of core coursework . It might take the form of a traditional exam, a paper and/or a project. For example, at the University of South Florida , PhD students are required to report on the results of a real-world, big data analytics project and include codes & systems that were developed in the process.

You’ll be required to develop an original idea for a research- or project-based dissertation and present your dissertation proposal to a dissertation advisory committee—experienced faculty members and (occasionally) outside experts who are interested in your area of work.

• A research-based dissertation will explore new realms of analytics research and potential applications.
• A project-based dissertation will involve work on a real-life project—this may be created at a research center or be suggested by an industry partner.

The dissertation proposal often takes the form of a written outline and an oral defense/presentation. If the committee accepts your proposal, you can get to work on your dissertation.

A PhD dissertation is a piece of original research that makes a significant contribution to the theory & practice of a field. In the world of data analytics & data science, dissertations can be research-based or project-based.

Dissertation Titles

Examples of real-life PhD in Data Analytics & Data Science dissertation titles include:

• A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data
• Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance
• Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data
• The Intelligent Management of Crowd-Powered Machine Learning
• Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models
• Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms

While you are writing up your dissertation, many universities will also expect you to be submitting related research papers to peer-reviewed journals & industry conferences.

The final step in the PhD process is the dissertation defense. You’ll be required to present your dissertation findings to your dissertation advisory committee and defend your research ideas in an oral & visual presentation. This will be followed by questions and a discussion.

It’s not as intimidating as it sounds. By this stage in your education, you will know your research inside-out and will have brainstormed many of the potential questions with your dissertation advisor. You can prepare for a defense by observing other student defenses, practicing with mock presentations, and reading up on the work of committee members.

PhD in Data Analytics: Admissions

Doctorate in data analytics: what it takes to get in.

Every PhD program in data analytics is going to have a unique set of admissions requirements! When you’re putting together a shortlist of doctorates, use the admissions links in our listings to save yourself time & trouble. You can decide if the program suits your level of expertise and education.

Doctoral programs in tech-driven disciplines—especially ones that are fully funded —are extremely competitive. You can stand out from the crowd by:

• Examining your entire application to see if you can make up for weaknesses (e.g. lower grades) with strengths (e.g. real-world projects)
• Matching your research interests to the university, department & research labs offering the program
• Collaborating with experienced analytics practitioners to co-author papers & publications
• Attending industry events and making connections that will help in your research
• Earning professional certificates to fill in any skills gaps

Degree Requirements

Your degree should be in a discipline that’s relevant to your area of research interest in the PhD. For a data analytics doctorate, that might mean a degree in statistics, data analytics, computer science, economics, or similar. The standard GPA requirement is 3.0 GPA or higher.

• Bachelor’s Degree Entry: Some doctoral programs in data analytics & data science are willing to consider applicants with a bachelor’s degree.
• Master’s Degree Entry:  Some doctoral programs are only looking for candidates with a master’s degree.

If you’re an undergraduate and you like the look of a PhD that only accepts master’s candidates, ask the program coordinator if you can earn an MS through the same university. Most doctoral programs have a “Master’s Along the Way” option.

Skills & Proficiencies

PhD candidates in analytics must be ready to tackle advanced coursework and high-level research. So universities will usually want to see evidence of proficiency/course credits in:

• Statistics, calculus & linear algebra
• Common analytical programming languages (e.g. R, Python, SAS, etc.)
• Analytics fundamentals (e.g. database management systems)

If you don’t have an undergraduate or master’s degree in analytics or a closely related field, universities will be poring over your transcripts & résumé to make sure you can handle any technical coursework.

General Requirements

In addition to your degree transcripts, almost all PhD programs in data analytics & data science fields will want to see:

• GRE or GMAT scores
• Letters of recommendation
• Statement of purpose
• TOEFL scores for non-English speaking international applicants

PhD in Data Analytics: Tuition & Funding

How to fund the phd.

Doctoral programs in data analytics & data science fall into 2 broad categories:

• Fully funded PhD programs
• Tuition-driven PhD programs

As you might expect, fully funded doctorate programs at strong universities are hard to get into!

Fully Funded PhD Programs

A number of STEM doctorates at research universities are fully funded. The university will waive all tuition costs and provide you with a living stipend as compensation for teaching & research activities. Many PhD students work as Teaching Assistants (TAs) and Research Assistant (RAs) during their doctoral studies.

Talk to the PhD program coordinator and check the fine print when you’re considering these programs.

• You may (or may not) qualify for on-campus housing and university health insurance.
• You may (or may not) qualify for conference stipends, overseas internships, and other perks.
• You may (or may not) be expected to pay for miscellaneous university fees.
• You may receive funding for Years 1-4 of your degree, but Year 5 support could be conditional on strong academic performance.

Tuition-Driven PhD Programs

You’ll also find doctoral programs in analytics & data science that do not offer any funding. They’ll expect you to pay for the degree out of your own pocket. At a private university, a PhD could cost upwards of $60,000-$80,000 in tuition alone.

So tread carefully! If you don’t qualify for fully funded PhD programs and you believe that a doctorate is  essential for your career goals, consider applying to a PhD program at a public university in your state—UCF’s in-state tuition for a PhD in Big Data Analytics is very reasonable.

You will also need to look into postgraduate loans, private scholarships & fellowships, employer reimbursement, and teaching & research job opportunities to offset your costs.

Online PhD in Data Analytics Programs

Can you earn an online phd in data analytics.

Yes—but we would caution against them. There are a few universities that offer online doctorates in data analytics, but they tend to be for-profit (e.g. Colorado Tech) or focused on executive-level training instead of research (e.g. DBA in Data Analytics from the University of the Southwest).

You’ll have a little more luck in finding online doctorates in data science, but they still won’t be offered by top-tier universities.

Why Are Online PhD Programs in Analytics Hard to Find?

Prestigious research universities & high-ranking schools are very cautious about maintaining their reputation for quality. They want doctoral students in data analytics & data science to:

• Attend classes in advanced topics, ask questions, and follow-up with faculty
• Have unfettered access to the university’s research centers, labs, and technical facilities
• Be able to teach undergraduates and conduct research in-person
• Meet with their dissertation advisor on a regular basis
• Network with visiting experts and fellow students

We agree with them. At this level, we highly recommend you choose an on-campus doctoral degree.

Career Prospects for PhD in Data Analytics Graduates

A PhD in Data Analytics or a closely related field is a super-specialized degree. You don’t need a doctorate to pursue a career in analytics & data science. Many senior-level practitioners simply have a degree like a Master’s in Data Analytics (or a similar title) and a lot of on-the-job experience.

However, a doctorate in analytics is an excellent choice for aspiring:

• University Professors: If you wish to teach analytics & data science at a college or university, you will probably need a research-focused doctorate. At the University of Notre Dame, 80% of its PhD in Analytics graduates go into academia.
• High-Level Researchers:  PhD graduates work in think tanks, industry research labs, and university research centers where exciting discoveries are taking place.
• Data Science & Analytics Consultants: You may wish to act in an advisory capacity for Wall Street, Silicon Valley, and other major centers of industry.
• Senior Research Positions: Some jobs in major tech companies, data-intensive businesses & financial companies (e.g. Senior Statistician) will require top-level research skills.

PhD Data Analytics FAQs

What should i look for in a data analytics doctoral program.

When you’re starting to put together a shortlist of doctoral programs, consider the following aspects:

• Funding Options: The best choice is going to be a fully funded PhD from a highly ranked & highly regarded university that includes teaching & research assistantships.
• Departmental Reputation: Which schools & departments are offering the degree? What kinds of unique benefits do they offer students? How much research funding do they receive?
• Faculty Expertise: Faculty profiles will be posted on the PhD program website. Read their bios, meet them for a virtual coffee, and learn more about their research & industry work. These people will become your advisors & mentors.
• Access to Resources: Will you have access to top-of-the-line analytics tools, commercial resources, and large-scale infrastructures? Can you work on projects within a major analytics research lab or center?
• Career Preparation: A strong PhD program will prepare you for the job market after graduation. Does the curriculum include opportunities for you to submit research papers to peer-reviewed journals? Does it offer stipends for conference travel? Does it bring in visiting experts for seminars?

What is a STEM Doctorate?

STEM stands for Science, Technology, Engineering & Mathematics. A STEM doctorate is any PhD—including the PhD in Data Analytics and the PhD in Data Science—that contains at least 50% of coursework in these fields.

• Are you an international student? Ask if the doctoral program has a “STEM designation” from the U.S. Department of Homeland Security (DHS). Students on an F-1 Visa can apply for Optional Practical Training (OPT) /temporary employment after graduation. Having a STEM-designated degree extends the OPT period from 12 months to 36 months.
• STEM programs often receive a fair amount of funding from the government and private industries. That means universities may be able to offer fully funded PhD programs to multiple students.

Is a PhD in Data Analytics Worth It?

Only if you have a specific career goal in mind. A PhD in Data Analytics or a closely related field is going to be time-consuming, challenging, and heavy on research. At least 4-5 years of your life will be devoted to earning it, so you and your family need to be prepared for the journey.

Unsure about your decision? Talk to analytics professionals who have already gone through the PhD gauntlet. You’ll find doctoral graduates on LinkedIn, at industry conferences , and within faculty directories on university websites. Be prepared to talk to them about your research interests and your goals.

All Phd in Data Analytics Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

PhD in Data Science, Analytics, and Engineering

University of arizona.

Department of Biosystems Engineering

Tucson, Arizona

PhD in Biosystems Analytics & Technology

University of central florida.

College of Sciences

Orlando, Florida

University of South Florida-Main Campus

Muma College of Business

Tampa, Florida

Georgia State University

Robinson College of Business

Atlanta, Georgia

PhD in Business Administration & Digital Innovation - Data Science & Analytics

Kennesaw state university.

School of Data Science and Analytics

Kennesaw, Georgia

Doctor of Philosophy in Analytics and Data Science

University of notre dame.

Mendoza College of Business

Notre Dame, Indiana

University of Kansas

School of Business

Lawrence, Kansas

PhD in Analytics and Operations

Central michigan university.

College of Science and Engineering

Mount Pleasant, Michigan

PhD in Statistics and Analytics

North carolina, north carolina state university at raleigh.

Center for Geospatial Analytics

Raleigh, North Carolina

PhD in Geospatial Analytics

Pennsylvania, pennsylvania state university-main campus.

College of the Liberal Arts

University Park, Pennsylvania

PhD in Human Development and Family Studies and Social Data Analytics

Phd in informatics and social data analytics, phd in political science and social data analytics, phd in psychology and social data analytics, phd in social data analytics, phd in sociology and social data analytics, phd in statistics and social data analytics.

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Visual Sheet Music Analytics - PhD Thesis - 2023/2024

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• Matthias Miller

Date created: 2023-12-03 08:40 PM | Last Updated: 2023-12-09 08:41 PM

Category: Project

Description: Additional material and appendices for the PhD thesis presented by Matthias Miller in 2023/2024

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This repository contains additional resources regarding the Ph.D. thesis titled "Visual Sheet Music Analytics" submitted by Matthias Miller at Data Analysis and Visualization Group at the University of Konstanz at the end of 2023.

The Visual Musicology Graph was used for categorizing research projects at the intersection of Musicology and Information Visualization.

The three major applications tha…

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