Choropleth Map:
For Indiana and Ohio, there wasn't a major change in the population growth. I kept the natural breaks classification method, and classified the data into four classes. The advantage of using the natural breaks method is that the software automatically identifies real classes and is exceptionally useful for choropleth maps due to true representations of the data.
Graduated Symbols Map:
The symbol I chose for this map were circles. The symbol size ranged from 4 to 18. The benefit of the graduated symbols is that they are based off classed data, so none of the circles exceed the county lines. Another benefit is being able to determine how many classes will be represented, so the circles are easily identifiable per class.
Proportional Symbols Map:
The proportional symbols I chose were circles. There are four symbols in the legend. The only issue with the proportional map is that despite the small county size, some of the symbols are so large that they exceed the boundaries of the counties.
Dot Density Map:
For the dot density map, the dots are at a size 2 due to the differences between the population change for the counties; meaning that there are counties with such high percentages of population change that if I chose the dots to be any larger the map would just look clustered and the counties wouldn't be easily identified. One dot represents a certain amount of people, and the value of the dots change over time.
Color Scheme:
For the choropleth map, I chose a yellow-red color scheme; with red indicating higher percentages for the population change and the yellow representing negative/lower percentages for population change. Red is an extreme color and symbolizes empowerment, which is why I chose that color for the higher percentages of change because it symbolizes more extreme changes for the population.
For the graduated symbol and proportional symbol maps I chose light yellow and light green, with the symbols being black. I chose that background so the symbols stand out against the background. For the dot density map, I chose a white background with black borders around each county and the color ramp for the dots weren't great selections, but I chose dark green dots to stand out against the background.
Overall, I tried manually classifying the data but was having trouble with the numbers due to the variety of negative and positive percentages for each year. I ended up with the natural breaks because the data is truly represented with that classification method (as explained further above).