Skip to main content

Infographic's

 This week was a fun and challenging week as we learned about and created infographics. It was fun to create the infographics themselves, but challenging to figure out the best methods and practices in analyzing raw data. 

We used 2018 County Health Rankings National Data from countyhealthrankings.org. I chose to move forward with the two values: Unhealthy Mental Days and Premature Dealth. I  choose these two variables because those that struggle with mental health die before their time due to depression, anxiety, and/or a combination of similar issues. Both variables are normalized by relating their value to all counties within each state in the USA. For example, the poor mental health days is normalized as the average number of reported mentally unhealthy days per month per ctizen. The normalized premature rate is the “age-adjusted years of potential life lost rate per 100,000.” 

Below, I created a scatterplot of the normalized data. I choose to keep the scatterplot in a pretty traditional format; blue with circle points. When trying out the different styles of multicolor, bicolor points, and different shapes, the scatterplot drew too much attention away from the choropleth maps.

Image 1: Scatterplot of Premature Death vs Mentally Unhealthy Days

I summarized the data by state, to determine the top 3 and bottom 3 states with premature death. When choosing the design choices for the bar graphs, I used grey. I wanted all the surrounding information outside of the choropleth maps to be subtle and not overtake the layout. For this reason, I chose a subtle gray. I also went with a clean boxy bar with its value inside to get rid of the excess axis grid information.



Keeping in touch with subtle light greys, that match with underlying hues of the choropleth USA maps, I created a statement based off of the scatterplot that summarized the data. To further provide evidence of this statement, I used a statistic from the National Institute of Mental Health, and represented it with a pie chart.


I found it tricky to create this infographic’s layout and what color ramp to use because I did not want to use bright/disrespectful hues on such a series topic. I concluded to use darker, more monotone colors, such as blue, purple, and grey. When deciding the overall layout of the infographic, I knew that I wanted the USA choropleth maps as they contained the most data. I played these maps in the center and middle of the layout with the “brighter” hues I had chosen to place them at the top of the color hierarchy. I placed the supplemental bar graphs, pie chart, and statement in grey under the maps. Lastly, I used blue to represent the scatter plot as this was important information. I divided the top half of the layout area with the scatterplot and title. Because the scatterplot was blue, I used purple for the title to place the two colors already used for the USA maps.


 


 





Comments

Popular posts from this blog

Bivariate Choropleth and Proportional Symbols

In the first part of this lab, we used proportional symbols to represent positive and negative values in job increases/decreases in the USA.  Because there were negative values in this data set, I created a new map to "fix" the data. In this new map, I created a new field and copied the negative job loss data. I then used the Calculate field data and multiplied it by one to make it positive. Lastly, I overlaid both maps on the data and was able to accurately represent the increase and decrease of jobs in the USA by state.   In the second part of this lab, we delved into how to prepare data for a bivariate choropleth map, choose colors for the legend, and create a good layout.  I created three separate fields to analyze the data: Class Obese, Class Inactivity, and Class Final. I used the symbology tool to create 3 Quantile for the Obese and Inactivity classes and used each quantile to set the three classifications in the fields I created using the Select by Attributes...

Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...