Foundation Skills in Data Analysis 2022

Foundation Skills in Data Analysis
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Part A: Evaluation Criteria
This section entails developing guidelines that need to be adhered to when selecting the correct
type visualization for a data set.
i. Heat maps should be used to visualize volumes of locations or events within a
geographical region. The ability of the maps to colour communicate makes them
reliance in displaying a generalized view of numeric values (Green, 2011).
ii. Heat maps are effective when there is need to draw attention to trends. The maps are
normally self-explanatory and hence effective when there is need to display
geographical trend of data (Green, 2011).
iii. The use of doughnut charts should be restricted to data with very few categories.
iv. The use of doughnut chart is effective when presenting the visualisation of a part to
whole data. The pieces of the information presented in the chart ought to add up to
100% (Odessa, 2019).
v. Pie charts should be used when the information is meant to make comparison of
various components as a percentage of the total.
vi. Combined column charts should be used when there is need to compare two or three
groups of data. The bars are drawn side by side hence allowing the user to visualize
which group of data is higher (Kelley & Donnelly, 2009).
vii. The data in a combined column chart ought to be independent of each other.
viii. Where the data in question refers to specific locations for instance business and crime
scenes maps can be used for visualisation.
ix. When the researcher needs to give a clear distribution of points within a geographical
set up maps should be used.
x. Qualitative geographical information should be presented using maps (Aparicio &
Costa, 2014).
xi. The use of a bar chart is an effective selection where the data labels are long since the
bars lie horizontally.
xii. The use of a bar graph is effective when there is need to do a comparison between
different groups of independent data sets. It can also be applicable in tracing changes
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overtime. The graphs are though only effective when the changes in question are
larger.
xiii. Grouped column charts ought to be used when comparing elements between different
categories as well as making comparisons across categories (Jansen, et al., 2015).
xiv. Stacked charts are also appropriate in presenting a part to whole relationship.
xv. To present and sort qualitative data tables can be used. The use of tables is more
relevant especially in situations where data is sorted with an intention of identifying
the underlying pattern.
xvi. Tables should be used when comparing individual values. Unlike with charts tables
can be applied to data that is comprised of multiple sets of data to compare individual
values of the set.
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Part B: Report Evaluation
The report evaluation involves selecting charts graphs and tables from a report and analysing
them based on the criteria developed in section A above. The visualisations which were
evaluated were obtained from the 2016 annual report of world health organization (World Health
Organization, 2018).
a. Heat maps
In the heat map presented below the WHO intends to display the global preparedness for the
influenza virus in terms of capital located for vaccination, manufacturing of pharmaceuticals as
well as diagnosis. The use of the heat map is deemed appropriate as it adheres to criteria (i), the
map allows the visualization of the influenza preparedness based on areas where resource
allocation is showing progress. In addition, the map is also effective as it in accordance with the
criteria (ii). The map displays the global trend of the influenza preparedness. The feature of the
heat map that allows it to give a geographical visualization of the events can be of use in
displaying location trend hence the selection of the chart is appropriate. The map gives the
intensity of the situation at a particular location this way the user of the information is able to
have physical view of where the events are taking place.
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Figure 1: Heat map (World Health Organization, 2018)
Doughnut chart
The figure 2 presented below presents a doughnut chart as used in the WHO (2016) report.
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Figure 2: Doughnut chart (World Health Organization, 2018)
Based on criterion (iii) the chart should only be used to visualise data with very few categories.
In the case of the chart above the number of categories presented are only 5, this can easily be
presented in the chart giving an appropriate view of the data set. Also, according to criterion (iv),
the chart is appropriate for visualizing a part to whole data set where the components of the set
add up to 100%. The 5 categories which have been presented in the chart gives a total of 100%
hence it adheres to the criterion. In addition, the categories are displayed as a percentage of a
whole. All these features can be used to conclude that the use of the chart was appropriate.
Pie Charts
Figur3 gives a visual presentation of a pie chart.
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Figure 3: Pie chart (World Health Organization, 2018)
Based on criterion (v), a pie chart ought to be used in a case where the information being
presented is meant to give a comparison of elements based on their proportion of the total. In the
chart presented the slices gives a visual view of the information on PIP countries against that of
other countries. The pie charts for ‘started’, ‘finalizing’ and ‘published’ are all presenting
dependent elements. For instance, an increase in the proportion of started for the PIP countries
will mean a drop of the same component for the other nations. Just by looking at the slices one
is able to visualize which of the elements forms the larger category. The pie charts are able to
give proportional comparison hence are an effective chart to apply for the case of the data. The
selection of the chart thereby gives an effective presentation of information in the report.
Combined bar graph
The figure 4 below gives a visual view of a combined column graph
Figure 4; Combined bar graph (World Health Organization, 2018)
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As per criterion (vi) a combined column graph is necessary when there is need to quantitatively
make comparison of two or more sets of data. In the diagram above the report makes a
comparison regarding the change in the IHR risk in two sets of countries that is all countries and
PIP countries. The use of the combined column graph is thus effective in this case as it allows
the user of the information to have a glimpse of how the two sets of nations do compare. Also, as
per the criterion (vii), the data components being displayed in the chart are independent of each
other. Any increase in the values of the PIP nations do not imply that the values for the all
countries will drop. The selection of the chart can therefore be concluded to be effective
considering the nature of data being presented.
Map
In figure 5 below the researcher intends to present the nations where regulatory capacity is being
built to assist curb the influenza virus. Unlike the other type of charts such as pie charts that are
meant to display quantitative data, the map can be used to visualise qualitative data. The case of
influenza regulations may not be possible to visualise in a pie chart. The chart thus adheres to
criterion (x). also, the information is meant to show geographical location and hence is in
accordance with criterion (viii). Finally, since the map give a distribution of how the influenza
regulation capacity building is taking place, it adheres to criterion (ix). The use of the map as a
chart to present data is thus an appropriate selection.
Figure 5; Map (World Health Organization, 2018)
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Bar chart
Figure 6; Presentation of data in a bar chart (World Health Organization, 2018)
The bar chart presents comparison of percentage target that have been achieved in dealing with
influenza based on a number of approaches. The comparative nature of the data being presented
means that the selection of the chart adheres to criterion (xii). A glimpse of the graph enables
the user to quickly gauge which approach has been more successful in the fight against influenza.
Also, the criterion (xi) provides that the bar chart is used for data sets with long labels, this is
exactly the case in the graph above hence it is a recommendable selection for visualisation of this
type of data set.
Stacked column chart
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Figure 7: Presentation of data in a stacked column chart (World Health Organization, 2018)
The data is meant to make comparison in expenditure between each area of work while at the
same time making comparisons across different years. The nature of visualisation necessitated in
this case is in line with criterion (xiii). Furthermore, the data presents the percentage of the whole
that is occupied by each period hence it adheres to the (xiv) criterion. Taking into account the
nature of data that was to be visualised the selection of the graph is a recommendable action.
Tables
The report by WHO (World Health Organization, 2018) make use of tables in a number of cases,
the table provided below in an example of the many tables that have been used in the report.
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Tabular presentation of data (World Health Organization, 2018)
The table above presents the nations that are undertaking planning for deployment and the
specific regions that they are located in. The criteria (xv) and (xvi) give guidelines for use of a
table. In the case presented above the data is qualitative in nature and hence so as to identify the
location where planning for deployment to curtail influenza is taking place, the tabular
presentation of the data is vital. Also, by using a table it is easy to note the individual nations
with regard to their geographical region. The report thus respects the two criteria. It can therefore
be concluded that the visualization selection is appropriate.
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References
Aparicio, M. & Costa, C., 2014. Data visualization. Communication Design Quarterly Review,
3(1), p. 7–11.
Green, D. A., 2011. A colour scheme for the display of astronomical intensity images. Bulletin
of the Astronomical Society of India, 39(1), p. 289–95.
Jansen, Y. et al., 2015. Opportunities and challenges for data physicalization. Proceedings of the
33rd Annual ACM Conference on Human Factors in Computing Systems, p. 3227–3236.
Kelley, W. & Donnelly, R., 2009. The Humongous Book of Statistics Problems. New York:
Alpha Book.
Odessa, C., 2019. Powerful Drawing Solution. [Online]
Available at: https://www.conceptdraw.com/examples/donut-chart-wikipedia
[Accessed 6 August 2019].
World Health Organization, 2018. Pandemic Influenza Preparedness Framework. [Online]
Available at: https://apps.who.int/iris/bitstream/handle/10665/258844/WHO-WHE-IHM-PIP-
2017.01-eng.pdf;jsessionid=2659BE3ED81677529A682BF7B95353CA?sequence=1
[Accessed 6 August 2019].
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