Joining Attribute Tables to Create Maps of Vital Statistics

Data are often available in tabular formats, such as county measures of mortality rates from NJSHAD. In this example I explore the differences in county-level deaths from heart diseases in New Jersey, from 2000-2015. The highest rate of deaths from heart disease was found in Salem County, with an age-adjusted death rate of 222.7 per 100,000. The lowest rates were found in Somerset county, with an age adjusted-death rate of 136.2 per 100,000. Heart disease is the leading cause of death in the U.S., and we dedicate February to promoting awareness of heart diseases and the health behaviors that protect against them: healthy diets, regular exercise, not smoking, and behaviors that help reduce stress.

 

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The webmap above portrays Jenks Natural Breaks for classifying mortality data. But, the categorization is up to the cartographer. The maps below illustrate four different classification schemes, all using the same data: Dot Density, Jenks Natural Breaks, Quantiles and Standard Deviation. The map with shades of red is often easiest for map readers to interpret correctly. Studies of health professionals have revealed the standard deviation classifications are valuable, since the mean is compared with outliers; however, as the legend below describes ‘standard deviations,’ these values are often complex for non-professional audiences to interpret.

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