Data Physicalisation Design: A Framework for Encoding, Evaluation, and Iteration

Why It Matters

Understanding the available encoding variables and evaluation methods is crucial for designers creating physical data representations. This structured approach ensures that the resulting physicalisations are not only aesthetically pleasing but also effectively communicate data and meet user needs.

Key Finding

The research provides a structured framework for designing and evaluating data physicalisations by identifying the building blocks (encoding variables) and the assessment tools (evaluation criteria and methods).

Key Findings

• Identification of a comprehensive set of encoding variables for data physicalisation.
• Compilation of relevant evaluation criteria for assessing data physicalisations.
• Categorization of methods suitable for evaluating data physicalisations.
• Development of a conceptual framework and a seven-stage model for designing and evaluating data physicalisations.

Research Evidence

Aim: What are the key encoding variables, evaluation criteria, and evaluation methods for data physicalisations, and how can they be integrated into a design and evaluation framework?

Method: Narrative and Systematic Literature Review

Procedure: The researchers conducted a narrative review of literature from Information Visualisation, HCI, and Cartography to identify encoding variables for data physicalisations. They also performed a systematic review to extract evaluation criteria and methods used for assessing data physicalisations. A conceptual framework and a seven-stage design/evaluation model were developed based on these findings.

Context: Data Physicalisation Design and Evaluation

Design Principle

Effective data physicalisations are achieved through deliberate selection of encoding variables and rigorous, user-centred evaluation.

How to Apply

When designing a data physicalisation, consciously consider the physical properties you will use to represent data (e.g., shape, colour, texture, size) and plan how you will test its clarity and impact with users.

Limitations

The review is based on existing literature, and novel encoding variables or evaluation methods may emerge over time. The effectiveness of specific criteria and methods may vary depending on the context and type of physicalisation.

Student Guide (IB Design Technology)

Simple Explanation: This research helps designers by giving them a checklist of ways to make physical data representations and a way to check if they work well.

Why This Matters: Understanding how to encode data physically and how to evaluate it is key to creating effective and engaging physical design projects that communicate information clearly.

Critical Thinking: How might the choice of physical material itself influence the encoding variables and the overall effectiveness of a data physicalisation?

IA-Ready Paragraph: The design of this physical data representation was informed by research into data physicalisation, specifically by considering established encoding variables such as [mention specific variables used, e.g., size, colour, texture] to convey [mention data being represented]. The effectiveness of this encoding was evaluated using criteria aligned with [mention criteria, e.g., clarity, accuracy, engagement], employing methods such as [mention methods, e.g., user interviews, task-based observation] to ensure the data is communicated effectively to the target user.

Project Tips

• When designing a physical representation of data, think about what physical attributes (like size, colour, or texture) you can change to represent different data points.
• Plan how you will ask people if your physical data representation is easy to understand and what it means.

How to Use in IA

• Use the identified encoding variables to justify your design choices for representing data in your physical product.
• Incorporate the suggested evaluation criteria and methods to assess the success of your design in communicating information.

Examiner Tips

• Demonstrate an understanding of how different physical properties can encode data and how this choice impacts user comprehension.
• Show a clear plan for evaluating the effectiveness of your data physicalisation, referencing established criteria and methods.

Independent Variable: ["Encoding variables (e.g., shape, size, colour, texture)","Evaluation criteria (e.g., clarity, accuracy, memorability)","Evaluation methods (e.g., user testing, expert review)"]

Dependent Variable: ["Effectiveness of data communication","User comprehension","User engagement","Accuracy of data interpretation"]

Controlled Variables: ["Complexity of the dataset","Target audience characteristics","Physical environment of use"]

Strengths

• Provides a comprehensive overview of current knowledge in data physicalisation design and evaluation.
• Offers a practical framework and model to guide designers and researchers.

Critical Questions

• Are there any cultural or contextual factors that might influence the interpretation of specific encoding variables in data physicalisations?
• How can the long-term impact and memorability of data physicalisations be effectively measured?

Extended Essay Application

• Investigate the efficacy of novel encoding variables for representing complex datasets in physical form.
• Develop and test a new evaluation framework for data physicalisations in a specific domain, such as environmental monitoring or public health.

Source

Encoding Variables, Evaluation Criteria, and Evaluation Methods for Data Physicalisations: A Review · Multimodal Technologies and Interaction · 2023 · 10.3390/mti7070073