This exercise uses SVG graphics created using the D3-JavaScript library to visualize census data, specifically 2014 data from the U.S. Census Bureau and the Behavioral Risk Factor Surveillance System, based on 2014 American Community Survey (ACS) 1-year estimates. The dataset includes information on rates of income, obesity, poverty, etc. by state. MOE stands for "margin of error.
Each circle in the scatter plot that represents each state with circle elements. The ticks on the axes allow us to infer approximate values for each circle, while tooltips display the data for the axis that the user selects.
The graph presents options for 3 different factors to compare against obesity. Clicking on the desired access will re-render the circles in the graph to refect the chosen comparison. A brief analysis of each of the 3 comparisons is provided below, as well as on the deployed page.
Income levels seem pretty well correlated with obesity levels in each state. Overall, states with a lower average income level among its population presented higher obesity levels. One could speculate that this could indicated that lower-cost food items are contribute to nutritional deficiency, resulting in higher probability of obesity rates in populations with lower incomes. It could also support the idea of “food desserts”, where lower-income areas may have lower availability of high-quality foods or more food options, thus contributing to nutritional deficiencies like obesity.
Poverty rate also appears to be pretty well correlated with obesity levels in each state. Generally, states with higher poverty rates also presented higher obeisty levels. As noted above, this may be an indication of lower nutritional content in the foods that are more affordable or available (or both) to families who live below the poverty line.
There is a less strong correlation between obesity and healthcare coverage. However, there is still a generally positive correlation between these two factors, indicating that the higher the percentage of the population that lacks healthcare coverage (used as a proxy for lack of access to health services), the higher the obesity rate.
- David Gotz's example to see how to implement tooltips with d3-tip.
- the
d3-tip.jsplugin developed by Justin Palmer