COS80025 Data Visualisation Report Sample
Assignment Details
Data visualisation is not only about data presentation, it must also be able to communicate information accurately and tell a story. You will write a 6-8 page research paper that describes and supports your visualisation work with theoretical foundation, related work, and methodology. This is to establish credibility and integrity of the visualisation technique used, and recommend that this is a good way to visualise the selected data.
Project Overview
You have the option to start a new data visualisation project or use the data visualisation project report task. Your goal is to write a scholarly paper of your data visualisation project supported by theoretical foundation, related work, and methodology. The paper structure is as follows:
Title
1. Introduction
- Background and motivation
- Question or problem to answer (Objectives or aims of doing this data visualisation)
- Structure of the paper
2. Theoretical Foundation
- Domain of the data
- Visual analysis idioms used
3. Related Literature
- Similar work done on the domain
- Existing works describing what (e.g. scenario) you want to communication and the importance of communicating these (How are the questions/problems above by others)
4. Methodology
- Data source
- Data processing such as cleaning, merging and classification
- Selection of design, mark, channel, graph features that you added, such as colour blindness, interactivity, storytelling, Gestalt, aesthetic, etc.
5. Analysis
- discussion of each information (e.g. Scenario) and how the questions/problems addressed by the visualisation
- discussion of accuracy, prediction, forecast (whenever applicable)
- discussion of how visual encoding idioms, design mantras, human perception principals, comprehensiveness, completeness, effectiveness applied. (In this part you should employ your learning of all lectures to analyse your design based on principals that you learned throughout the semester.
6. Conclusion
- discussion of what the data visualisation had achieved and/or communicated
- discussion of the limitations of the developed visualisation and what can be improved in the future
Tasks
You will need to accomplish the following tasks. You should apply the suitable techniques covered in the lectures and tutorials.
• Conduct research on the domain and key information that needs to be communicated. This is demonstrated by the references cited in the report.
• Formulate the problem as a data visualisation story telling task.
• Perform at least three appropriate data visualisation to tell the story.
• Perform data visualisation analysis
• Write a scholarly paper using IEEE paper template and supported by relevant references (can use EndNote as refereeing tool)
Solution
I. INTRODUCTION
Background and Motivation
Australia’s healthcare system is characterized by high quality and accessibility of medical services while at the same time grappling with population health management and specifically dealing with a number of what might be considered fatalism-related deaths. It is important to know mortality trends in order to better direct health systems plans and funding. Examining the graphical representation of data concerning leading causes of death, some recommendations can be made as to demographic characteristics of risk, for example, dangerous age and gender. It not only clears up the understanding of modern priorities in combating diseases but also helps to track trends that could potentially go unnoticed in a pool of statistical information [1] for university assignment help Through the use of visualizations, these trends can easily be understood and put into practice by the various players in the healthcare sector, governments, and society at large. Furthermore, visual representations of data can enhance health literacy by disseminating important risk information, encouraging preventative behaviors, and engaging audiences in dialogue, which all contribute to the advancement of the country’s healthcare and its continued adaptation to the needs of the increasingly diverse Australian population.
Research aim
The aim is to visualize Australia's 2019-2021 mortality data to reveal age- and gender-based trends, supporting informed public health decisions.
Objectives
? To analyze age- and gender-specific mortality trends in Australia from 2019 to 2021, highlighting significant health risks within demographics.
? To communicate key insights through visualizations, aiding policymakers in prioritizing health resources effectively.
? To enhance public awareness of prevalent health issues, promoting preventative measures and informed health choices among Australians.
Structure of the Paper
.png)
Fig 1: Structure of report
II. THEORETICAL FOUNDATION
Domain of the Data
This data can be categorized within public health and epidemiology which examines trends in mortality in Australia by various population groups. The use of mortality data can be very beneficial when it comes to evaluating health risks and, even more so, risks that are selective in regard to gender or age. Examining the primary causes of death, the respective public health authorities can identify trends in the increase in mortality from chronic diseases or potentially preventable diseases. It shall help in the formulation of policy-making information to relevant authorities to the executive power of targeting certain strata of population for health programs or for distribution of their healthcare resources [2]. Moreover, mortality data are used in educational activities wherein people are made aware of the major threats to their health which may lead to changes in behavior, testing, or immunization. Informing the public about such patterns enhances their ability to take proper care of their health and empowers them with the knowledge that can help diminish almost all avoidable deaths, thus leading to a healthier population and less strain on the healthcare system.
Visual Analysis Idioms Used
The techniques used in the visual analysis for this project in the presentation of mortality trends on age and gender are the following. Bar charts can be used to represent the age distribution and make comparison between the age-specific death rates and show such features as higher mortality of individuals of advanced ages. Gender-disaggregated stacked bar charts extend these findings to identify how causes of death may vary within the different age groups for men and women. Also, line graphs illustrate rate changes and study the year-to-year variations of mortality caused by certain diseases, which is vital to determining new threats [3]. The chosen software for visualization is Tableau, as this provides not only interactivity and customization options for these insights. Each such visual is developed with the intention of presenting complex information with ease of comprehension and inclusion into the public health interface such that it improves decision-making.
III. RELATED LITERATURE
Similar Work in the Domain
A variety of research has pointed to the need for health data visualization, especially in relation to mortality rates, as a vital technique for comprehending the direction of public health and guiding decisions made at different policy levels. An example of venture in this line is the Australian Institute of Health and Welfare (AIHW), which continually publishes reports presenting mortality information across various groups [8]. In their Australia’s Health reports they use different forms of graphs as well as trend analysis of issues related to life expectancy, causes of death, and health differences between population subgroups [3]. These visualizations are useful to gauge new strains of diseases and direct focus on treating them, thus, aiding in improved public health care. Another significant data source is the “Cause of Death, Australia” report which also offers a great deal of graphical data on the mortality causes in the course of history. This report contains several web-based visualizations with integrated analysis to enable users to further dissect the mortality dataset by age, sex, and region [9]. They are highly effective when it comes to helping researchers and policymakers establish improved comprehension of the impact of specific health issues within various groups of individuals and jurisdictions.
At the international level, mortality and morbidity are dissected in the Global Burden of Disease (GBD) study; this has graphs that compare Australia with other countries. Thus, the results outlined in GBD assist the reader in positioning the local health tendencies in the international framework and emphasize the importance of creating concrete health action plans to tackle the outlined problems [4]. In addition, an essential and basic kind of data is in the area of mortality, where statistical publications are made by the Australian Bureau of Statistics (ABS) and can be presented in the form of an appealing picture or activity graph. These resources help a great deal in public engagement as they present formal statistical data into easy-to-comprehend graphics [10]. It also helps spread the word as it leads to an active public that can engage in any health-related problems or projects. Collectively, these projects demonstrate the value of using visual aids in promoting knowledge translation and utilization in public health.
Importance of Communicating
Mortality data visualization plays a significant role in public health decision-making since it eliminates the possibility of large amounts of information being ignored, enables the interpretation of trends and potential pressing health problems at a glance, and helps to understand the essence of the presented data. Through depicting mortality causes, the existing health authorities can target high-impact approaches and appropriately order preventable disease death rates where they are high. Additionally, the use of appropriate graphics aids awareness programs since such visuals provide tendencies of how certain health issues affect the population and this leads to active participation in the practice of healthy lifestyles [5]. These visuals, focusing on the differences in mortality based on age and gender, can be used to design purposeful health promotion education to develop preventive and early detection health campaigns. Lastly, the visual representation of mortality helps to enhance public awareness of mortality data, thus creating a healthy discourse and leading to a healthier community due to better and informed strategies in combating various health challenges.
IV. METHODOLOGY
Data Source
The dataset used in this study is obtained from Kaggle, focusing on the major causes of death in Australia for the years 2019-2021. These factors are crucial in the study of public health as this dataset includes the age, divided into intervals, and gender, marked as male and female. The dataset also categorizes death causes into different types such as medical ailments cause of death, accidents, or any other related factors. This approach is useful to permit an extensive analysis of mortality patterns for all the groups of the population, which is useful to understand the relationship between age and gender in the health sector [5]. It also supports priority setting for policymakers as well as identifies the important trends and at-risk populations. Furthermore, the findings can be used in educating various groups of people to reduce the high-rate prevalence of the leading diseases in the society and therefore improvements to the health standards and a decrease in the mortality rate in Australia.
Data Processing
Data transformation for this project involved cleaning, combining, and categorizing the mortality dataset with the help of Tableau software. First, gaps within the data or incongruities were resolved for data cleansing purposes prior to analysis. The collected dataset was further divided into groups of age and gender, which helped to compare the data sets. Hence, some calculated fields were developed to generate other useful figures, including age-adjusted mortality rates [6]. Methods of data combining applied additional datasets, increasing the density of the examination. Interactive elements of Tableau were used to modify the characteristics of the visualizations to successfully translate the raw data obtained from public death certificates and represent variations in the causes of mortality between populations.
Design Elements in Visualization
In organizing the bar charts for displaying mortality information, some important decisions were made that would assist in readability and effectiveness. The color contrast was employed to mark gender differences and deploy different colors for male and female bars to allow direct comparison. Labels on both axes were clear, with ages labeled on the abscissa and mortality rates on the ordinate to avoid confusion. The title of the chart was designed to be extremely concise so as to clearly describe what the chart is about [7]. Also, grid lines were incorporated in order to assist those reading the values to do it accurately. In combination, these design elements were chosen because their implementation enhances user interaction and enables immediate comprehension of mortality patterns by age and gender.
V. ANALYSIS
.png)
Fig 2: Percentage of deaths based on age
This bar chart shows the proportion of deaths in Australia targeted in different age categories. It presents the groups in a manner that arranges them in an approximately 10-year interval ranging from 0-1 years, up to 95 plus years, and “all ages.” The lowest percentage of surviving is registered in the 0–1 age group, this means that the mortality rate among infants is higher. After this, the death percentages of the other age groups are almost equal but with minor changes between ages. There are also rising trends in death percentages in the oldest age groups namely 85-94 and 95+ years which we know are normal with age. The equal distribution of deaths across the middle-aged groups indicates that the mortality rate remains consistent within such populace groups. In conclusion, comparing these mortality rates in this chart creates an understanding of vulnerable age categories, including infants and the elderly.
.png)
Fig 3: Death percentage based on cause of death
The bar chart compares the proportion of deaths due to different causes in Australia. On the vertical axis, each case is indicated, and “Percentage of Deaths” is identified on the horizontal axis. The term “All causes” refers to the total percentage and is considerably greater than any individual cause due to acting as a standard by which overall mortality is measured. Specific causes are less popular, while “Acute respiratory disease” can be considered to have a noteworthy rank, which suggests that it is among the key contributors to mortality. Other causes like cerebrovascular disease are also present but their death percentages are comparatively much lower in this regard. Deaths due to accidents such as falls, drowning, and different cancers, including bladder cancer, brain cancer, and breast cancer, among others have low contributions towards the total mortality as depicted by the small bars. This chart is informative to show that while respiratory diseases are the most significant cause of death, numerous health complications lead to deaths. Such data is particularly useful when determining the extent of health risks and framing health policies that target the leading causes of death.
.png)
Fig 4: Australian deaths and percentage death based on gender
This bar chart shows the death count and the percentage of death based on gender in Australia where there is a general category referred to as ‘All.’ The orange bars correspond to the percentage of deaths, which is relatively high and equally depictive for both females and males, meaning that mortality percentages are somewhat similar between the sexes. The blue bars denote the count of deaths, and it is nowhere near the quantity expressed as a percentage, and this indicates the total number of deaths and not the rate of deaths. Relatively, the following chart gives a call on the experiential breakdown of death on the basis of gender for visual understanding of the contribution of gender in overall rates of death which is relatively closer for both genders in Australia.
.png)
Fig 5: Count of Australian deaths based on cause
In the scatter plot, the Australian deaths are grouped based on different factors and the count of each factor is represented. The absolute numbers of deaths for each of these causes are depicted as the size of the dots, and it vary from accidental drowning and falls with nearly 30 dots to liver disease and land transport accidents with fewer dots. The chart shows that data distribution varies among the different health issues, proving that there are some health problems including respiratory diseases and cancers, through which mortality rates are relatively high. This visualization asserts how appropriate health interventions should be meted out to tackle common deaths in Australia.
VI. CONCLUSION
Conclusion
The visualization goal achieved its intended purpose of conveying significant information regarding Australian mortality trends via various types of effective visualizations. The bar chart of age-specific deaths used to display the number of deaths in the various age groups was revealing, as it showed high rates of infant mortality and the elderly population. This forced stakeholders to come up with clear-cut categories that when addressed, public health undergoing demographic groups would have been adequately addressed. Another important visual representation was the distribution of deaths by cause, which showed that acute respiratory diseases have a very high mortality rate. It informed the general public on some of the major health risks and assisted policymakers in allocating health resources to avoid the mentioned diseases
Moreover, the visualization on comparing the mortality between genders clarified mortality patterns, and it was found that males and females were almost equally responsible for death percentages. This finding underpins more focused health promotion programs for tackling gender-specific health concerns. The second display of the number of deaths caused by a particular disease in the form of a scatter plot also emphasized the unequal distribution of risk, as well as bringing to the audience’s attention both common and relatively rare but important threats. Altogether, these visualizations did help in answering the main research questions by highlighting disparities by age and gender in deaths, guiding health policy directions, and raising awareness of major health threats in the Australian population.
Limitation
However, some limitations were seen in the visualization project, although they were not very significant due to the project’s strengths mentioned above. First, the sample of causes of death may not be comprehensive, meaning that new health threats might not be included, and the overall picture may be distorted. Moreover, the result may be biased due to the variation in reporting practices and delays which might affect the precision of the depicted trends especially in less frequently reported causes. Another limitation is one of geographical resolution; the analysis does not disaggregate mortality rates by area within Australia, which might have revealed more about regional variations in health. The visualizations also expect some level of prior knowledge in dwell medical terminology, making it slightly challenging for lay audiences to comprehend. Furthermore, although the visualizations represent the mortality data at least in some sense, they do not reveal any information about factors that might contribute to such trends, including socio-economic status or access to health care, which might help to paint the bigger picture of the health situation in Australia.
VII. REFERENCES
[1] Boord, M. S., Brown, P., Soriano, J., Meola, T., Dumuid, D., Milte, R., and Lim, R. 2024. A digitally enabled, pharmacist service to detect medicine harms in residential aged care (nursing home) (ADEPT): Protocol for a feasibility study. BMJ Open, 14(2) https://doi.org/10.1136/bmjopen-2023-080148
[2] Chaudhary, S., Ray, R. A., and Glass, B. D. 2024. Answering the call for community pharmacists to improve healthcare delivery to trans and gender diverse people: Guide for designing, implementing, and evaluating an online education program in australia. Pharmacy, 12(1), 7. https://doi.org/10.3390/pharmacy12010007
[3] Ellis, L. A., Dammery, G., Gillespie, J., Ansell, J., Wells, L., Smith, C. L., . . . Zurynski, Y. 2024. Public perceptions of the australian health system during COVID?19: Findings from a 2021 survey compared to four previous surveys. Health Expectations, 27(4) https://doi.org/10.1111/hex.14140
[4] Gabriel Arquelau, P. R., Marques Serrano, A. L., Amanda Nunes Lopes Espiñeira Lemos, Edna, D. C., Fábio Lúcio Lopes de Mendonça, Robson de, O. A., . . . García Villalba, L. J. 2024. Understanding data breach from a global perspective: Incident visualization and data protection law review. Data, 9(2), 27. https://doi.org/10.3390/data9020027
[5] Jammal, M., Kolt, G. S., Liu, K. P. Y., and Nariman Dennaoui Emma S. George. 2024. Healthcare professionals’ perceptions on providing support to informal carers within stroke care. PLoS One, 19(10) https://doi.org/10.1371/journal.pone.0311915
[6] Keil, M., Frehse, L., Hagemeister, M., Knieß, M., Lange, O., Kronenberg, T., and Rogowski, W. 2024. Carbon footprint of healthcare systems: A systematic review of evidence and methods. BMJ Open, 14(4) https://doi.org/10.1136/bmjopen-2023-078464
[7] Kuo, N. I.., Perez-Concha, O., Hanly, M., Mnatzaganian, E., Hao, B., Sipio, M. D., . . . Barbieri, S. 2024. Enriching data science and health care education: Application and impact of synthetic data sets through the health gym project. JMIR Medical Education, 10 https://doi.org/10.2196/51388
[8] Omonaiye, O., Ward-Stockham, K., Darzins, P., Kitt, C., Newnham, E., Taylor, N. F., and Considine, J. 2024. Hospital discharge processes: Insights from patients, caregivers, and staff in an australian healthcare setting. PLoS One, 19(9) https://doi.org/10.1371/journal.pone.0308042
[9] Potts, B., Doran, C. M., and Begg, S. 2024. The utility of data collected as part of Australia’s aboriginal and torres strait islander health performance framework. International Journal of Environmental Research and Public Health, 21(3), 340. https://doi.org/10.3390/ijerph21030340
[10] Vinals, L., Radhakrishnan, A., and Sarri, G. 2024. Opportunity and accessibility: An environmental scan of publicly available data repositories to address disparities in healthcare decision-making. International Journal for Equity in Health, 23, 1-11. https://doi.org/10.1186/s12939-024-02187-3
Fill the form to continue reading
Would you like to schedule a callback?
Send us a message and we will get back to you
Highlights
Earn While You Learn With Us
Confidentiality Agreement
Money Back Guarantee
Live Expert Sessions
550+ Ph.D Experts
21 Step Quality Check
100% Quality
24*7 Live Help
On Time Delivery
Plagiarism-Free
81 Isla Avenue Glenroy, Mel, VIC, 3046 AU
