Final_Project_EdieGodwin

Part One

Here is a very broad, general overveiw of what I plan to do for my final project. I had trouble picking a topic but I think I found one that is both important to me and should lend itself well to different data visualizations: gentrification.

Final Project

Gentrification is characterized by a low-income area beginning to conform to middle-class tastes. Generally gentrification brings increased property values and investment to a neighborhood. While this is not inherently bad, the connotation of gentrification focuses on what happens when it is done poorly. Without the proper policies in place, gentrification can lead to the residents of a neighborhood being pushed out to other low-income areas for middle-class (typically white) families to take their place.

Here in Pittsburgh, residents often point to the Lawrenceville neighborhood as the poster-child for gentrification. The area was built by low-income, diverse, working families into a very interesting, eclectic neighborhood. In the last decade or so, it has been that unique vibe that attracted investment and interest to the neighborhood.

I’m interested in seeing if Lawrenceville has been a victim of gentrification or if the city did a reasonably good job in protecting the citizens of Lawrenceville. I’ll do this by comparing census data from 2011 with the most recent data, 2017, for median home values, median incomes and demography.

The Story

Starting with what gentrification is and looks like and explaining the salience of the three metrics I’m looking at, I’ll move into comparing data from 2011 to data from 2017 for zip codes 15201 and 15224. I would like to present some distributions and maps to show the differences between 2011 and 2017 Lawrenceville. If available, it would be interesting to see migration patterns of families who came to or left Lawrenceville, but that data may not be available. Depending on the findings from the data I would like to sugegst that people either look to Lawrenceville as an example or a cautionary tale. Gentrification doesn’t always have to be bad, so there is a very real chance that locals just don’t like the recent changes to the neighborhood.

The Data

To create compelling points and data visualizations I will pull from American Fact Finder.

https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_B25077&prodType=table
-Median Home Value

https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_S1901&prodType=table
-Distribution of incomes (adjusted for inflation)

https://ucsur.pitt.edu/files/census/UCSUR_SF1_NeighborhoodProfiles_July2011.pdf
-Demographic information

Method and Medium

Using colors from the Lawrenceville website, seen here: http://lvpgh.com/, I will create infographics and graphs using a mix of Excel, RawGraphs and Canva. I would like to create maps using ArcGis to show the variations in home values or incomes, whatever would be the most impactful on a map. Maps will be helpful because they will provide the reader with more context than would otherwise be possible. I will present my project using Shorthand. Their smooth scrolling will be conducive to the flow of my story and the format will allow me to frame the story without having to touch a powerpoint

Part Two

User Research Protocol:

Target Audience: Community Groups/ Citizen Action Orgs.
Identifying Individuals to Interview: A local community group meets at the public library on Saturdays and several Lawrenceville residents go to my gym. Both groups would be open to looking at and critiquing my planning documents.
Interview Script:

  1. What do you think the point of this graph/chart is?
  2. What tells you that (their response) is the point of the graph/chart?
  3. What tone is conveyed by the color palette used? What do these colors remind you of or make you feel?
  4. How would you use this graph/chart?

Story Board

Findings

I presented my sketches (below) to four people and I have summarized the responses that I got for each sketch.

Median Household Income (2011 v. 2017)

  1. Everyone who saw it understood that it was a comparison of median household income and that it had increased from 2011 to 2017.
  2. It had a “clear title” and “is a simple graph”
  3. The colors used gave it a positive feeling- influencing what people answered next:
  4. Most people said they would use this graph to show that Lawrenceville is doing better now than it was and that they might show this to potential commercial investors

Change in Demographic Composition from 2011 to 2017

  1. People had to look at this one longer to understand what it is, but they all came to understand it on their own. (Some people would ask why it started in the center but then would see that it is a % change)
  2. Again, this one has a clear title, but most people wanted the demographic categories spelled out, not just represented by one or two letters.
  3. People liked the colors for this one because they made it very easy to distinguish one column from another.
  4. Half thought they might use this to give background and context for Lawrenceville, the other half was unsure of a specific way to use it but said they did think it could be useful.

Median Home Value (2011 v. 2017)

  1. All respondents looked at this one the longest and were unsure of the conclusion they came to (even though it was correct, they weren’t confident in their answer)
  2. Again, respondents were unsure in their answer, telling me that the average person is not as comfortable reading a distribution as I assumed they would be.
  3. No real comment on tone, mostly that it looks cheerful.
  4. People were unsure of how to use this outside of a paper.

Changes Implemented:

Median Household Income (2011 v. 2017)

Because we’re talking about gentrification and displacement of low-income families, I changed the colors here to be a little darker for a more serious tone.

Change in Demographic Composition from 2011 to 2017

No changes made.

Median Home Value (2011 v. 2017)

This one I changed to be a bar graph, something that I think most people are more familiar with and can understand faster than they could the distributions.

Part Three

Planning and Process

Data

Step one was finding public data sets. Using census data for zip code 15201 (Lawrenceville) from American Fact Finder, I was able to access enough data to have substantial, meaningful findings.

I looked at demographic, income and housing data from 2000, 2010 and 2017 5 year estimates. This will let me identify the long term possible effects of gentrification in Lawrenceville. I intended to create a map of home values but realized that I would need parcel-level data about the value of the homes in Lawrenceville, which I could not obtain.

The data that I did get from American Fact Finder was good for comparing the incomes, home values and demographics from year to year. The visualizations I made are fitting for my intended audience because they are easy to read and fairly common but they aren’t unique. I felt that some of the visualizations available to me on datawrapper or Raw Graphs would be too foreign to my audience and therefore frustrating.This idea was echoed in the responses I got from interviews about my data sketches. People generally understood bar graphs, but the home value distribution took people longer to decipher and they were less likely to understand what it was. I want my data to be useful and peple won’t use data that they feel is hard to read.

Planning

When thinking about who the audience for this data is, I decided that I would want to present this data to community groups and local community leaders. The two personas I used to create my content I called Beverly and Ben.

Beverly : older, in her 40’s to 50’s, and cares a lot about her neighborhood. She joined a community action group to participate and steer her community. She has been out of school for a long time and academic jargon is frustrating for her. She doesn’t want to spend very long looking at complicated graphs or graphics but she understands the importance of data in decision making.

Ben : younger than Beverly, 30’s, but just as dedicated to his neighborhood. He grew up here and is in a position of power or influence. He has a Bachelors and is not uncomfortable with academic style papers but does prefer easier reads. He is busy and also doesn’t want to spend a lot of time having to figure out graphics.

I chose Shorthand to present my data because it is an attractive, public platform. If I presented this to a group they would have instant access to the whole presentation.

Creating The Visualizations

I changed the types of graphs that I planned on using after the feedback I got. Along with changing the kinds of graphs, I had to change my color schemes to reflect more of the serious tone of the material. The later versions have more blues and an orange accent, the colors of Lawrenceville that I got from their website (http://lvpgh.com/).

The Outcome

Findings can be found here:

https://carnegiemellon.shorthandstories.com/LawrencevilleGentrification/index.html

If I were to do this again I would probably learn some HTML to make Shorthand work for me a little better. It is more customizable if you understand and can use HTML. As it is, it has good functionality but could be better. I also would add some data sets like rental prices, numebr of homes, and possibly percentage of abandoned properties in the area. All of these would have helped paint a picture of just how gentrification had changed the neighborhood. It also would have been interesting to collect data from some of the surrounding areas like Friendship or Bloomfield, to see if there was a significant effect on other neighborhoods.

The data that I do have seems to suggest that gentrification in Lawrenceville displaced some families and has left the neighborhood more expensive than it was pre-2009. Home values and incomes have gone up but total population and the percentage of non-white citizens has decreased. Some people say that administration did a good job of protecting citizens, but it seems pretty clear that low-income citizens were pushed further out of the city. I end on a call to action to encourage people to get involved in their neighborhoods. Often times the only advocates a neighborhood has are its citizens, so it helps to take a proactive stance. If citizens start demanding preotections from gentrification before it happens, they will surley be better off.