The Legacies of Redlining in Pittsburgh

by Devin Rutan

Pittsburgh is still defined by a geography of uneven development where modern disparities were built from historic patterns of discrimination. While searching through PghSNAP, I was struck by the similarities between the survey of prevailing building conditions and the Home Owners Loan Corporation (HOLC) map of 1937. So, I created a GIS-based framework to assess the legacies of neighborhood appraisal and lending discrimination in Pittsburgh by intersecting the HOLC map with census data.

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Current building conditions & 1937 Home Owners Lending Corporation (HOLC) divisions

Before getting swept up in the analysis, I want to be clear about what the HOLC map is and what it represents, so in the interest of brevity, I have highlighted some of the key aspects of the history. For a more thorough (perhaps too thorough) version you can read the full paper: here.

In the 1930’s, the federal government fundamentally transformed the mortgage market, creating the 30 year mortgage packages that make home-ownership accessible. In an effort to address perceived weaknesses in the housing market, federal officials advocated more ‘scientific’ appraisal methods. Appraisal ideology was forged within a climate of prejudice generally pervasive throughout white society, explicit institutional discrimination at most levels of government throughout the United States, and a heavily skewed distribution of economic power; policy makers saw little value in poor, African-American, immigrant, or Jewish communities and even viewed them as a direct threat to the value of middle class, white communities.

The maps are terrific localized representations of appraisal practices in each of the 239 cities they depicted. The HOLC maps were not directly published and used by bankers and appraisers to make lending decisions but were, nonetheless, certainly influential in the development of biased appraisals: the federal government published the tools, rationales, and examples necessary for banks to create maps of their own.

The first portion of my investigation tried to understand more about the impact of these practices at the time they were being honed. I intersected the 1940 Census, obtained from NHGIS, with the HOLC map. One of the strongest relationships within the appraisal of Pittsburgh neighborhoods was racial segregation. In the graph below, I arranged the communities by their HOLC ranking and then plotted the percentage of white residents in the tract (at this time, Pittsburgh had virtually only two racial groups). Looking at the four plots together, it is clear that a huge portion of Pittsburgh, regardless of value, was exclusively white. The dashed blue line represents the overall percentage of white people in Pittsburgh at the time; if communities were not segregated by race, they would hover around this line. Now consider the valuation of the tracts: not only were African Americans concentrated into a handful of places but they were relegated the lowest quality neighborhoods that were considered to be the least valuable (partially because of their presence). Also, a methodological aside, because census tracts encompass an aggregated area, segregation at the block level could only be starker.

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Next, I explored how these historic practices have continued to shape Pittsburgh. I used standardized census data to identify entrenched neighborhood characteristics from 1970 to 2000. Tracts that persistently had the largest proportions of African Americans were almost entirely aligned with red and yellow areas as you can see in the map below. Three main clusters of African American residents appeared: the Hill District, Manchester, and Homewood. These are communities that were historically considered the least valuable and were undermined economically and are, at least in part, still dealing with the effects. Further, tracts that persistently had the highest concentrations of poverty were also heavily focused in red and yellow areas. Red and yellow tracts were also much more susceptible to population loss than green or blue areas as you can see in the graph below. In many ways these spaces have held their position through time and the access, or lack of access, to mortgage financing had long reaching legacies. Groups that historically were victimized by appraisal ideology continue to occupy these spaces. These neighborhoods are likely less stable as well, considering the large presence of poverty and heaviest population losses.

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Black communities have suffered from disinvestment

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Pittsburgh’s uneven decline in population, 1970-2000

On the other hand, those communities that were uplifted by their historic value largely retained their status into modern times. Tracts with persistently the highest average incomes, home-ownership rates, even for African Americans, and the highest average values were all largely aligned with the historic green or blue categorizations. These communities benefited from unfettered access to the mortgage market and became the most stable, affluent neighborhoods in Pittsburgh because of their relative health. As you can see in the map for highest average values, Squirrel Hill, in particular, maintained its position. The disproportionate access to mortgage funds even continues today: according to PCRG, 7 neighborhoods received 50% of all mortgage dollars in 2015—6 of the 7 were historically rated either green or blue.

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Wealthy communities benefited from redlining

What is clear from the assessment of Pittsburgh’s geographic legacies of redlining is that the city is still largely defined by an ugly history of uneven development. As much as we may like to think that we have moved beyond pre-World War 2 or pre-Civil Rights Pittsburgh, we live in a city that is still, at least somewhat, constructed the same way. Policies that have attempted to create equality and opportunity for parts of the city that were left behind have failed to do so. Those parts of the city that were built on their exclusion have maintained their privileged and elevated status. Today, as we are having debates about neighborhood quality, accessibility, and inclusion, we must remember the specific history of uneven development. Are we comfortable with this geography? If not, what are we willing to do, lest it define us for another 60 years?

 

Devin Rutan graduated from the University of Pittsburgh with a Bachelors of Philosophy in Urban Studies and studied Applied Statistics and GIS. Devin cares about housing and neighborhood development and is currently working with the Northside Coalition for Fair Housing and the Pittsburgh Tenants Union. Originally from the DC area, Devin is an avid basketball fan: Let’s go Wizards! You can follow his work here or connect with Devin here: https://www.linkedin.com/in/devin-rutan-884517134/.

Data Day 2016

by Eric Darsow

Our digital age birthed another unusual occurrence: a tabling event devoted entirely and exclusively to the idea of data. Organizations of all sizes and girths carted in maps made in several centuries, charts of dazzling design, and slews of glimmering screens. A 3D printing robot was even spotted spewing layers of plastic into cute shapes. Amid this flurry of patterns and coefficients, the Students for Urban Data Systems (SUDSers) teamed up with nerds from CMU’s CREATE LAB to referee the pesky spar between the number crunchers and the story tellers.

The so-called “numbers and narrative” divide is turning out to be a chasm of our own making. While the process of regressing a spreadsheet full of figures obviously lacks a well-told story’s emotional pin-pricks, cryptic tabular outputs can, in fact, add dimensions of extent and intensity to an issue first illuminated by a personal narrative.

For example, how are we to make sense of, say, a sudden drop in high school test scores without talking to some teenagers about their experience bubbling in answers to mind-numbing test questions? The other direction works, too: few folks would rebuff a decision to augment an angry biker’s story about getting run off the road by a texting driver with a map showing ten years of bike crashes in Pittsburgh.

The SUDS + CREATE exhibit facilitated a safe crossing of this oft-feared number/narrative gap by displaying a few statistics about a central topic—such as transportation—and then inviting folks to write and physically connect a story or question to an otherwise lonely and contextless number.
data-day

One attendee affixed a short story about his personal experience with skyrocketing housing prices in his home city of Seoul. Pinned and ready for connections, another visitor complemented the narrative account with satellite images (pixel data) showing the Korean capital’s stunning vertical growth since the mid-1980s. Adding some sky shots of Austin, Texas’s metastasizing suburbanization over the same time period couched the sky-high rent story into a global context.

Even young people (perhaps less demoralized by hours of myopic method design meetings) sense intuitively the value of a well-told story alongside a chart or graph. One 9 year-old who visited our station looked over a bar graph depicting the average number of bicycle crashes by hour of the day. After a few minutes of thinking and talking aloud about the bars and axes, he used a marker and construction paper to ask all future board viewers: Why are there so many more bike crashes at midnight than 4:00 am? With an average bed time of 9:15 pm for children under ten in the United States, his wonder was about as genuine as it comes.

An enthusiastic transplant to Pittsburgh, Eric explores how the computerization of society impacts our geographic communities, social landscapes, and work identities. Eric is eagerly wrapping up his grad program in information systems at CMU and actively balances his screen-based life with wood carpentry and trying his hand at “installation art.” He serves as SUDS’s Assistant Director of Outreach, and interned at the CREATE Lab last summer.

What’s happening with Healthy Ride?

By Jackson Whitmore

The below analysis provides a quick look at the first data released by Healthy Ride Pittsburgh, a bike share system operated by Pittsburgh Bike Share. The system opened at the end of May 2015. This analysis evaluates data from the first quarter of the system’s operations (May 30 to Sep 30). Additional data will be released quarterly.

System Overview

From 2015/06/30 to 2015/09/30 a total of 40,083 trips were taken. These trips were broken down into the following categories: Customer, Daily, Subscriber.

The total number of trips made by each user category over the course of the time period are depicted in the chart below.

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As we can see, almost all trips were made using either a Subscriber or Customer pass. After a quick look at the Healthy Ride website, it appears that customer passes represent pay-as-you-go riders. Subscriber passes represent users who have purchased a recurring plan that allows for unlimited 30 or 60 minute trips, depending on the plan.

Daily passes were hardly used with only 39 rides taken. Interestingly, there does not appear to be a corresponding category for these passes on the Healthy Ride website. A quick check of the hypothesis that they may have been a promotional pass offered during the opening of the system is quickly discredited since the passes were used from 2015/07/04 to 2015/09/23. It would be interesting to find out exactly what these trips represent.

Finally, there were some records which, for whatever reason, did not have a user type recorded.

Temporal Aspects of Trips

As bike share programs are becoming more and more popular across the country, urban policy analysts and planners are attempting to identify their effect on the movement of people in and around cities. While a full analysis into the usage patterns of Healthy Rider users is beyond the scope of this document, a few quick plots can help us get a feel for how the system was used during its first few months in existence.

The below histograms depict the total number of trips taken by trip duration in 10 minute increments for each user type. Given the extremely small number of daily pass users, they were dropped from the analysis along with trips with no user type. Furthermore, trips over 500 minutes long were eliminated as they do not represent the riding patterns of the vast majority of users.

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It is immediately evident that riders making use of a subscription pass take many more short trips than those making use of a pay-as-you-go pass. This intuitively makes sense because these customers are only charged for trips over 30 or 60 minutes. Essentially, they are reducing their overall cost per trip with each additional trip they make within their limit. Additionally, we would expect someone signing up for a subscription to anticipate using the pass repeatedly, most likely for short trips such as a leg of their commute or to complete an errand.

However, these numbers are aggregated over months so let’s take a look at them for each day of the week.

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It is interesting to note that, in the aggregate, it appears that the number of subscription pass holders using the system for short trips remains relatively stable. There is a subtle increase in the number of very short trips being made by these users in the second half of the work week. Overall, this fits with our hypothesis that these riders planned on frequently using the system.

Riders with a customer pass see a large increase in trips made across all trip durations on Saturday and Sunday. Again, this makes sense as these users are most likely making use of the system for “leisure” rides which they may not make during the work week.

Let’s drill down some more and see how total system usage varied not just by day but by time of day as well.

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The above chart shows average (mean) trips throughout the system by time of day for each day of the week. We can see that users of the customer pass mostly use the bikes on weekend afternoons while users of the subscriber pass maintain a more consistent level of usage throughout the week. This fits with our previous hypotheses about how each type of pass holder utilizes the system. Finally, there is a spike in subscriber pass usage at the end of the workday indicating that for these users the bike share is favored more as a means of transportation after work than before.

Now that we have a general feel for the temporal characteristics of the system, we will take a look at how it is used spatially.

Spatial Aspects of Trips

The Healthy Ride system’s capacity is distributed throughout Pittsburgh but appears clustered in the Downtown area and then relatively segmented throughout the rest of the Pittsburgh area.

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Unsurprisingly, the system seems to be centered around Downtown Pittsburgh. Complementing the stations Downtown are a cluster of them on the North Shore, including one of the system’s largest stations. These were likely positioned to service the Heinz Field and PNC Park as well as the large park and rides on the North Side.

Of note is how the system exists in a relatively segmented state. The “segmenting” refers to the gaps of station availability between major Pittsburgh neighborhoods. For example, there are no stations connecting Downtown to East Liberty or Oakland. Furthermore, while South Oakland and the University of Pittsburgh are relatively well served by the system there is only one station near the Carnegie Mellon University campus.

These phenomena may be explained by the fact that the system was partly funded with federal Congestion Mitigation and Air Quality Improvement (CMAQ) funds. Funds distributed by this program are meant to reduce congestion and improve air quality via the reduction of car emissions. Thus, the current station placement schema was most likely developed in a manner which prioritized these criteria over system connectivity.

The map below explores how the usage of the Healthy Ride stations varies by hour for weekdays and weekends.

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It appears that the majority of the system’s trips originate in the Downtown for all days of the week. South Side also shows up as an origination hot spot. These two areas see a higher increase in average trips over the weekend than other areas, such as Shadyside, which remain relatively stable.

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Looking at average trips by hour for the work week we can see that system usage starts to pick up in the morning around 7am or 8am and then remains relatively constant throughout the course of the day. There are a large amount of trips made late at night in the Downtown and Oakland which may be a result of the large student populations in these areas. Either way, the larger than expected usage levels would likely prove interesting to investigate further.

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Quite interestingly, it seems that less trips are made late at night on the weekends relative to the day than during the week. It is important to note the relativistic nature of this comparison since in absolute terms many more trips are made during the weekend than on the week. Otherwise, weekend use seems to follow the same spatio-temporal patterns as the week.

Final Thoughts

The system as a whole seems to be used in the way that one would expect. For instance, there is a higher level of higher weekend usage and the majority of trips are concentrated around major population/job centers such as Oakland and Downtown. However, there are some interesting aspects of the system’s current state such as the system’s preponderance of non-subscription users and the high level of late night weekday usage.

For future analyses, it would be interesting to examine the effect of weather on the system’s usage, look into which stations generate trips and which stations terminate them during the peak periods, and as more data is released look into capacity constraints within the system.

Jackson Whitmore is a Public Policy and Data Analytics student at Carnegie Mellon’s Heinz College. His interests lie at the intersection of data and cities, specifically the transportation systems that support them.

Emojis of Pittsburgh

by Dan Tasse and Jennifer Chou

What do people in Squirrel Hill talk about?

Or, more interestingly, what do people in Squirrel Hill talk about that people in other neighborhoods don’t? What is it that makes Squirrel Hill Squirrel Hill? That’s the question we set out to answer with this project.

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Most frequently tweeted words in each Pittsburgh neighborhood

How it works

We gathered all tweets geotagged in Pittsburgh over about a year, from December 2013 to January 2015. We sorted them by neighborhood (using boundaries provided by the WPRDC) and used a modified TF-IDF algorithm to figure out what words were specific to each neighborhood. This algorithm counts the frequency of a word in a given neighborhood, and then adjusts the word’s final score based on how many other neighborhoods also use that word.

For example, “Steelers” is used a lot in Squirrel Hill, but it’s also used in many other neighborhoods, so it has a pretty low score. “Tunnel”, however, is quite popular in Squirrel Hill (mostly due to people grousing about tunnel traffic), but not elsewhere. Similarly, “10a” is a popular bus used to get around Pitt, but isn’t used elsewhere, so “10a” shows up a lot in Oakland.

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Tweets referencing the “10a” bus

An emoji is worth…

These words just represent what people are talking about on Twitter. What are people feeling? To answer that question, we looked to the emojis people are tweeting. Emojis are an interesting new form of communication: one character can often say more than a word, so they can tell us about where people like to do certain things, or maybe even how people feel.

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Top emojis in each ‘hood

For example, we can see that the zoo is up in Highland Park, and that people like watching baseball and football and drinking beer on the North Shore. Obvious enough. But did you know how popular the swimming pool in Oakland is, or the Christmas tree lighting downtown?

Future work, and so what?

There’s still work to do, of course. One major challenge is algorithmic: How do we combine these posts from multiple people into a representative aggregate? A lot of these words/emojis are boosted by one person tweeting them multiple times. We don’t want one person to dominate the neighborhood’s tweets, but we do want an avid basketball fan to count more than someone who just tweeted about basketball once.

We hope this is the first step towards useful neighborhood guides. Imagine if you were moving to Pittsburgh for the first time, and looking for the right area to live in. Knowing that Squirrel Hill South has a lot of basketball fans, or that the top words in Lawrenceville are trendy bars or music venues, could really help you get a feel for the city and its many unique neighborhoods.

Try it out! http://emojimap.herokuapp.com

(Be patient; it’s on a free server so it’ll be a little slow.) And send any feedback or ideas to dantasse@cmu.edu.

Dan Tasse is a PhD student in Human-Computer Interaction at CMU. He’s interested in how we can use social media posts to help people understand their cities and neighborhoods better.

Jennifer Chou is an undergraduate studying Computer Science at CMU.

Energy for all in Nigeria

by Madeleine Gleave

Nigeria’s energy poverty crisis

Like many developing countries, Nigeria is facing an energy poverty crisis. The International Energy Agency (IEA) estimates that nearly 1.3 billion people globally lack access to electricity, and about half of these people live in Africa. Energy poverty has crippling side effects; no electricity also means no access to safer and healthier electric cooking and heating, powered health centers and refrigerated medicines, light to study at night, or electricity to run a business. In Nigeria, the average level of access is only 53%.

Despite being rich in natural resources required to produce energy, such as oil and gas, Nigeria’s energy infrastructure is lacking. Many people live near power plants and transmission lines, but aren’t yet connected to the grid. Others are in very remote areas where off-grid solutions, such as solar panels, may help them generate their own electricity long before a power line reaches them.

To explore this problem, I created a StoryMap in ArcGIS that shows the highly disparate levels of electricity access, energy demand, and infrastructure across Nigeria.

Check out the full StoryMap here:

Identifying the best electricity access solution

As Nigeria and its development partners look for energy access expansion solutions, how can they choose the best intervention for the best region? Where should they target grid connections, grid expansion, or off-grid solutions?

Selecting the best approach from this set of solutions depends on the context of the specific geographic area, and is influenced by existing levels of access, proximity to existing lines and power plants, level of urban development, demographic characteristics, and income levels. I developed a composite energy access index, mapped using a kernel density heat map, to evaluate an area’s suitability for each type of access intervention. The higher the index score (the red areas on the heat map seen here), the more suited the area is to grid supply. The lower the index score (pale yellow), the more suitable for off-grid power. Mid-range scores (the orange and dark yellow areas) are good candidates for grid expansion.

heatmap

Madeleine Gleave is a Public Policy and Management student in the Heinz College at CMU. She is particularly passionate about using data to improve planning, management, and evaluation in international development policy and programs.