Literature Synthesis - Visualisations

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1 Literature Synthesis - Visualisations By Jacques Questiaux QSTJAC001 Abstract This review takes a look at current technologies and methods that are used today to visualise data. Visualisations are defined as a way of representing a volume of data in an image. A specific type of data called a data cube is of particular interest when it comes to visualisations, due to the way the data is stored. It is an abstract data structure but can be applied to many scientific fields. Techniques such as zoom graphs and stereography are looked at for visualisation purposes to see how they can be utilised. Software such as Karma, PGPLOT and 3D Slicer are looked at to see what programs are used presently to visualise data, and a comparison is made to determine the best aspects of these programs. Introduction Visualising data is important [1] for gaining an overview about information that is in a format that makes it difficult for people to grasp at a glance what the relevant pieces of information that they are looking for, are. The aim of this research is to find the best possible way of visualising astronomical data. By reviewing past techniques and technologies, a better understanding of what needs to be present in a good visualisation can be acquired. The applications for visualising data span across many fields of science. Two fields that are very similar in data representation types are the astronomy and the medical fields [3]. They both have 3D data sets that are very large and require some form of visualisation in order to make sense of the data. There are many ways to visualise data. Some data sets require 2D visualisations, while others require 3D visualisations [4]. Both views have their strengths and weaknesses, and when it comes to astronomical data, both visualisation types have their place. In order to ascertain what types of data are best represented by these different visualisation types, investigation needs to be conducted. The motivation for this research is purely to find out what technologies and methods exist presently and to see what aspects of them can be improved upon. Taking the best parts of existing technologies and combining them to create a new and improved way of visualising data is what is planned and hopefully can be learned from delving into past research papers and looking at current platforms for creating visualisations. Visualisations are a valuable tool when it comes to research and education, as they can help students to understand what is going on within a volume of data. Often teachers and lecturers find it difficult to effectively communicate certain aspects of the content that is being taught [11], but with the aid of visualisations, this problem can be effectively remedied. What is a visualisation? A visualisation is a way of representing a data set in the form of an image. Most visualisations come in the form of a 2D or 3D image. The way in which the data is represented within the 2D or 3D image relies heavily on the type of data that one desires

2 to visualise. Since visualisations are important when trying to summarise data, they occur in many different scientific fields [1]. This makes research into visualisations important since many scientific fields use visualisations to summarise their data. Visualisations are a form of data abstraction, much in the same way that statistical values such as means, standard deviations, ranges etc give an overview about a volume of data in a descriptive way. Visualisations have the same goal as these statistical values, which is to summarise data and give an abstract view of the data in such a way that it is useful to the user. The saying A picture is worth a thousand words is a good way to explain the usefulness of a visualisation, although in this case it should be A visualization is worth a large volume of data. Visualisations are the perfect way for conveying a summary about a data set. Since people are generally good at interpreting visual data, it makes sense to represent data visually so that they can process it more efficiently. As shown in the figures below, it can be seen that the diversity of the forms which visualisations can take on is vast. There are many ways of representing data visually, and as such, choosing the right type of visualisation can be crucial for gaining knowledge about the data from the visualisation. If an unsuitable type of visualisation is chosen, the data could be easily misinterpreted by the user. The purpose of this review is to find out the existing visualisation techniques and to find out which types of visualisations work best for certain types of data sets. Figure 1 A 3D visualisation Figure 2 Charles Minard's information graphic of Napoleon's march Figure 3 Venn diagrams

3 data it is quick and easy to see trends within the data, or notable outliers to the data. Current methods for visualising data For this review, astronomical visualisations and visualisations that use similar data to astronomical data will be the main focus. Figure 4 Graphs Types of data being visualised Visualisations can be applied to almost any data type whether it is quantitative or qualitative data. In the field of astronomy, there is plenty of data that can be visualised in some way. There is a vast amount of data in the world that can utilise visualisations - this can range from geographical maps to astronomical imagery. One of the standard formats used for scientific data is the notion of a data cube. A data cube is quite a broad term, but can be applied to data that has three or more dimensions to it. Usually threedimensional data can be displayed correctly in the form of a data cube visualisation, but up to 5 dimensions can be rendered by using colour, as seen in Fig. 1, and time as the 4 th and 5 th dimensions respectively [5]. This can be useful because it allows the user to gain information about the data cube by looking at one image. On the other hand, the visualisation can become cluttered with too much information and can detract from the users experience by making it difficult to interpret important parts of information from the visualisation. Spreadsheet or tabulated data is probably the most common form of data to be visualised. The same visualisation techniques for data cubes can be applied to spreadsheet data. By visualising the Multiscale visualisation [6] is a technique used for displaying information relating to data cubes. This method is more of an abstract way of navigating data cubes. Multiscale visualisations use a method called a zoom graph [6]: this is where a graph of the data is displayed to the user at first as an abstract overview of the data. When the user zooms into a more specific location within the graph, the graph starts to display more information about that certain subsection. This includes adding more fields of data to the graph. This organises the graph in a similar way to the tree structure commonly used in computer science. The top nodes of the tree give more of an overview of the data, but when branching down the tree, the data presented to the user gets more refined and detailed. This allows users to navigate the data easily and extract the data they are looking for. The downside to this method is that it makes comparing instances of data that are not within close proximity to each other difficult. A technique also mentioned in [4] is the use of stereography when visualising data. This can help with the visualisation imagery. Allowing a 3D visualisation to have a feeling of depth can add to the effect the visualisation has on the user. Even a 2D visualisation can utilise stereography, as when done correctly, 2D visualisations can appear to be 3D, allowing effectively another dimension to the data. This can be an alternative for creating a 3D visualisation projected onto a 2D surface such as

4 the computer screen. Allowing the user to see depth in a 2D visualisation automatically allows the user to absorb three dimensions worth of data, but only having to plot two dimensions on the visualisation. Humans in general don t process depth as well as the horizontal and vertical planes, therefore this isn t the most reliable way to convey data to the user, but it can still be used to give a general overview. Karma [7] is a widely used program for visualisation data [4]. It is a suite of tools used for constructing visualisations for astronomical data. Unlike most of its competitor products, Karma provides all the tools you need to make a visualisation itself. Its counterparts are a scripting language and graphic libraries to use within a programming environment [4]. Karma does provide the same libraries other tools provide, but what makes Karma so widely used is the fact that it has its own tool set for making visualisations. PGPLOT [8] is the most used [4] graph plotting API. The library is very old and out-dated, as it was written in FORTRAN, but despite this, it is still used often by researchers. The library has many plugins that have managed to keep it up to date and aligned with the hardware of today. The main reason it is widely used is that is simple to use and can plot standard graphs. It is a very general purpose graph plotter but with very limited advanced functionality. 3D Slicer [9] is a free and open source 3D visualising program for plotting 3D data. It is mainly used is the medical field for visualising medical data. Even though this is mainly a medical visualising tool, it has been used for visualising astronomical data [3]. It specialises in 3D volumetric rendering which is useful in the astronomy field. Astronomical data is often difficult to visual in a 2D plane, so by using 3D Slicer it is possible to visualise a star cluster or some other stellar object in 3D, and to take cross sections at certain places. According to [3], 3D Slicer has aided astronomy research and has been used for many publications, and has helped identify stellar objects such as outflows [10]. These platforms were chosen because they are some of the most widely used programs within their respective fields. There are many other platforms that have been noted and looked over, but for the purpose of this paper we will only need to look at aspects from the best technologies available. By critically examining the different aspects of these platforms, we can formulate our own visualisation techniques that will improve upon the current techniques that are in place. Conclusion Looking at the current platforms for visualising data, it has been observed that there is plenty of room for improvement. An opportunity has arisen from this research which is to provide a system that is user friendly, requires no compiling or coding experience from the end user, and provides clean and detailed visualisations that remove clutter from the data. As seen previously, providing an easy way for a user to create graphs or other visualisations is what researchers are looking for. Providing abstract views of data and allowing users to pin point exactly what they are looking for quickly and efficiently must also be a top priority. Looking at current programs such as Karma, PGPLOT and 3D Slicer, a good overview of what needs to be in a visualisation program has been found. Combining the techniques used from these programs shall make a satisfactory visualisation tool for researchers. Astronomical visualisations are the main focus of this research, but it is not only restricted to this field.

5 To summarise what needs to be taken away from this research is: 1. The program should have a PGPLOT-like interface so as to let the majority users of visualisation programs (PGPLOT users) feel accustomed to the software immediately. 2. Provide tools and a library to be used within a programming environment so as to allow users to make visualisations using the set of tools, or by using the libraries to create their own. 3. The libraries provided should be able to be used within modern day programming languages for example C++, Java and Python. 4. Provide functionality in the same way as 3D slicer so as to allow users to create visualisations from all fields of science and abroad. This in turn means that the program should provide support for multiple formats. 5. Provide a method of abstraction. Use a zoom graph or some other method to allow users to get an overview of the data they are visualising before they need the extra detail.

6 References [1] Mann, B., Williams, R., Atkinson, M., Brodlie, K., Storkey, A., & Williams, C. (2002). Scientific Data Mining, Integration, and Visualization. Integration The Vlsi Journal, (October), [2] Shneiderman, B. (2002). Inventing discovery tools: combining information visualization with data mining. Information Visualization, 1(1), doi: /palgrave/ivs/ [3] Borkin, M., Kauffmann, J., & Halle, M. (n.d.). Application of Medical Imaging to the 3D Visualization of Astronomy Data. Computer, 5-6. [4] Barnes, D et al (2006). An Advanced, Three-dimensional Plotting Library for Astronomy. Astronomical Society of Australia, 1-12 [5] Gray, J., Bosworth, A., Pellow, F., & Pirahesh, H. (1997). Data Cube : A Relational Aggregation Operator Generalizing Data Cube : A Relational Aggregation Operator Generalizing Group- By, Cross-Tab, and Sub-Totals, 1(1). [6] Stolte, C., Tang, D., & Hanrahan, P. (2002). InfoVis 2002 Best Paper Multiscale Visualization Using Data Cubes. Proceedings of the IEEE. [7] Karma xray - e/karma [8] PGPLOT [9] 3D Slicer - [10] Astronomical Medical Project - [11] Yair, Y., Schur, Y., & Mintz, R. (2003). A Thinking Journey to the Planets Using Scientific Visualization Technologies : Implications to Astronomy Education. Science Education, 12(1).