Process Book: Data Visualization by maggiechuang

DATA VISUALIZATION Maggie Chuang Word & Image II Fall 2017

1. RESEARCH What did you turn to for reference, inspiration, and understanding for your project? How did it influenceâ&#x20AC;&#x201D; directly or indirectlyâ&#x20AC;&#x201D;what you made?

2. DISCOVERY What was your process for generating possible ideas for the project? How wide and deep did you explore before coming to a final idea? Did your discovery process generate outcomes that were successful?

3. REFINEMENT How did you refine your work? How did you make decisions as you refined your idea? What criteria did you use for evaluation? Did you find your final refined idea to be the final iteration of your idea?

4. CRITICISM What criticism did you receive from your peers and faculty about your project? Did you agree or disagree with it? What did you learn from criticism?

RESEARCH I began this project by solely looking at the given data sets, and tried to find inspiration in the information alone. Early on, I was struck by the National Pew data, particularly with just how many people believed that black and white American demographics were treated equally. That initial reaction was exaggerated once I saw just how distinct the gaps were between demographics in St. Louis, especially in the city. I was confused and somewhat amazed that a large majority of America could believe there was equality between these demographics, even with the egregious differences that were occurring in our major cities. This led me to focus my project on criticizing the equality viewpoint as a whole, rather than dialing into a specific aspect of it. My main concept was to point out the nationâ&#x20AC;&#x2122;s general agreement in equal treatment, and juxtapose it with very obvious UNequal treatment in St. Louis City. For visual inspiration, I was drawn to several different projects and websites that focused on unique forms of data visualization. Dear Data was a major influence, and opened my mind early to just how abstract a graph could be; to the writers of the book, marks and symbols werenâ&#x20AC;&#x2122;t explicitly related to content, which was a point I took to heart. Other sources like Information is Beautiful by David McCandless and the Feltron Annual Report were 5

inspiring in terms of how the handled final compositions of visualization. Their graphs had a lot of visual interest on a macro level, and encouraged me as a viewer to look closer and investigate the micro-information. I also searched in general for hand-drawn and image-based approaches to data visualization, and tried to avoid looking at straight bar graphs and pie charts. Although I wasnâ&#x20AC;&#x2122;t sure what I wanted my direction to be, I knew I wanted to be inspired by more radical styles of visualization so that I could push the creativity of my own. One of my personal project goals was to avoid typical or default chart types, and create something that was aesthetically new while also effective in its communication.

Initial research findings. The enlarged reference above was particularly inspired me in the direction of my visualization; the image is beautiful and interesting, but also immediately clear that its comparing two sets of data.

Continued examples of visual research

The first data set I focused on was the general race distributions in the different counties. What I found significant in this data was that St. Louis City essentially had equal populations of black and white ciztizens.

The place where I found the easiest point to show disparity was in the unemployment rates of St. Louis City. Above I have the information of the Black Unemployment rates in the city, which were equal to 25.7%â&#x20AC;&#x201D;almost 10% higher than the national average. White unemployment was at approximately 5%, siginicantly lower than the national average.

DISCOVERY While I was generating new ideas, I stayed away from the computer and tried to experiment entirely through handdrawn sketches. To start, I identified the general structure of what my data needed to look like, and then generated visual ideas based on that concept. Essentially, I was looking to compare two things: unemployment rates between demographics, and the demographicâ&#x20AC;&#x2122;s population size. By the end of the process, I had found two styles of representation that were potentials to represent the data overall. One possibility was two stacks of bars (representing the demographics), with pulled out bars to show the amount of unemployment in each. While this representation was a quick and effective read, it was very similar to a bar graphs and other common styles. The direction I decided to pursue for the project was a method of representing the data in spokes rotating around a circular, which looked abstractedly like a star/burst. Once I chose a version, I tried several other drawn iterations of it to get the exact proportions and general shape that I wanted. When I felt satisfied, I finally moved to digitizing my visualization.

White Population of STL City County (45%)

We Think It’s Equal

On the left is a representation of the populations of white and black citizens in St. Louis (STL) City county; with only a 3% difference in population percentage, there is almost perfectly equal representation in St. Louis City. White unemployment in STL City Country = 6%

However, the orange markers represent the portion of each demographic that is unemployed. While the populations are relatively the same size, unemployment rates are different by 20%.

Black unemployment in STL City Country = 26%

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment. White unemployment in STL City Country = 6%

Black Population of STL City County (48%)

In a national Pew study in 2012, it was found that almost two-thirds of of respondents thought that America’s black and white populations were treated equally. In particular, 70% overall believed that there was equal treatment in the workplace. This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis. On the left is a representation of the populations of white and black citizens in St. Louis (STL) City county; notably, each demographic makes up almost equal parts of the total population. However, the orange markers represent the portion of each demographic that is unemployed. While the populations only differ in number by 3%, unemployment rates are different by 20%. v Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis. With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Black unemployment in STL City Country = 26%

“Equal” Treatment

White Population of STL City County (45%)

We Think It’s Equal

White unemployment in STL City Country = 6%

However, the orange markers represent the portion of each demographic that is unemployed. While the populations are relatively the same size, unemployment rates are different by 20%.

Black unemployment in STL City Country = 26%

Black Population of STL City County (48%)

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis. On the left is a representation of the populations of white and black citizens in St. Louis (STL) City county; notably, each demographic makes up almost equal parts of the total population. However, the orange markers represent the portion of each demographic that is unemployed. While the populations only differ in number by 3%, unemployment rates are different by 20%. v Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally? Black unemployment in STL City Country = 26%

White Population of STL City County (45%)

We Think It’s Equal

We Think It’s Equal In a national Pew study in 2012, it was found that almost two-thirds of of respondents thought that America’s black and white populations were treated equally. In particular, 70% overall believed that there was equal treatment in the workplace. This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

The sides of this poster represent the relative sizes of the white and black populations in St. Louis City county. The red represents their relative unemployment rates.

However, the orange markers represent the portion of each demographic that is unemployed. While the populations only differ in number by 3%, unemployment rates are different by 20%.

Black Population of STL City County (48%)

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

26% of black citizens in STL City County are unemployed 6% of white citizens in STL City County are unemployed Black unemployment in STL City Country = 26%

Above: Versions of the stacked-bars concept. Left: Sketches testing inital ideas for visualization

This style of visualization was what I chose for my final product.

Treated “Equally”

Is this equal? White unemployment in STL City = 6%

White unemployment in STL City = 6%

Black unemployment in STL City = 6%

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment. With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Treated “Equally”

Population of STL City that is white = 45%

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

White unemployment in STL City = 6%

Black unemployment in STL City = 26%

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Population of STL City that is black = 48%

With such stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

We Think It’s Equal

In a national Pew study in 2012, it was found that almost twothirds of of respondents thought that America’s black and white populations were treated equally. In particular, 70% believed that there was equal treatment in the workplace. This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis. White unemployment in STL City = 6%

STL City County Employment

Black unemployment in STL City = 6%

The graph above represents the relative populations of white and black citizens in the St. Louis County. On the right is the white population (45% of the county’s total population), and on the left is the black/African-American population (47%). The red spokes represent the portion of each demographic that is unemployed within the county, with white unemployment at 6% and black unemployment at 26%. In a county where the demographic populations are almost equal in number, there is a 20% difference in their employment rates. How can we think our nation is treating blacks and white citizens equally, when disparities like this exist in places of equal representation?

Iterations on my chosen direction. At this point I was playing around with several factors, including spatial relationships of the data sets, color, and text placement.

REFINEMENT The focus of my refinement process to finalize the composition of my poster. My goal was to create a clear line of hierarchy, so that the information was called out in the order that best explained my argument. I also needed to resolve all the micro-typographic elements, because they would be far more noticeable on my simple/sparse poster. These included margins, leading, scale shifts in type, color, and font style. One critical change I made was lengthening the format of my poster. I originally started with a perfect square, because I felt it would complement the circular visualization most effectively. However, once I added in all of my text and headers, I found that it was too much of a limitation to try and fit everything into a square; with the added length on the bottom, I was able to give the elements a comfortable amount of space to live in. Color was also a big component of my refinements. I passed my composition through several color studies, and eventually concluded by poster should be mostly black and white; adding too much color seemed to give my poster to a brighter/less powerful tone than just monochrome. One final tweak I made was to use a pop of red to emphasize the main data points, as well as key text elements that referenced my overall argument. 19

Treated Equally

Treated Equally This graph represents population demographics in St. Louis (STL) County. On the right is the white population (45% of the county’s total population), and on the left is the black population (47%). The red spokes represent the portion of each demographic that is unemployed within the county, with white unemployment at 6% and black unemployment at 26%. Population of STL City that is white = 45%

Even though white and black populations are most equal in STL City compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

White population density in STL City = 45%

White unemployment in STL City = 6%

White unemployment in STL City = 6% Black unemployment in STL City = 26%

Black population density in STL City = 48%

Black unemployment in STL City = 26%

Population of STL City that is black = 48%

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

With such stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

TREATED

Treated Equally Po p

of ion ulat

S TL

wh hat is City t

ite = 45%

Population of STL C

it y t h at is blac k

= 48

Population of STL City that is white = 45%

White unemployment in STL City = 6%

Black unemployment in STL City = 6%

White unemployment in STL City = 6%

Black unemployment in STL City = 26%

Population of STL City that is black = 48%

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis. With this unemployment inequality in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

EQUALLY

Treated Equally

Black unemployment in STL City = 6%

b la c k = 48 %

White unemployment in STL City = 6%

Populati on of S

TL C ity t hat is

P op ulati on

wh ite

of S T

LC ity th

at is

wh ite =

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

ity t h

at is

White unemployment in STL City = 6%

lat ion o

f ST

ity t hat i s

black

= 48%

Po p

u la

tion

of S

TL C

Black unemployment in STL City = 26%

This graph represents population demographics in STL County. On the right is the white population (45% of the county’s total population), and on the left is the black population (47%). The white spokes represent the portion of each demographic that is unemployed within the county, with white unemployment at 6% and black unemployment at 26%. Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment. With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally? In a national Pew study in 2012, it was found that almost two-thirds of of respondents thought that America’s black and white populations were treated equally. In particular, 70% overall believed that there was equal treatment in the workplace.

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

iterations on placement and general layout of the final poster

Treated Equally

P op ulati on

of S T

LC ity tha t is

wh ite =

Population of STL City that is white = 45%

White unemployment in STL City = 6%

Black unemployment in STL City = 6%

b la c k = 48 %

White unemployment in STL City = 6%

Po p

u la

tion o

f ST

L Ci ty th

at is

Black unemployment in STL City = 26%

Population of STL City that is black = 48%

Even though white and black populations are most equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

With the stark employment disparity in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in the city county of St. Louis.

f on o ulati Pop

Ci STL

t ty tha

is white

= 45%

Populatio n

of STL

City that is bl ac k

f STL on o ulati Pop

= 48 %

White unemployment in STL City = 6%

Black unemployment in STL City = 6%

Populatio n

of STL

City that is bl ac k

= 48 %

Black unemployment in STL City = 6%

TREATED EQUALLY This graph represents population demographics in STL County. On the right is the white population (45% of the county’s total population), and on the left is the black population (47%). The white spokes represent the portion of each demographic that is unemployed within the county, with white unemployment at 6% and black unemployment at 26%.

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

% hite = 45 at is w

White unemployment in STL City = 6%

City th

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

f STL on o ulati Pop

City th

% hite = 45 at is w

Populatio n

of STL

City that is bl ac k

f STL on o ulati Pop

= 48 %

White unemployment in STL City = 6%

Black unemployment in STL City = 6%

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

% hite = 45 at is w

Populatio n

of STL

City that is bl ac k

= 48 %

White unemployment in STL City = 6%

City th

Black unemployment in STL City = 6%

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment.

final color studies—from these, I decided that a single saturated color would be used to call out the data and main points in my text 21

CRITICISM Readability was the general theme for the criticism of my poster, which is something I tackled at every stage of the process. Since I was using a more unique visualization, I needed the argument of the data to be obvious from a first glance, which required me to find the perfect balance of line weight, color, and value. My peers and professors were a large part of why I removed most of the color from my poster, because they often commented that it was harder to understand the information when hues became added factors. By the end of the project, I felt that I could still iterate on the relationships between my typographic elements, particularly with the scale shifts. In my final critique it was mentioned that I could try other treatments of the captions on my visualization, which I agree with and intend to test out. I think I could have extended my color treatments to the captions, and possibly used red to call out the text instead of changing the scale.

Percentage of STL City’s population that is white = 45%

Percentage of STL City’s population that is black = 48%

White unemployment in STL City = 6%

Black unemployment in STL City = 26%

TREATED UNEQUALLY The above graph represents population demographics in St. Louis (STL) City. On the left is the white population (45% of the county’s total population), and on the right is the black population (47%). The red spokes represent the portion of each demographic that is unemployed within the county—with white unemployment at 6%, and black unemployment at 26%.

In a national Pew study in 2012, it was found that almost two-thirds of respondents thought that America’s black and white populations were treated equally. In particular, 70% overall believed that there is equal treatment in the workplace. This majority opinion doesn’t reflect the reality of what is happening in many American cities, especially in St. Louis. With this unemployment inequality in St. Louis City, despite the near equal representation, how can we think that black and white demographics are treated equally?

Even though white and black populations are almost equal in STL City County compared to the other STL counties, it’s here we see the greatest disparity in unemployment. Citation for Above Data: “A report on the health and well-being of African Americans in St. Louis and why it matters for everyone.” For the Sake of All, St. Louis, Missouri. (July 31, 2015).

Citation for Pew Data (Left): “On Views of Race and Inequality, Blacks and Whites Are Worlds Apart: Social and Demographic Trends, 2016.” Pew Research Center, Washington, D.C (June 22, 2016).

final version