Data visualization

After reading the article, US Electoral Map Interpretations, it is clear that no one visualization is going to represent the whole truth. Each different visualization usual represents a different version of the “truth” and leads people to interpret what they want from it. I am going to be comparing two different types of visualizations, one being a choropleth and the second being an interactive line graph. Both of these are common and mentioned in the assigned article, but have their distinct advantages and disadvantages.

I have decided to focus on the topic of unemployment in the United States, using two different visual representations. The first is a choropleth map of the US 2020 average unemployment rates using colors to represent different percentages. This type of map is one of the most common kinds to use, and is typically easy for the average person to understand. However, this map does not take into consideration the fluctuations each state might have experienced throughout the entire year of 2020. It is a simple good representation of the data, but it does not provide much detail.

Map

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https://www.bls.gov/lau/maps/aastrate.gif

The next visualization I am using is an interactive line graph. This type of visualization is extremely detailed and allows the user to decide exactly which data to focus on. The user can choose the state, the time period, etc. The user can also just look at the years data as a whole to get a bigger picture. This type of visualization can be more useful than the choropleth map mentioned earlier, because it can provide a deeper insight. It could, however, be a bit more difficult for an average person to decipher the information. At first glance, it may be confusing to understand with so many different lines and so much data being presented

2020 State Employment Rates
https://app.powerbi.com/view?r=eyJrIjoiNzg0OWMwNjAtOTFiMC00MDQ1LWJlOWYtNDU2NjM3ZDYxOGY0IiwidCI6IjM4MmZiOGIwLTRkYzMtNDEwNy04MGJkLTM1OTViMjQzMmZhZSIsImMiOjZ9

(link provides access to interactive content)

Whichever visualizations you choose to use, representing data in visual form is extremely important to organize the data and make it easier to understand.

https://www.wired.com/story/is-us-leaning-red-or-blue-election-maps/
https://mapsvg.com/blog/thematic-map
https://www.ncsl.org/research/labor-and-employment/state-unemployment-update.aspx

Cartograms Versus Dot Density Thematic Maps

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Hello:

After reviewing the article based on how selective electoral maps are inferred, I realized that when analyzing these visualizations, you see what you want to see. The set of visual examples that interests me to share with the group are the choropleth maps called cartograms versus the Dasymetric Dot Density Thematic map. Even though both are commonly used as population data visualizations, the choropleth maps are not at best when representing regional patterns of the geographical area or small details or numeric data variations and the dasymetric method does not use the common geographic content to demonstrate population data, like counties. but in the article’s election data-driven visualization that spatially used supplementary data to proportionate the varied geographic areas, the map showed the data as a randomly placed density of dots in areas. Both of these types of geographic mappings have both pros and cons, but how they are used and deduced will result in differences. The examples below show the same data content but the dot density map in my view, inaccuracy relates actual numeric content to the geographic positions on the map. The other plus is that the dot density map shows equally well in black and white.

Map

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A picture containing graphical user interface

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Having the understanding that data visualization is a key tool for conveying data to gain better understandings and personal interpretations are the basic importance for the usage, additionally, the underlying common assumptions are weighted on the interpretations of these visualizations which determines that data has to be, normally distributed, non-existence of independent variables, random samples, and stable.

https://www.nytimes.com/interactive/2021/world/covid-cases.html
https://mapsvg.com/blog/choropleth-map
https://towardsdatascience.com/data-visualization-in-data-science-5681cbdde5bf

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