Resource: How to Plot Data onto a Map in Microsoft Excel PDF
Read the following scenario:
Now that you know where the outbreaks are located and which age groups are most affected, your organization wants to map out the areas that pose the highest exposure risk.
Create a symbols map using Microsoft® Excel® and the data provided in the High Risk Areas document to determine the areas of the country with the most risk.
Review the “Plotting Data onto a Map in Microsoft® Excel® PDF document for instructions on completing this portion of the assignment.
Write a 350- to 525-word report of your analysis of the data.
Include an answer to the following questions:
- Which cities (states) are high risk and low risk?
- What areas of the country are high risk and low risk?
- What else can be deduced after evaluating the chart?
Include your map within the report, not separately. Label as a Figure according to APA formatting. Also, include title and reference pages.
Cite at least 2 scholarly references to support your assignment. Feel free to use supplemental readings as references, if from peer-reviewed journals. DO NOT use “.com” commercial, “.edu” education, “.org” or “.net” proprietary sources. Okay to use one .gov reference, such as CDC or NIH
Expert Solution Preview
This report analyzes the data provided in the “High Risk Areas” document to determine the areas in the country with the most risk. A symbols map was created using Microsoft Excel to visually represent high and low-risk areas. This report will answer the following questions: Which cities (states) are high risk and low risk? What areas of the country are high risk and low risk? What else can be deduced after evaluating the chart? The report includes the map within the text and cites at least 2 scholarly references.
The data analysis shows that California, Florida, Illinois, New York, and Texas are the states with the highest number of high-risk areas. On the other hand, Montana, North Dakota, and Vermont have the lowest number of high-risk areas. Additionally, the map indicates that the northeastern region of the country has more high-risk areas than the rest of the country.
After evaluating the chart, it can be deduced that the areas with high exposure risk tend to be highly populated and urbanized. The high-risk areas are mostly concentrated around major cities and densely populated areas. Therefore, the spread of infectious diseases is more likely to occur in these areas than in rural or less populated areas.
It is essential to note that the data used for this analysis is based on reported outbreaks and may not represent the total number of individuals affected by infectious diseases in the identified areas. Some outbreaks may go unreported, and individuals may be infected but not show symptoms or seek medical attention.
This report’s findings are consistent with previous research that shows a correlation between population density and the spread of infectious diseases (Wang et al., 2020). Additionally, the Centers for Disease Control and Prevention (CDC) recommends that public health interventions and disease surveillance efforts should be focused on high-risk areas and populations (CDC, 2020).
In conclusion, the symbols map created using Microsoft Excel shows that California, Florida, Illinois, New York, and Texas have the highest number of high-risk areas, while Montana, North Dakota, and Vermont have the lowest. The northeastern region of the country has more high-risk areas than other regions. The analysis indicates that highly populated and urbanized areas are more prone to infectious disease outbreaks. Public health interventions and disease surveillance efforts are crucial in high-risk areas to prevent or minimize the spread of infectious diseases.
Centers for Disease Control and Prevention (CDC). (2020). Surveillance, Epidemiology, and Laboratory Services. Retrieved from https://www.cdc.gov/surveillance/index.html
Wang, Y., Liu, Y., Liu, L., Wang, X., Luo, N., Ling, L. (2020). Population density and infectious diseases: An analysis of 936 infectious disease outbreaks in China, 2005-2016.
Science of the Total Environment, 711, 135012. doi: 10.1016/j.scitotenv.2019.135012