How can a data visualization present visually deceptive information to blur the line between fact and falsehood?
Approaching this project, I had not considered how the design of data visualizations could impact a wide range of audiences. I quickly learned that data is very often used to not only reveal specific correlations but also to hide important facts. I approached this data visualization project with an unclear vision for utilizing and visually structuring data. However, I began by looking at a wide range of data sets with the goal of finding two that only focused on data collected in North Carolina.
Deciding on what data to work with and how to weave two unrelated sets together to devise a cohesive story was more challenging than I had expected. While this project was meant to expose the lies within collected data, I found myself obsessed with correctly depicting the lie with exact numbers, dates, etc… As I began to visualize the parsed data in programs such as Raw Graphs and Tableau Public, I was able to gain a better understanding of how to use the data to create visual lies.
Ultimately, I chose to focus on data sets that were very different from one another. The first set consisted of records detailing NC Certified Minority- and Female-Owned Businesses from 2010 to 2017. The second set outlined Food Inspection Violations in Wake County from 2012 to 2017. Parsing through both data sets, I focused on the city of Raleigh, the years 2013, 2014, and 2016 along with the categories; Male and Female, Minority types, and total records of food inspection violations.
Considering the categories I was focusing on, I had trouble developing a lie that was deceptive enough to call for attention. Being aware of how scrutinized women have become in our society at present, I settled on using the phrase “Health Codes Discriminate against Women.” Using the total records of food inspection violations as the distinguishing factor between Male, Female and various Minority types, it became easier to develop visually deceptive forms. Representing this collective data by utilizing various line weights, colors, and opacities helped to blur the line between fact and falsehood.
How can a data visualization present visually deceptive information to blur the line between fact and falsehood?
Approaching this project, I had not considered how the design of data visualizations could impact a wide range of audiences. I quickly learned that data is very often used to not only reveal specific correlations but also to hide important facts. I approached this data visualization project with an unclear vision for utilizing and visually structuring data. However, I began by looking at a wide range of data sets with the goal of finding two that only focused on data collected in North Carolina.
Deciding on what data to work with and how to weave two unrelated sets together to devise a cohesive story was more challenging than I had expected. While this project was meant to expose the lies within collected data, I found myself obsessed with correctly depicting the lie with exact numbers, dates, etc… As I began to visualize the parsed data in programs such as Raw Graphs and Tableau Public, I was able to gain a better understanding of how to use the data to create visual lies.
Ultimately, I chose to focus on data sets that were very different from one another. The first set consisted of records detailing NC Certified Minority- and Female-Owned Businesses from 2010 to 2017. The second set outlined Food Inspection Violations in Wake County from 2012 to 2017. Parsing through both data sets, I focused on the city of Raleigh, the years 2013, 2014, and 2016 along with the categories; Male and Female, Minority types, and total records of food inspection violations.
Considering the categories I was focusing on, I had trouble developing a lie that was deceptive enough to call for attention. Being aware of how scrutinized women have become in our society at present, I settled on using the phrase “Health Codes Discriminate against Women.” Using the total records of food inspection violations as the distinguishing factor between Male, Female and various Minority types, it became easier to develop visually deceptive forms. Representing this collective data by utilizing various line weights, colors, and opacities helped to blur the line between fact and falsehood.
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