Module 6: Proportional Symbol and Bi-variate Choropleth

This week we learned about proportional symbol mapping and bi-variate choropleth (above).

Proportional symbol mapping is a quantitative map that varies the representation of a  feature, and the size, shape, and color vary with the particular variable. This type of map is very appropriate to map data counts.
Our first assignment was to utilize a proportional symbol map to represent the population size of cities in India.  First we were asked to determine the appropriate variables for a custom conic projection for the area.  I utilized a central meridian of 80E and parallels of 30.8N and 12.0N. The symbol properties for the proportional symbol were set with consideration of the background colors and visibility.  Finally a custom legend was created to show the range of symbols utilizing Flannery Appearance Compensation to accentuate the differences in the larger symbols.

Next we moved on to divergent proportional symbology to display jobs gained or lost by state in a time frame of December 2007 and July 2015.  This map creation had many challenges.  First the use of negative numbers does not work for this process.  The original shape file was divided into two shape files utilizing Select by Attribute SQL for <0 and >0.  In the file with the data <0, a new field was created, the field calculator was utilized to obtain the absolute value of those numbers (essentially making them positive).  Due to technical difficulties with loading this data (load times were extreme for even the smallest changes), and the two layers (gains and losses) being set to the same minimum size, line width, and no maximum created different size reference I tried to make the map as completely basic as possible. I am not satisfied with this map, but in the time frame and technical issues this is it.

Update:  Due to the technical issues with ArcPro with proportional symbol for raw count data that I encountered for the above map, I chose to change the symbolization for the raw count data to a dot density map.  More confident in the display of the data in this format.

Finally, we were asked to create a bi-variate choropleth map displaying two variables (% obesity and % physical inactivity)  Bi-variate choropleth maps display two variables (sometimes three = Tri- or Multi-variate) on the same map.  Variables for choropleth maps should be normalized.  These were in the form of %.  Each variable was classed into 3 class quantile classification in order to obtain the break values for the classes.  From the attribute table use of SQL to obtain the records for these classifications were then classed in new fields.  The completion of a column for each variable were then combined using concatenate to a third/final field.  The map was symbolized

with unique values from this final field.  Adjustments were performed to those unique colors by color ramp, complementary color wheel and filling in changes based on Hue (top left and bottom right are complementary - opposite on the color wheel, top right lies between on color wheel) , Saturation (very low or 0 in the bottom left and gradually increase to top right) and Value (lowest in the top right and lowest in the bottom left).  Here is a close up of the legend.  Map appears at top of this post.

Module 5: Analytics

This week we downloaded 2018 County Health Rankings National Data from http://www.countyhealthrankings.org/explore-health-rankings/rankings-data-documentation 
After downloading the data we were to review and choose two variables that could be related and create an info-graphic from the variables. "The County Health Rankings are based on counties and county equivalents.  The data is a variety of national and state sources that are standardized and combined using scientifically-informed weights."(County Health Rankings & Roadmaps, 2019).

The objective of this lab assignment is to practice the use of a number of different data visualization techniques, including bar charts and scatter plots, as well as the design of communication materials that combine maps and other graphics. We were to select appropriate chart types for our chosen data.  We then created charts for data visualization, including scatter plots, bar charts, and a pie chart.  Finally, we were to combine maps, charts, and text into a single data visualization product (above). 

I chose "% uninsured" and "% frequent mental distress".  I was unable to determine how "% frequent mental distress" was specifically determined, but feel the relationship to Mental Illness would be close.  My hypothesis was that the states with higher mental distress would also have higher uninsured rates.  The correlation being that those with mental illness not able to obtain treatment due to no insurance would lead to higher mental distress incidents.  The scatter plot of the two variables shows some correlation but not as strong as I had suspected.  The bar charts show the high and low states for the variable as well as the national, Florida and Alabama (my specific area).  The area chart illustrates how few of the population are not insured.  I also included a pie chart as well as a simple graphic to show 1 out of 25.

Reference:
County Health Rankings & Roadmaps. (2019). Retrieved from: http://www.countyhealthrankings.org/explore-health-rankings/our-methods [Accessed 8 Feb. 2019].

Module 4: Color and Choropleth

This week's project was to pick one state from a list provided and extract the state information and map the population change from 2010 to 2014.  I picked Colorado.  Colorado has 2 UTM zones and 3 StatePlane, so these were eliminated as projection choices.  I did not locate a projection specific for the State of Colorado.  I chose to use NAD 1983 (2011) Contiguous USA Albers.  A custom Albers projection adjusted with central meridian and standard parallel would have been better, but I couldn’t figure out how to change them.  The formula I utilized to normalize data to percent of change in population is: (Population 2014-Population 2010)/Population 2010*100.  I used an 8 class manually assigned classification.  I after looking at the natural breaks for a 5 class I decided to take the natural breaks and round them to more user friendly intervals keeping significant breaks for both growth and loss of population.  I also added a critical class for the 0 marker.  I utilized a divergent color scheme to help symbolize those that population increased/decreased.  Darkening greens to indicate increased growth and progressively darker grays for loss of population.