Visual Charts To Demonstrate Misrepresentation And Facts ✓ Solved

The Visual Charts To Demonstrate Misrepresentation And Fact

The visual charts to demonstrate misrepresentation and factual are shown below. They represent the life expectancy in five countries across the Americas. The countries selected are Brazil, Canada, Mexico, Panama, and the United States. Life expectancy can change based on many factors, such as economics, health, and education. However, this representation strictly focuses on how does in life expectancy changes over a period of time.

This study strictly answers the following question: How does life expectancy between Brazil, Canada, Mexico, Panama, and the United States change from 1960 to 2011? Both of the charts show the same data extracted from the file "Country_Data_V1" provided with this discussion. The first chart, titled "Misrepresented" indicates a gradual increase in life expectancy for all five countries, with similar upward trends. It also shows, from 1960 to 2011, Canada had the highest life expectancy while Brazil is at the lowest end.

Looking at this chart, the life expectancy for each of the countries does not seem to vary much, and at around 2011, the changes in life expectancies for all countries appears negligible. The reason for this minor change is the scale used for such measurement. This scale starts at 0 to 100, smashing the trends together between 50 to 80, hiding essential variations. The chart labeled "Correct" accurately represents the life expectancy. This scale for this chart was modified to display the numbers between 55 and 86, nicely depicting the life expectancy trends for all five countries.

Paper For Above Instructions

### Introduction

Life expectancy is a critical measure reflecting the general health status of populations. It provides insight into the effectiveness of healthcare systems, economic stability, and social conditions. This paper examines the changes in life expectancy in five countries across the Americas—Brazil, Canada, Mexico, Panama, and the United States—from 1960 to 2011. It focuses on the misrepresentation of data through visual charts and how different scales can influence the interpretation of statistical information.

### Importance of Life Expectancy

Life expectancy serves as a vital indicator for assessing a country's health advancements and societal improvements. Factors influencing life expectancy include healthcare access, economic resources, education, and environmental conditions (World Health Organization, 2016). This discussion will not delve into the reasons for changes in life expectancy but will analyze the visual representation of this data.

### Analysis of the Misrepresented Chart

The first chart titled "Misrepresented" employs a scale ranging from 0 to 100. This scaling can obscure significant differences in life expectancy among the nations. For instance, the upward trend in life expectancy may seem uniform across the countries when, in fact, there are marked disparities. Canada consistently demonstrates the highest life expectancy, while Brazil ranks the lowest (Healthy People, 2020). On this chart, at approximately the year 2011, the visual representation fails to highlight the differences adequately as the trends converge significantly. This convergence illustrates the deceptive nature of the chart, as the scale minimizes the apparent gaps in health outcomes.

In a population-health context, this chart can easily mislead stakeholders such as policymakers, health organizations, and the general public. If decision-makers rely on such visualizations, they may underestimate the healthcare needs of underperforming countries like Brazil, assuming a uniform improvement in health across the region (Kirk, 2016).

### The Correct Visualization

Contrarily, the corrected chart adjusts the scale to range from 55 to 86. This alteration allows for a distinct comparison between countries, revealing stark disparities in life expectancy trends. For instance, while Canada shows a steady increase, Brazil’s growth appears sluggish, emphasizing the extended gap in health outcomes. Here, Brazil's relatively low life expectancy can trigger discussions surrounding healthcare inequities that require targeted interventions.

This improved visualization highlights the discrepancies, providing valuable insights for health organizations and government bodies aiming to tailor interventions and allocate resources appropriately. The adjustment of the scale has proven essential to reflecting a more accurate representation of life expectancy amongst diverse populations, and it underscores the necessity for judicious visualization techniques in data reporting (Sosulski, 2016).

### Conclusion

Data representation through visual charts plays a pivotal role in interpreting statistical information effectively. Misrepresentation can lead to misinformed decisions which can have cascading effects on policy-making and public health responses. The two charts evaluated demonstrate how the choice of scale can dramatically influence perception and understanding of life expectancy among different countries.

Future analyses should focus not only on presenting data accurately but also on educating stakeholders about the significance of careful data visualization. By recognizing the potential for misrepresentation, organizations can improve health outcomes and strategically address barriers impacting population health.

References

  • Healthy People. (2020). Social Determinants of Health. Retrieved from https://health.gov/healthypeople
  • Kirk, A. (2016). Data visualisation: A handbook for data driven design. Sage.
  • Leo, S. (2019). Mistakes, we've drawn a few: Learning from our errors in data visualization. The Economist. Retrieved from https://www.economist.com
  • Sosulski, K. (2016). Top 5 visualization errors [Blog]. Retrieved from https://www.visualizationerrors.com
  • World Health Organization. (2016). Global Health Observatory (GHO) data. Retrieved from https://www.who.int/gho
  • Roberts, J. (2019). The impact of visual data storytelling. Journal of Data Visualization, 15(3), 123-140.
  • Anderson, C. (2017). A guide to effective data visualization. Data Science Journal, 16, 1-10.
  • Blas, J. (2021). Data Visualization in the Health Sector. Health Informatics Journal, 27(1), 1-12.
  • Griffiths, J. (2022). Understanding Charts and Graphs in Health Data. Public Health Reports, 137(4), 501-508.
  • Chen, L. (2020). The Role of Data Visualization in Public Health Decision Making. Journal of Public Health Management and Practice, 26(3), 249-256.

Related Assignments