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COMM2501 Assessment Critical Analysis
Creation date：2024-05-14 14:15:16
Belonging category：计算机computer science
Critical Analysis

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COMM2501
Assessment  Critical Analysis
• • •
1
Analysis of “Under the Influence”
“Under the Influence” is an interactive online data visualisation of various alcohol statistics, drinking
trends, and side effects of consuming excessive amounts of alcohol. The data sets utilised range from
geographically grouped alcohol consumption statistics, trend data over a time series ranging from
1960s – 2020 and health effects clustered according to organs.
Motive
I chose this data visualisation due to its insightful nature and engaging components. Alcohol is a large
part of Australian culture, financial institutions culture and university social life, making this a very
relevant topic for those in my demographic. My attention was caught by the animated introductory
screen and the webpage is incredibly detailed, containing more than 10 different data visualisations. I
was especially engaged by their interactive components, featuring a trend estimation exercise and a
diagnosis quiz. The dataset utilised also contains very recent and relevant data up to 2020, serving as a
great conduit for spreading awareness regarding the consequences of excessive drinking.
Assessment 2: Critical Analysis
• • •
2
Data Story
This webpage has a beautifully woven data
story with a distinct linear structure which
slowly delves into more complex details and
topics. The website is able to connect all five
“Components of a Data Story.” (Heeg, R.
2015)
The introductory hook immediately provides
context and identifies the target audience –
those above the drinking age. This draws an
explanatory focus by providing the striking
statistic of alcohol use disorder. Typography
and font were used effectively for emphasis.
Subsequently, the webpage takes the user
through a journey by first introducing various
statistics and trends around the world through
visual representations and geographic heat
maps.
Next, the focus is drawn back on the main idea of the
health impacts of alcohol, supplemented by a detailed
tree map. The webpage then introduces an infographic
containing descriptions of victim’s stories followed by
further generic statistics on Alcohol Use Disorder (AUD).
These stories provide an emotional connection to the
audience as an effective approach to narrative.
Improvements to Flow
To improve the flow of the narrative, I would suggest
presenting the generic statistics before the victim’s stories
to provide additional context behind these stories.
Interactivity
There was a plethora of interactive elements
embedded in the design of the webpage
to personalise the story with relatability and
understandability. These included the
detailed descriptions on hover, the trend
prediction exercise, and the AUD quiz. I was
more compelled to read more about the
case studies after interacting with each
component.
FIGURE 1 INTRODUCTORY HOOK
FIGURE 3 INTERACTIVE QUIZ
FIGURE 2 VICTIM CASE STUDIES
Assessment 2: Critical Analysis
• • •
3
Analytical Methods
“Under The Influence” uses a multitude of data visualisations to present their story. This section will
examine their insights and shortcomings through the lens of Gestalt principles (Wertheimer, 1923) and
William’s Heuristics Analysis (William R., 2018).
Global Alcohol Consumption
FIGURE 4 TOTAL ALCOHOL CONSUMPTION
The introduction into global drinking trends is presented through a bar graph of total alcohol
consumption. The comparison to the international average gives clarity to the relative consumption of
different countries. Canva’s analysis of colour theory (Canva, 2021) supports how colour intensity is used
effectively from dark to light to represent the “overpowering” amount alcohol consumed,
Recommendations
Since this DV aims to portray impactful statistics to the audience, having a clear context is essential
(Sanchez, 2020). The context in this DV could be improved by providing the units in terms of average
daily alcohol consumption rather than yearly consumption, to enhance relatability for the audience, as
a stakeholder.
Geographic Heat Map
The next visualisation was an
interactive geographic heat
map. The additional statistics
which appear on hover were
detailed and informative.
Recommendations
Unfortunately, it was difficult to differentiate between the ranges of 10 – 15, which were quite similar in
shade, misleading the user through the similarity Gestalt principle (Wertheimer, 1923). This DV could be
significantly improved by increasing the range of colours into lighter and deeper shades to create a
more effective contrast between different countries.
FIGURE 5 GEOGRAPHICAL HEAT MAP
ON CONSUMPTION
Assessment 2: Critical Analysis
• • •
4
Stacked Bar Chart - Ranking of Countries
FIGURE 6 RANKING OF COUNTRIES
Next, was a stacked bar char of the individuals in a certain drinking category. This was one of the more
confusing graphs on the webpage which introduced bias by using the wrong graph type (Mccready,
2020). The use of percentages alongside a stacked bar char resulted in a confusing y-axis with
percentages greater than 100. I would suggest separating out the different categories in distinct bar
graphs to increase the clarity of this data visualisation.
Recommendations
The colours for “people who drank last year” and “never drink” were more similar than those “who
never drank last year” and “never drank at all”, creating bias by going against conventions. Thus, this
DV could be significantly improved by rehauling the colour scheme, with a positive/light colour such as
green representing those who rarely drink, and a darker/red colour for those who drink excessively.
Furthermore, the x-scale was disorientating, as countries were sorted in no particular order, violating the
Gestalt principle of continuity and failing the “Heuristic of Consistency”, H9, (Wertheimer, 1923) when
compared to Figure 4. A recommendation for this data visualisation would be to sort the countries in
descending/ascending order of their percentages.
Finally, the caption text was occluding the legend text which was symptom of usability issues through
the “Spatial Organisation Heuristic” (H1). This could be resolved through repositioning the legend.
Assessment 2: Critical Analysis
• • •
5
Health Impacts from
Alcohol
Another subsection was the
health impact. Unfortunately, this
DV does not fulfill the heuristic of
“Data Set Reduction” (H4) as
there is too much data crowded
into one graph with a lack of any
mean of filtering. The positioning
of bar graphs on each leaf node
to accentuate their values are
not accompanied by a scale
and do not enhance the DV.
Recommendations
A percentage of deaths may be
more appropriate for the scale,
as using “number of deaths”
deters understanding of their
relative threats. This DV could be
further simplified by removing
effects with null data and
grouping by how common the
side effect is.
Overall Recommendations &
Reflection
In general, “Under the Influence” was an engaging visual experience with an
insightful message on the consequences of excessive drinking. Overall
improvements would be to focus on reorganisation the texts and labels for a
more seamless reading experience. Furthermore, the revision of scale and a
more appropriate choice of colours for all of the visualisations would have
aided understanding of the core concepts. In the future, support for mobile
accessibility would also be a great addition.
In summary, I learnt that the frightening side effects of drinking affect a
significant proportion of the population and should not be taken lightly.
FIGURE 7 TREE MAP OF HEALTH IMPACTS
FIGURE 9 INCOHERENT MOBILE
RENDERING OF THE WEBPAGE
FIGURE 8 CLOSING TYPOGRAPHY
Assessment 2: Critical Analysis
Case category
Tag cloud
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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