HI6038 Data Visualisation Assignment Sample
Task Details
You have to answer the question :
Question 1: Principles and Standards in Data Visualisation (10 Marks)
a) Explain the principle of clarity in data visualisation and provide an example of a common mistake that reduces clarity.
b) Explain the principle of simplicity in visualisation design and discuss how this principle can conflict with the need for detail.
c) Analyse a poorly designed visualisation (you may find one online or create one yourself). Identify at least three issues in the visualisation that violate these principles.
d) Redesign the visualisation based on the identified issues and explain how the redesign improves clarity and simplicity.
Question 2: Visualising Data Distributions
a) Compare and contrast two visualisation techniques (e.g., histogram vs. violin plot) for analysing income distributions across three regions. Justify when one might be preferred over the other.
b) Discuss how outliers and skewness impact the interpretation of the income data and howthese can be addressed in the selected visualisations.
c) Propose an advanced visualisation (e.g., overlaying a density plot on a histogram) that could provide additional insights into the distributions. Explain its benefits.
Question 3: Mapping and Geographic Data
a) Evaluate the advantages and limitations of geographic visualisation techniques, such as heat maps and proportional symbol maps, for analyzing customer density across multiple cities. Provide specific scenarios where each technique excels.
b) Design a mapping strategy for a retail chain analyzing sales performance in different regions. Incorporate at least two types of geographic visualisations and justify their selection.
c) Identify potential challenges in geographic visualisations, such as data overlap or misinterpretation, and propose strategies to mitigate these challenges.
Question 4: Visualising Uncertainty and Relationships
a) Explain at least three techniques (e.g., confidence intervals, ensemble plots, gradient plots) for visualising uncertainty in datasets. Discuss their strengths and limitations.
b) Create a visualisation for monthly sales predictions with confidence intervals. Include labelled components and describe how uncertainty is represented.
c) Analyse the relationship between sales and advertising expenditure using scatter plots and bubble charts. Discuss how these visualisations can reveal correlation, trends, and outliers.
Question 5: Psychology in Data Visualisation
a) Discuss how human perception influences the effectiveness of data visualisations, focusing on pre-attentive processing and Gestalt principles (e.g., similarity, proximity, enclosure).
b) Apply the above (see part a) principles to redesign a confusing dashboard (you may create an example or refer to an existing one) to improve user comprehension. Provide specific changes and justify them.
Solution
Question 1
a) Clarity in visualizing data helps data to be communicated in an easily understandable, comprehensible, and analyzable manner. It is minimizing unnecessary complexity, avoiding visual confusion, and using a logical form that directs attention effectively. Clear visualization will represent important findings unequivocally such that the audience can easily detect trends, comparisons, and patterns (Mesa et al., 2022, p. 14358). One of the most common pitfalls that make things unclear is overdependence on the application of 3D effects in charts. An example is that 3D pie charts alter proportions in a manner where it is not easy to accurately compare segment sizes. The effect of perspective would enlarge or diminish parts and cause misinterpretation. A more suitable option is a simple 2D bar chart or pie chart in plain percentages where audiences can have an easy time understanding information without unwanted misinformation.
b) Simplicity in data visualization maintains that data will be presented in a straightforward, simple, and easily digestible manner. Simplicity is the absence of unnecessary things, few colors, and few labels or types of charts. A simple design allows the target population to focus on key observations without carrying around ornamentation or excess information. Yet the simplicity rule will from time to time find itself contradicting the demands of detail. A finance dashboard, for example, will have, on one side, to quest for simplicity and granularity of information to the optimal degree in one stroke. Being too simplistic with omission of the very crucial variables, labels, or background is likely to end in a misunderstanding or lose vital insights (Sturdee et al., 2022, pp. 23). Overemphasizing detail, on the other hand, makes the visual work hard to digest
c) Visual:
This is a bad graph because its inverted and non-conventional layout makes it time-consuming to understand. The horizontal bars, which get smaller as one moves along the graph, fail to intuitively represent a part-to-whole relationship or trend. Moreover, the absence of clear value labels makes observers estimate and this acts as a barrier to the accurate interpretation of data.
d) Visual :
This is redesigned based on same data. This design effectively displays data by region in a clear and understandable bar chart. Vertical bars are easy to compare product quantities between regions. Labeled axes, a clear title, and clear colors allow quick for university assignment help understanding of what is being displayed, making it an easy-to-consume and structured visualization.
Question 2
a) A histogram graphs distributions of data as bars, the bar height indicating how frequently income values occur in bins. It is good at showing the shape of a distribution, identifying skewness, and estimating modes. Histograms are bin-sensitive, and comparing several region distributions could involve having several side-by-side plots, which is messy
A violin plot is, however, a blend of a density plot and a box plot that illustrates the shape, central tendency, and spread of the distribution in a single plot. It is highly effective in comparing distributions across more than one category since it only indicates outliers and density. while a violin plot is better for comparing multiple distributions simultaneously, the histogram is preferred when precise frequency counts are needed.
b) Skewness and outlying values strongly influence the interpretation of income distributions (Wells et al., 2021, pp. 1-20). Outliers, such as very high salaries, can inappropriately skew the impression of central tendency and variability so that it is impossible to accurately estimate average levels of income. Skewness, and in particular right-skewed distributions (common in income data), are the warning that most people have low wages with a few having substantially higher wages, impacting the mean and inappropriately misleading summary statistics. Outliers may manifest as single bars in histograms, causing the distribution to look stretched. Bin widths may be resized or log-transformed scales used to make them more understandable. Violin plots are effective in dealing with outliers by depicting the variations in density, and the use of quartile indicators assists in pointing towards median levels of income without distorting them
c) An overlayed density curve over a histogram leverages the advantages of both plots and gives a more precise and less crowded description of income distributions. The histogram shows raw frequency counts, whereas the density curve smooths the distribution and exposes patterns independent of particular bin widths. This double visualization enables one to comprehend both the precise data distribution and trends (Sahib and Stapa, 2022, p. e3331).
Among the main advantages is improved handling of skewness—hence, the central tendency is emphasized by the density curve, and the long tail in earnings data is caught. It also facilitates detection of multi-modal distributions, where several peaks represent various groups of income. To facilitate the comparison of income distributions in three regions, having a distinct color for each of the density curves within one histogram enables easy viewing without clutter
Question 3
a) Proportional symbol maps and heat maps are suitable methods of customer density analysis in various cities, each with pros and cons A heat map employs the color saturation to reveal customer concentration, hence being suitable for the purposes of detecting concentration patterns. It is best employed in situations where companies want to detect high-traffic zones, for example, detecting customer hotspots for shop expansion. Heat maps are simplifications of information and have the tendency to obscure individual values and are difficult to ascertain the correct numbers. Proportional symbol map indicates customer density as different-sized symbols (e.g., circles) for a particular area. This method is best for contrasting customer counts in cities in geographic terms (Shanono et al., 2021, pp. 1-23). It makes highly accurate comparisons but gets confused at high-density sites if symbols overlie each other.
b) To contrast regional sales performance, a retail chain can make effective use of a choropleth map and a proportional symbol map for a strong geographic portrayal
1. Choropleth Map:
This map makes use of color gradients to indicate sales volume per region. It is helpful for easily spotting high- and low-performing regions. For instance, darker shades can be utilized to indicate higher sales, making it easy for decision-makers to spot regional trends (Wynn et al., 2023, pp. 45-49).
2. Proportional Symbol Map:
With circles or symbols placed over store locations proportional to sales revenue, direct comparison among individual stores is easier. It serves to distinguish between store-level differences in performance within the same geographic location and problems with the choropleth map.
b) 1. Data Overlap: Overlapping symbols in dense areas on proportional symbol maps hide data points.
Mitigation: Employ transparent and variable transparency, clustering algorithms, or graduated symbols to avoid clutter without compromising accuracy.
2. Misinterpretation of Color Scales: Choropleth maps mislead the data if the colors are not selected suitably.
Mitigation: Apply perceptually equal color gradients and offer an explicit legend to contextualize.
3. Boundary and Scale Distortion: Geographical boundaries can distort true influence, and huge regions can be given undue importance over tiny but very potent ones.
Mitigation: Normalize data and apply cartograms or hexbin maps for equal-area portrayal.
4. Limited Interactivity: Static maps lack the level of detail.
Mitigation: Use interactive dashboards that include filtering and zoom to enable further exploration of data.
Question 4
1.
a) Confidence Intervals:
Confidence intervals show the interval in which a parameter will most likely lie, indicating doubt regarding a point or prediction. They are suitable for the expression of statistical reliability. The power of this tool lies in its capacity to show the precision of estimates, particularly in regression analysis or scientific observations (Tawil et al., 2024, p. 79).
2. Ensemble Plots:
Ensemble plots show many predictions or simulations, and they provide a visual sense of uncertainty by plotting an ensemble of potential outcomes. It is a great way to illustrate model variability and prediction spread. The drawback is that big ensembles are difficult to interpret, particularly with datasets that are very complex, and it is tricky to highlight overall trends.
b) Visuals
This stacked bar chart is convenient to show monthly sales data with forecasts and confidence limits. One bar is used to represent each month, divided to show actual sales, forecasted sales, and interval between the top and bottom bounds of the confidence limit. This chart makes easy comparison of the sales with the forecast possible and has visualization of the prediction uncertainty.
c) Scatter plot :
Bubble chart:
They both plot points on a graph, but the scatter plot plots two variables per point, whereas the "bubble" chart only appears to use one (the x-coordinate), and only the y-axis space them out. So it's not really making use of the bubble size dimension, so it's a badly done scatter plot with wasted vertical padding.
Question 5
a) Human perception is of paramount importance to how we read data visualizations, and their usefulness depends on it. Pre-attentive processing is the brain's capacity to quickly process some visual properties (such as color, size, or direction) unconsciously. It is used to direct attention towards significant features in a visualization, such as showing significant trends or outliers. Applying strong colours or large font sizes to significant data points takes advantage of this capacity, instantly making them pop. Gestalt principles also account for the way human beings perceive data relationships and patterns (Hammond et al., 2024, p. 119466). For instance, the principle of similarity makes it easy for us to classify objects with similar observable attributes, shape or color, in a bid to ease comparison.
b) Suppose this following dashboard showing sales information with several graphs including bar charts, pie charts, and line plots of various colors. This layout makes users bewildered since they are not able to relate equivalent points of information and extract important trends.
Reconfiguring the Dashboard:
1. Pre-attentive Processing:
To make important sales figures truly pop, I would employ bold colors (e.g., red to show a decrease in sales and green to show an increase in sales), as the human eye will immediately pick up on such colors. Large, readable number labels would then be placed next to important metrics for quick reference.
2. Gestalt Principle of Similarity:
I would make sure that data which are similar (e.g., sales by regions) all look the same (e.g., blues) in different charts so it is easy to compare. This is visual consistency.
References:
Hammond, E.B., Coulon, F., Hallett, S.H., Thomas, R., Dick, A., Hardy, D., Dickens, M., Washbourn, E. and Beriro, D.J., 2024. The development of a novel decision support system for regional land use planning for brownfield land. Journal of Environmental Management, 349, p.119466. https://www.sciencedirect.com/science/article/pii/S0301479723022545
Mesa, D., Renda, G., Gorkin III, R., Kuys, B. and Cook, S.M., 2022. Implementing a design thinking approach to de-risk the digitalisation of manufacturing SMEs. Sustainability, 14(21), p.14358. https://www.mdpi.com/2071-1050/14/21/14358
Sahib, F.H. and Stapa, M., 2022. Global trends of the Common European Framework of Reference: A bibliometric analysis. Review of Education, 10(1), p.e3331. https://eric.ed.gov/?id=EJ1333444
Shanono, I.H., Muhammad, A., Abdullah, N.R.H., Daniyal, H. and Tiong, M.C., 2021. Optimal reactive power dispatch: a bibliometric analysis. Journal of Electrical Systems and Information Technology, 8, pp.1-23. https://www.researchgate.net/publication/348292284_Optimal_reactive_power_dispatch_a_bibliometric_analysis
Sturdee, M., Knudsen, S. and Carpendale, S., 2022. Data-painting: Expressive free-form visualisation. https://sorenknudsen.com/assets/publications/sturdee2022data-painting.pdf
Tawil, A.R.H., Mohamed, M., Schmoor, X., Vlachos, K. and Haidar, D., 2024. Trends and challenges towards effective data-driven decision making in UK Small and Medium-sized Enterprises: Case studies and lessons learnt from the analysis of 85 Small and Medium-sized Enterprises. Big Data and Cognitive Computing, 8(7), p.79. https://www.mdpi.com/2504-2289/8/7/79
Wells, J., Grant, R., Chang, J. and Kayyali, R., 2021. Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data. BMC Public Health, 21, pp.1-20. https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12072-1
Wynn, M.O., Brady, S., McKenna, J., Swanson, L. and George, R., 2023. Implementation of the infection control estimate: A case study on the use of a newly developed digital tool for outbreak management in the acute setting. Journal of Infection Prevention, 24(1), pp.45-49. https://pubmed.ncbi.nlm.nih.gov/36644520/
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