Spatiotemporal data mining for movement ecology

GPS tracker technology is revolutionising ecological research. Movement Ecology is the science of using these data to infer behaviours that were not previously known to learn more about the organism, how it interacts with its environment and it sensitivity with the environment. This cross-disciplinary project will take advantage of a long-term collaboration with a movement ecology group to apply the latest data mining techniques to detect places of ecological significance for these animals at appropriate spatial scales.

Objectives

  • Review: Review the state of the art of movement ecology and spatiotemporal data mining
  • Implement: Implement appropriate versions data mining techniques that can be demonstrated to users
  • Test and evaluate: assess the utility of these methods for achieve movement ecology goals

Skills

  • Development: ability to build interactive visualisation interfaces that connect to statistics and data mining libraries/software
  • Statistics: a good understanding of applied statistics
  • User-centred work: open to designing, testing and evaluating these interfaces with analysts from different domains  
  • Other: Handling datasets, working with geographical data

Contact

This topic was suggested by Dr Aidan Slingsby. Please direct further enquiries to the giCentre.

Literate Visualization

Increasingly we are seeing software for generating visualization being embedded in live documents that mix textual narrative, graphics and code. Examples include Observable, Jupyter and R Notebook. This opens up new opportunities to enhance such environments and change the practice of designing, constructing, sharing and using visualization. The giCentre has developed a new framework for considering integrated narrative and visualization – Literate Visualization  –  and we seek interested researchers to help develop both the theory and practice.

Objectives (can focus on a subset of these)
 - Review existing and emerging approaches in narrative-based visualization
 - Enrich the theory of Literate Visualization
 - Develop Literate Visualization construction software
 - Evaluate Literate Visualization as a design and communication process.
 
Skills/interests
 - Story telling in Visualization
 - Visualization Design
 - Construction of web-based visualization software
 - Visual Data Science

Contact
This topic was suggested by Professor Jo Wood. Please direct further enquiries to the giCentre.

Narrative Visualization for Exploring Active Travel

While there may be claimed agreement that ‘active travel (e.g. walking and cycling) is desirable,  much of the public discourse is heated, polarised and lacking in evidence.  This PhD topic would investigate the role that visualization can play in informing a more evidence-based discussion. It brings together several important themes in visualization: spatio-temporal trajectory visualization; visualization of risk and uncertainty; visual storytelling and rhetoric. 

For examples of previous giCentre work in this area including PhD student research, see Beecham 2014,  Beecham and Wood 2014a,  Beecham and Wood 2014b, Beecham and Wood 2014c, Wood et al 2014

Objectives (can focus on a subset of these)
 - Review of existing work on trajectory geovisualization
 - Review of visual communication of risk and uncertainty
 - Review and development of models of data visualization rhetoric
 - Design and implementation and evaluation of a visual analytic system for supporting discourse in active travel.
 
Skills/interests 
 - Geovisualization, especially trajectory visualization.
 - Statistical methods for uncertainty.
 - Visual storytelling
 - Visualization / statistical graphics programming (e.g. R, Python, D3, Processing)
 - Active travel including transport policy and active travel advocacy

Contact
This topic was suggested by Professor Jo Wood. Please direct further enquiries to the giCentre.

Visual Support of Bayesian Reasoning

The process of using data visualization to explore hypotheses and build knowledge is common in visual analytics and data science. Bayesian methods formalise the relationship between prior and new knowledge in the light of evidence. Yet despite the apparent compatibility of the two approaches,  Bayesian approaches are not well supported in most data visualization / visual analytics environments. This PhD topic would explore new visual analytic designs and approaches to support Bayesian Reasoning.

Objectives (can focus on a subset of these)
 - Review Bayesian approaches in visualization and data science
 - Develop new methods of Bayesian data visualization
 - Evaluate use of visual Bayesian reasoning in a data science context
 
Skills/interests - Visual data science
 - Statistical methods (including Bayesian approaches)
 - Statistical graphics
 - Visualization / statistical graphics programming (e.g. R, Python, D3)

Contact
This topic was suggested by Professor Jo Wood. Please direct further enquiries to the giCentre.

Audio-Visual Narrative in Data Visualization Design

Storytelling is an emerging and popular theme within information visualization. Most research in this area focuses on narrative structure and visual design to support storytelling. This PhD topic would consider how narrative in musical composition and use of sound can enhance the narrative process in visualization. 

This research would take place in the giCentre but with additional supervisory input from the Department of Music at City University.

 

Objectives (can focus on a subset of these)
 - Review theory and practice in information visualization storytelling
 - Review theory and practice in narrative in musical composition
 - Evaluate the effectiveness of musical composition in supporting narrative visualization
 - Develop theory in audio-visual narrative visualization design


Skills/interests
 - Data visualization design
 - Music composition
 - Digital music technologies
 - Narrative in visualization and music

Contact
This topic was suggested by Professor Jo Wood. Please direct further enquiries to the giCentre.

The Semiology of Sound in Data Analysis

Bertin’s Semiology of Graphics is well known and influential in the theory and practice of visualization design. Less well explored is an equivalent semiology of sound for supporting data analysis. While sonification has been applied to data visualization there is scope to develop theory to direct best practice and evaluation of different sonification strategies. This research would take place in the giCentre but with input from the Department of Music at City University.

Objectives (can focus on a subset of these)
 - Review existing and emerging uses of sonification to support data analysis
 - Build new data sonification platforms
 - Evaluate different sonification techniques and strategies
 - Develop new theory in the semiology of sound in data analysis
 
Skills/interests
 - Data visualization design
 - Music / sound
 - Web audio

Contact
This topic was suggested by Professor Jo Wood. Please direct further enquiries to the giCentre.

Visual depiction of temporal trends in movement

The visual depiction of large numbers of movements is challenging, but it is even more so where we are interested in changes of movement over time. There are opportunities to combine analytical techniques from data mining to help summarise important patterns of movement, but these will need to be encapsulated in effective and intuitive information visualisation. There is also the potential to investigate the impacts of missing or imprecise data in characteristing these trends. There are many potential applications of this work.

Objectives
 - Review approaches to the visual depiction of origin-destination data
 - Review approaches to the visual depiction of temporal trends
 - Design and implement visual approaches to summarise temporal trends in movement data
 - Evaluate their effectiveness
 - (Ideally) produce a library to enable others to use these approaches.
 
Skills/interests
 - Geographical data
 - Information visualisation
 - Digital prototyping and implementation preferably using JavaScript
 - Evaluation

Contact
This topic was suggested by Dr Aidan Slingsby. Please direct further enquiries to the giCentre.

Characteristing objects based on spatio-temporal aspects of their movement

The movement of people, animals and other moving objects can indicate a great deal of information about them. This project will help formalise the characteristics that can be used to do this. We have a large dataset of animal movements and access to users of these data. There is also the potential to investigate the impacts of missing or imprecise data in characteristing these trends. There are many potential applications of this work.

Objectives
 - Identify the extent to which objects can be characterised by their movement
 - Identify characteristics of movement can do these effectively
 - Implement an interactive querying interface to enable experts to filter objects using these criteria.
 - Create, evaluate and reflect on a framework for helping characterise objects based on spatio-temporal aspects of their movement.

 
Skills/interests

 - Geographical data
 - Information visualisation
 - Digital prototyping and implementation preferably using JavaScript
 - Evaluation
 
Contact
This topic was suggested by Dr Aidan Slingsby. Please direct further enquiries to the giCentre.

Geographically ordered glyphs for studying multivariate geographical distributions

This project will investigate glyph-based approaches to the visual depiction of multivariate data across geographical space. It will build on existing work, ideas and data that will help get this project to a good start. The project will involve design, implementation and evaluation and will tackle important theoretical issues in geographical visualisation.

Objectives
 - Review the multivariate glyph literature.
 - Identify, implement and evaluate different geographical discretisation methods.
 - Identify, implement and evaluate glyphs that are effective for comparison across 2D space.
 - Generate guidelines to facilitate their use.
 
Skills/interests
 - Geographical data
 - Information visualisation
 - Digital prototyping and implementation preferably using JavaScript
 - Evaluation

Contact
This topic was suggested by Dr Aidan Slingsby. Please direct further enquiries to the giCentre.
 

Shelf life of data: a critical look at exploratory data analysis

Exploratory data analysis has significant potential within the analysis of complex, heterogeneous data where analysts are seeking observations that they can answer questions with and can action on within their application domain. As an open-ended process, exploratory analysis sessions can lead to several unexpected findings but with potential for high numbers of false positives and with limited guidance on when to conclude the analysis. This project aims to provide mechanisms and heuristics to increase the likelihood of success and the efficiency of exploratory analysis processes and investigates answers to questions such as, “When to know that the results are stable and actionable?”, “When does a data set stop being useful during exploratory data analysis session?”, “How to action on findings from an exploratory analysis process?” to name a few.

Objectives

  • Review literature within visual analytics and exploratory data analysis for data analysis strategies

  • Develop a theoretical framework to inform and guide users on the progress of an exploratory analysis process towards robust, stable and actionable findings

  • Design, develop, and evaluate interactive visualisations to support the above

Skills/Interests

  • Experience in designing and implement interactive visual representations

  • Good understanding of statistics, exploratory analysis, and related data-science techniques

  • Interest in user-centred design processes

Contact

This topic was suggested by Dr Cagatay Turkay. Please direct further enquiries to the giCentre.

Visualisation as Evidence: On the role and the limits of visualisation as scientific evidence

The tremendously increased availability and volume of data is reshaping how scientific investigation is executed, however, scientists still resort to conventional statistical analysis routines. Data-intensive investigations and analysis requires a new set of tools for the effective and robust generation and testing of hypotheses. Visualisation offers possibilities that can embrace the variety, uncertainty, and the multi-dimensionality within phenomena being investigated. This project aims to investigate the role and limitations of visual representations as scientific evidence to support and provide confidence within hypothesis generation and testing processes.

Objectives

  • Review literature in statistical and visual data analysis to build an understanding on the use of visualisation as an inference methodology

  • Design visual representations to be used as evidence in supporting scientific arguments within data-intensive hypothesis generation and testing settings

  • Design studies to investigate the effectiveness of visualisation designs

  • Develop a framework for the effective use of visualisation as scientific evidence

Skills/Interests

  • Ability to design and implement interactive visual representations

  • Good understanding of Statistical Analysis Techniques

  • Ability to design and execute user studies

Contact

This topic was suggested by Dr Cagatay Turkay. Please direct further enquiries to the giCentre.

Visual Data Science: Incorporating statistical modelling into visual exploratory data analysis

Elicit requirements, design, implement, test and evaluate visual encodings and interactions for supporting explorative geographical data analysis with appropriate statistical and data mining techniques. These techniques should support analysts using domain knowledge to build simple models on-the-fly, gradually refining them using various model fit metrics and summary statistics, with the goal to understand the processes and drivers that led to the original data. Part of this process is maintaining the provenance of which models were built and their characteristics. Restricted to geographic data with features that are generally understood by analysts (i.e. not high dimensional data)

Objectives

  • Review: Exploratory visual interfaces, analytical "goals" in these interfaces, suitable statistical and data mining techniques, their application

  • Design: Based on one or more case studies, design visual encodings and interactions to support exploratory data analysis with appropriate statistical and data mining techniques

  • Implement: Implement versions of this design in order for them to be test and evaluated

  • Test and evaluate: assess the impact this has on the exploratory visualisation process

  • Provenance: Maintaining the provenance of which models were built and their characteristics and make available to support the data exploration process    

Skills

  • Development: ability to build interactive visualisation interfaces that connect to statistics and data mining libraries/software

  • Statistics: a good understanding of applied statistics

  • Statistical graphics: knowledge of the use of statistical graphics

  • User-centred work: open to designing, testing and evaluating these interfaces with analysts from different domains  

  • Other: Handling datasets, working with geographical data

Contact

This topic was suggested by Dr Aidan Slingsby. Please direct further enquiries to the giCentre.

From Personal Data to Personable Visualizations

Explore how by constructing visualizations of their personal data people can create an intimate relationship with their data. This exploration is deeply human-centred. It involves conducting several long-term studies with a group of people already collecting some kind of personal data (e.g., energy bills, bank accounts, fitness, time tracker, medical condition), human-centred iterative approaches to design and qualitative evaluations such as interviews, focus groups, and observations. The space of personal data visualization is ripe for research work, particularly in a dynamic context (with e.g., daily or weekly data updates) as the singularities of this context, such as the relationship that people have with their own personal data, are yet to be explored. This project will particularly look into the role of physical engagement when constructing physical visualizations in order to uncover how cognitive and kinesthetic efforts can enhance learning, memorability, enjoyment, and overall visualization literacy.

Objectives

  • Review the literature in physical visualization, personal visualization, and narrative visualizations; as well as in kinesthetic learning, learning-by-doing, and visualization literacy.

  • Iteratively design (possibly computer-supported) prototypes with people collecting personal data and willing to make better sense of it by themselves.

  • Study through qualitative methods what makes visualizations of personal data personable to people.

Skills/interests

  • Visualization / statistical graphics design

  • Human-centred design

  • Hands-on activities, fabrication

  • Qualitative methods such as interviews and open coding

  • Design and prototyping (computer-supported or not) skills

  • Map out the space of personable visualizations of personal data.

Contact 

This topic was suggested by Dr Charles Perin. Please direct further enquiries to the giCentre.

A quantitative model of visual performance based on principles of visual scanning

Important strides have been made in understanding how people scan visual content for information as a function of the interplay between the fovea and the peripheral view, the interplay between intentional and automatic processes, and the content itself. Such knowledge may be used to predict performance on low-level visual tasks in different visual encodings, and could be used in many cases as an alternative to user experimentation.

The project aims to build on previous models that use an understanding of low-level visual behavior to predict performance on simple HCI tasks (e.g., EPIC architecture), but would innovate by extending it to the much more complex content typical of real-life data visualizations.

Activities

  • Conducting user studies to explore visual scanning patterns for typical data-reading tasks in typical visual data content

  • Relating observed behavior to low-level models of visual scanning

  • Developing a general model of visual scanning as a function of visual content type and data-reading task

  • Conducting experiments to validate the model

Skills/Interests

  • User study design

  • Human factors (e.g., vision, cognition, task analysis)

  • Basic data handling and data analysis

Contact

This topic was suggested by Dr Radu Jianu. Please direct further enquiries to the giCentre.

Gaze-adaptive visualizations: principles, designs, benefits

Eye-tracking can provide real-time insight into the short and long term data preferences and interests of people using visual data analysis systems.  These systems could leverage such information to adapt intelligently in real time, and show data in ways that better support users’ goals and tasks. This project aims to explore principles of gaze-adaptive visual support in real applications.

Activities

  • Design and implement concrete gaze-adaptive interactive responses in established data visualizations or data-analytic systems.
  • Conduct user studies to test the effectiveness of such designs

Skills/Interests

  • User study design

  • Human factors (e.g., vision, cognition, task analysis)

  • Iterative software design and development, rapid prototyping

Contact

This topic was suggested by Dr Radu Jianu. Please direct further enquiries to the giCentre.

Framework for data-of-interest (DOI) eye-tracking experimentation

Eye-tracking allows us to capture where users are looking on a computer screen. DOI, a novel eye-tracking experimentation paradigm, relies on mapping users’ gazes to interactive visual content automatically and dynamically. This allows experimenters to track users’ deep interests in particular content, how these interests change over time, and how they support goals and tasks. This can facilitate unprecedented insight into how people rely on rich visual content and data to understand, learn, analyze, and decide.

The project aims to lay the groundwork for DOI experimentation by exploring the types of insights that can be achieved with it, and by designing novel analytical paradigms and tools necessary to interpret the large and complex data that DOI experiments typically produce. These topics will be pursued by applying DOI experimentation to concrete problems, such as exploring how people use visualization to explore and analyze data, or how students learn from visual content.

Activities

  • Design experiments and collect concrete DOI eye-tracking data, possibly in collaboration with researchers in other domains.

  • Analyze collected DOI data using conventional data science methods (i.e., existing visualization tools and data analysis packages)

  • Design and develop (iteratively) novel visual analytics paradigms and tools to explore DOI data in general

Skills/interests

  • User study design

  • Data handling, analysis, and visualization

  • Human-centred design

  • Iterative software design and development, rapid prototyping

Contact

This topic was suggested by Dr Radu Jianu. Please direct further enquiries to the giCentre.