Mineral raw material flow systems are complex and require adequate visualisation for understanding their characteristics. Visualisations are different maps of complex systems. They can inform decision making in industry and government, by visualizing current status, historical trends, and potential future developments under different conditions. Visualization tools are developed to support the recording (monitoring), exploration (analysis), and explanation (interpretation) of information. The usefulness and quality of visualizations therefore strongly depends on the quality of the underlying system definition, data, and models. However, as the systems are complex visualisation is a demanding task, as the model illustrations and results have to be cognitively sizable. Therefore, this requires the implementation of different levels of resolution and techniques to disintegrate or merge flows and processes.
Visualisation in the modern world is essential and foundational for communication. It is easy to overload the readers’ senses with too much information, yet with some time and effort, we can convey and impart complex ideas and structures using visual aids that otherwise may be difficult to explain through the written word alone. Good visualisation is the art of simplifying, creating context, and imparting meaning to data, to tell a coherent story.
The physical economy monitoring framework seeks to provide methods and guidelines for structuring MFA data, enabling a more comprehensive understanding of mineral and material systems. Visualisation is one of the key components described in the MinFuture Framework, while the MinFuture Dimensions describe four dimensions which are key to MFA studies: stages, trade, linkages, time. Uncertainty and stocks are added to this list to create six core aspects for which visualisation might be required.
Please note: The following text is taken from the existing MinFuture reports "A systems approach for the monitoring of the physical economy" and "Visualising Material Systems". Please click on the afore-mentioned links to access and read the full publications.
Review of visualisation theory and principles
There is a long history of visual theorists and designers defining principles of analytical design’ with key contributors including Edward Tufte, Jacque Bertin, Stephen Few, and Jock Mackinlay. Effective visualisation is found in simplicity, data visualised in its most naked or pure form, void of frivolous additions. Tufte (2006) presents six foundational principles of analytical design for communicating the essential information in visualisations:
- Principle 1: Show comparisons, contrasts, differences.
- Principle 2: Show causality, mechanism, explanation, systematic structure.
- Principle 3: Show multivariate data.
- Principle 4: Integrate words, numbers, images, diagrams.
- Principle 5: Describe the evidence.
- Principle 6: Content must be relevant, have integrity and be of significant quality.
Bertin in his book ‘Semiology of Graphics’ (1983) describes seven foundational variables of graphical perception, which relate to the way we perceive information through sight. Figure 14 below shows these position and retinal variables against their strengths and weakness for displaying information as a point, area and line.
Figure 14: Foundational variables of graphical perception and their strengths and weaknesses (Bertin, 1983).
Visualisation matters because it is an essential part of the way we communicate information to others. Tufte (2006) comments that ‘the world we seek to understand is profoundly multivariate’ and therefore visualisations by association must also be multivariate. The aim of the visual designer is to draw out clarity from this complexity.
Two types of visualisation tools are required for telling data driven stories:
- Elicitation tools are used for extraction and interrogation of the MFA data, to ensure credibility and extract clear narratives.
- Communication tools are used to convey the data structure and narrative to the reader.
The process of telling a visual story is analogous to the Google Mapping approach which takes traditional maps, creates journey options and keeps standout routes, Figure 15. Similarly, for visual story-telling, an interactive data environment can be developed, where tables of MFA data (maps) are structured, allowing the creation of data stories (journeys) and communication of these stories to decision makers (standout routes), Figure 16.
Figure 15: Visual storytelling processes analogy to Google mapping
Figure 16: A systematic approach to developing data driven MFA visual stories
Use of visualisation in MFA
An analysis of the types and forms of visualisation used across 48 MFA studies, sourced from research publications and online interactive models was undertaken. The charts below show the frequency with which the Core Dimensions (plus Uncertainty and Stocks) and Bertin’s retinal variables, were included in these studies. Clearly stages, time and stocks are important dimensions in MFA studies, while size is used almost exclusively for displaying quantitative data. Yet, apart from size, there was little consistency across the MFA studies in the use of retinal variables to display information. Many of the visuals reviewed were judged overly complicated and difficult to interpret.
Figure 17: An analysis of the frequency of use of (a) the core framework dimensions (see section 3, plus uncertainty and stocks) and (b) Bertin’s retinal variables included in examined MFA studies
This finding suggests that designers of visualisations in the MFA community are mostly ignorant of the long history of visualisation theory and design principles. MinFuture seeks to redress this problem by providing a Best Practice Guide for Visualising MFA data.
Best Practice Guide for Visualising MFA
Sankey Diagrams are judged as the preferred primary diagram for visualising MFA data, as they convey both the material system structure and the quantitative values of material flows in a clear manner. However, to communicate all of the Core Dimensions, plus uncertainty and stocks, requires the use of secondary visuals (the most important are shown in Figure 18. These diagrams support the data and invite the viewer to see deeper insights. The use of interactive visual platforms, allows ‘pop-up’ windows to be simply accessed with a ‘click’. A full catalogue of best visual options approaches for communicating the core dimensions is provided in the MinFuture report "Visualising Material Systems" (Cullen & Brazel 2018).
Figure 18: Primary and secondary visualisation options for MFA
A step-by-step methodology will be described to develop a visualisation platform and to create and communicate data driven MFA stories:
- Collect: Gathering of data from e.g. GIS
- Create: Exploration phase where journeys are made and recorded. This is a two phase process in which the raw data is formatted under the elicitation phase then subsequently analysed for trends and stories under the communication phase.
- Communicate - A collective repository of stories created from the data
Creating good visualisation
The following list of questions is useful creating good visuals from complex MFA data:
- What is it for?
- What are my audience needs?
- What is the best way to represent the information?
- How should I display multivariate information?
- What does the data look like? Sketch a wire frame
- Have I included titles and captions?
- Does it support the literature, is it consistent with other visualisations?
- Does the visualisation tell a narrative?
- Can the visualisation be made interactive?
Our final word is a plea that more time be given to creating visuals in MFA research. When a researcher undertakes a typical MFA study, the data collection takes many months, the writing up takes several, yet we are lucky to spend more than a few days on the visuals. However, the reader under time pressure looks first at the title, followed by the abstract, and then dwells on the figures. A wealth of information on visualising material systems is provided in the MinFuture report "Visualising Material Systems" (Cullen & Brazel 2018). Good visualisation takes time and requires multiple iterations to perfect. Allocating more time to visualisation, not only helps us communicate our message well, but it also gives us the skills to create better visuals in the future. Practice makes perfect!
- Visualisation in the modern world is essential and foundational for communication.
- Good visualisation is the art of simplifying, creating context, and imparting meaning to data, to tell a coherent story.
- Good visualisation takes time and requires multiple iterations to perfect.
- Allocating more time to visualisation, not only helps us communicate our message well, but it also gives us the skills to create better visuals in the future.
The intention of this conference "Monitoring the physical economy requires putting raw material data in a system context" was to present, reflect on and refine key recommendations from the MinFuture project on a monitoring system for the physical economy.
The presentations held and recommendations made will soon be available via this website.
This report aims to address existing challenges by developing a methodological framework for the monitoring of the physical economy that facilitates the users in reflecting more systematically about the problems mentioned above and in developing more effective strategies for addressing them. The framework proposed is based on Material Flow Analysis (MFA), a tool widely used for tracking materials and energy in the economy.
The MinFuture project seeks to provide methods and guidelines for structuring MFA data, enabling a more comprehensive understanding of mineral and material systems. Visualisation is one of the key components described in the MinFuture Pyramid, while the MinFuture Core Dimensions describe four dimensions which are key to MFA studies: stages, trade, linkages, time. We add uncertainty and stocks to this list to create six core dimensions for which visualisation might be required.
In order to develop strategies as well as to define and reach goals concerning raw materials management, maps are needed to help navigate existing knowledge and data.
The purpose of this workshop was to further develop the roadmap for monitoring the physical economy. During the spring of 2018, the MinFuture project has held several commodity specific workshops to test the developed framework and to identify commodity specific trends, opportunities and challenges that can inform the MinFuture roadmap. Workshops has been held on aluminium, cobalt, neodymium, platinum, phosphorus and construction aggregates, and stakeholders from different parts of the supply chain has contributed to it.