Microsoft Word - Assignment2_Q3_2022.doc
School of Computer, Data & Mathematical Sciences
Visualisation - (COMP7016)
Q3 2022: Assignment 2
Multidimensional Data Visualisation
(Assignment deadline: Sunday 04/09 11:59pm on vUWS)
Assignment Details
For this assignment, you are required to identify and develop one (or more) visualisation(s) for
one of the given multidimensional datasets (We give two data sets, you only need to pick one of
them) using existing software and/or programming platform, (e.g., Tableau, TabuVis, R, Python,
etc.). Based on the visualisations, you can explore to find insight, patterns, ir(regularity) and
interesting properties from the visualisation. Note, it might be required to clean and process the
data before using them in visualisation tools.
You are also required to write a report with 1500 words (note: your report can be longer than
1500 words if necessary) on the following aspects:
• Brief technical details of the used visualisation method(s),
• Discussion on the advantages and disadvantages of the visualisation method(s) compared
with other methods in the literature. Can the visualisation method(s) be used effectively
for large multidimensional data sets and why? Which other analysis techniques could be
helpful to support the visualisation in the big data analysis? Such as in data processing,
automated analysis, etc.
• Discussion on the analysis results and findings on the data sets,
• Discussion on other aspects, literature review of related work and your critical thinking on
the visualisation(s).
Note: images (as figures) are essential and should be included in the report to illustrate the
visualisations, results, and findings.
Marking criteria for the assignment includes
• Development of satisfactorily visualisation(s) for multi-dimensional data (50%). The
marking will be based on how well the visualisation method presenting the
multidimensional data. Interaction should also be included in the visualisation.
• A report on the technical description of the visualisation, analysis results and other
aspects (50%)
Deliverables
Students must individually complete the visualisation(s) and the report. The report should be
typed and submitted online through vUWS as a Word or pdf file. A high standard of professional
English and neat logical structure (including consistent and complete referencing style) is
expected.
Declaration
You are required to submit a declaration with the following claim (in a text file or world file).
DECLARATION
I hold a copy of this assignment that I can produce if the original is lost or damaged.
I hereby certify that no part of this assignment/product has been copied from any other student’s
work or from any other source except where due acknowledgement is made in the assignment.
No part of this assignment/product has been written/produced for me by another person except
where such collaboration has been authorised by the subject lecture
tutor concerned.
Submission
Together with the report document, the declaration, visualisation program(s) and data sets, and
the report should be submitted via vUWS before the deadline for marking purpose. All these files
should be zipped into one file with your student id as the zipped file name. A submission that
does not follow the formats is not acceptable (i.e. Doc or PDF format for the report document,
and ZIP file for source codes and others). No hard copy of the work and email submission is
acceptable.
XXXXXXXXXXpdf
COVID 19 Data Visualisation using Tableau
Introduction
In our day to day lives, we encounter different and various type of information. This can also
vary by size, form and source of obtaining the data. To better understand this, people would leverage
on different visualisation techniques. Data visualisation, according to Heitzman (2019), involves
gathering of data and converting them into graphical representation to better enhance in terms of
presenting insights and meaning about the information. As also discussed by Kelleher and Wagener
(2011), visualisation is significant tool in presenting and communicating information as it is able to
synthesize huge amount of data into an effective graphical representation. Basically, the purpose of
having this can be
oadly categorized into two which are data analysis and data presentation.
To be able to perform visualisation, Senay and Ignatius XXXXXXXXXXproposed these steps: Data
Manipulation, Visual Mapping and Rendering. On the other hand, Fry XXXXXXXXXXprovided more steps to
consider particularly in dealing with large data set. These seven steps are shown below.
Figure 1: Data Visualisation Steps
The initial step is, of course, to obtain the data from a certain source. This is basically the data
acquisition part. After that data parsing would need to be performed. This can mean exploring the data
and organize the data structure. Once the structure is organized, filtering can easily be done to ensure
that only data of interest are selected. Next would be data mining or for other they also consider this as
data enrichment wherein some additional techniques are implemented to be able to understand any
pattern that exists. From there, tools and techniques can be identified on how to better illustrate the data
e.g. map, bar, scatter plot etc. That can be enhanced by implementing graphic design methods. One way
is by ensuring that colour and shapes are properly used to provide better readability. Then finally,
interaction can also be added to enable used to explore the data and/or control the features the user
wants to see (Fry, 2008).
Tableau as Data Visualisation Tool
One of the visualisation tools that can be used to implement those steps discussed is Tableau.
Tableau, as introduced its website, is said to be a powerful, flexible and secure data visual analytics
platform (Tableau, n.d.). Among its various software available, Tableau Desktop, an easy to use
interface, will be used in this paper to implement different visualisation techniques. Aside from that,
Acquire Parse Filter Mine Represent Refine Interact
Dataflair XXXXXXXXXXalso mentioned the other benefits of using this tool such as high quality of visual image
capabilities, strong and reliable performance, multiple information source connection among others. In
the succeeding parts of this paper the different visualisation generated from Tableau will be discussed
including on how the visualisation steps mentioned were considered.
COVID 19 Data Set
As mentioned earlier, the first part of Data visualisation is to identify the source. In this case,
this paper will use the COVID 19 data set from Our World in Data website (Ritchie, XXXXXXXXXXHowever,
it will only consider the data reported until May 21, 2020. As also mentioned in their website. There
are four main metrics in data set which are total confirmed cases, total deaths, new confirmed cases,
and new deaths. Details on number of tests, population, location, date are also other fields available in
the csv source file. Using the VLOOKUP method from Excel, the author has also added new field which
is continent wherein the aim is to improve the visualisation that will be performed.
Visualisation Implementation
After exploring the data set, it would be a nice start to show the global count using a bar chart.
On the graph below, it shows there the global summary of total confirmed case and total deaths as of
May 21, 2020. Note that Total test is empty hence author simply showed these two to provide a global
count for this pandemic.
Figure 2: Global COVID Count of Confirmed Case and Death as of May 21
To track the count for both over time, a line chart can provide more details about this as shown
elow. This line chart is able to provide information regarding the increasing number of cases of both
metrics. On below, dual axis was used to plot the on the same graph. It was also helpful to have unique
colour on each metric and include a label of the peak of new cases for both. For example, on the week
of April 6, there were 52, 531 new death cases which was at its peak. On the other hand, it was on the
week of May 11 that the number of new cases was reported to be at its peak of 608,652. Also, another
good thing that we can observed on both metrics is the drop on new cases which hopefully would be
continuously going down.
Figure 3: Global Covid Trend of New Confirmed Case and Death as of May 21
From both bar and line graph, it was shown some significant numbers on COVID confirmed
cases and death count. Kiss XXXXXXXXXXdiscussed line graph would definitely be useful to present trend
which indeed serve its purpose in this case; hence, it was effective to visualise the global overview.
However, if all of the figures of each countries were included it would not be that effective and would
not give a clear picture of the information. As mentioned by Gulbis XXXXXXXXXXand ROM (n.d.), bar and
line graphs may not be that suitable for large scale visualisation.
Knowing that limitation, another technique that can be explored to somehow show the highlevel
information per country is using a map. As mentioned by Gulbis (2016), map can provide an overview
(but not an absolute count) of the data across various location in this case country. As also suggested
y the same author, using overlay bu
le can better improved the visualisation which is implemented
in this by adding the blue-green circle. From that, its size can aid in identifying which countries have
higher number of confirmed cases. From Figure 4, it can be observed that United States has the most
number of cases. By hovering the pointer on the circle, the total case can be checked which is 1,551,853.
Furthermore, each continent was given unique colours. In doing so, another insight can be
observed from the map in terms of which continent has more countries having high confirmed COVID
cases. From the below map, it can be noticed that there are a lot of countries in Europe with high cases
of COVID as shown by the blue-green circles.
Figure 4: Total COVID Cases per Country using Symbol Map.
The good thing on symbol map as discussed by Mace (2014)