Time Required: 30-60 minutes
Standards: NGSS HS-LS2-2, CCSS.ELA-Literacy.RST.9-10.7, CCSS.ELA-Literacy.9-10.3
CDC Lyme disease information
Excel version: Lyme cases by state, 2003-2012.xlsx
CSV version: Lyme cases by state, 2003.csv
Excel version: WNV cases reported by state, 1999-2013.x
CDC EEEV information
Excel version: EEEV cases from 2000–2010.xlsx
CDC LACV information
Vector: the Ochlerotatus triseriatus mosquito
Pick another disease of interest
- Find data: Search the CDC site for a disease you’ve heard of, then look for a link to “statistics” for data about that disease.
Download or copy those data into an Excel file, making sure that the top row contains only dates, and the left column includes either U.S. states or U.S. counties. So long as your data resemble the snapshot below, you can save your Excel file and start mapping.
- If you need to convert the file into a .csv file, start by removing all of the spaces from your state and county names. Next click “Save As,” and choose “Comma-Separated values (.csv)” from the drop-down menu; then click “ok.”
Open Heat Map –www.openheatmap.com
- Go to www.openheatmap.com and click “Create your map.”
- Select “Excel or CSV file,” then click “upload.”
Select the dataset you chose, and click upload.*
*Note: You can use this with any data that are organized by state or county information in the left hand column.
- Click “View your map.”
- Use the play button to visualize your data. You can also adjust the color settings, title, author, dates, and key to better represent your data. You will also probably have to adjust the data range to accurately reflect your data.
Click “Save & view” in order to share or embed your map.
*This map requires that you sign up for a free trial account.
- Upload the .csv file version of the data you would like to use, then click “complete.” You can ignore any error warnings—the data will still map correctly. Then click, "Return to Map."
- You can adjust the map to display different marker styles and colors based on your dataset by selecting “style & color” from the box in the upper left corner, and then choosing “style” and “color by value” from the box that pops up.
You can choose which year’s data you would like to map by selecting the year from the drop-down menu in the “color by value” menu.
- Print out the U.S. Map and Key.
- Choose a single year (column) of data from one of the datasets above. This will be the data that you map.
- Choose the key whose highest value is closest to the highest value of any state or county in your data set. For example, if the highest number of cases of Lyme disease in any state for the year you are mapping is 300, choose the key whose range goes to 350, not the key that goes to 2000.
- Color each box in your key a different color. Label your key with the disease you are mapping and the year the data was collected.
Using your key as a guide, color in each state based on the number of disease cases it had according to your data set. For example, if Texas had 25 cases of Lyme disease in 2003, color in Texas using the color from the key whose range includes that number. You can see an example of how to color in states in the image below:
Based on data of confirmed La Crosse encephalitis cases from 2000:
Minnesota 8 Colorado 1 North Carolina 6 Connecticut 1 Ohio 18 Georgia 2 Tennessee 19 Illinois 3 West Virginia 40 Indiana 2 Wisconsin 6 Iowa 4 all other states 0 Kentucky 2
- Based on your map, are there regions of the United States that are disease hotspots? Are there areas of the United States that seem to always have few or no cases each year?
- Compare the number of cases in states between years. Does the disease seem to be confined to certain parts of the U.S. even between years?
- How do the number of cases seem to be changing from year to year? Is there a pattern that suggests the disease is becoming more common, or that it is becoming less common over time?
How might adding data from additional years change your conclusion from question 2 or 3 above?
- Are the regions with fewer cases similar in some way? Think about population size, altitude, proximity to the ocean, and latitude. How could these factors affect the spread of a disease transmitted by vectors such as mosquitoes or ticks?
Compare your disease heat map to the map below of climate zones in the continental U.S. Are there any similarities? How could regional climate affect the distribution of a vectored disease? (Click on the map for an enlarged version.)