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.

1 & 2

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.

3

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.

4

Black communities have suffered from disinvestment

5

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.

6

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/.

The Affordable Housing Problem and Investor Behavior in Pittsburgh

By Nick Kharas, Claire Jacquillat, Erin Yanacek, and Maksim Khaitovich

Our cross-functional team from Heinz College and Tepper is interested in looking at the affordable housing scene in Pittsburgh. This was part of a case competition we won, jointly organized by SUDS and the Data Analytics clubs at Heinz and Tepper. Eventually, the topic grew on us as we shared similar experiences while searching for a house to rent or buy. Currently, Pittsburgh has an affordable housing deficit of 17,000 units. Affordability is a reflection of the price of the house, its condition, and the livability of the surrounding area. Thus, solutions to Pittsburgh’s housing challenges need to focus on healthy community development. Keeping that in mind, we propose an “Assess and Address” framework that identifies investor behavior and creates a system of incentives and penalties to address negative influences.

Hazelwood – Our Pilot

Like many other neighborhoods in Pittsburgh, Hazelwood has been hit hard since the city’s steel mills shut down. Still, it has maintained its tight community spirit. New developments like technology company investments, startups and malls are reviving communities in neighborhoods like East Liberty and Lawrenceville, but at the risk of gentrification. Hazelwood is representative of what has been happening across Pittsburgh. Recent developments like Almono and Summerset at Frick Park have the potential to revive the neighborhood. At the same time, we must also ensure that these new developments bring positive change and not displacement for longtime residents.

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Hazelwood’s community focus is still intact, but the recent developments could attract bad investors. As we see from the comparison of average property prices, Hazelwood does not have cases where properties in poor or unsound condition are sold at inflated values, unlike the rest of Allegheny County. While this is a good sign for Hazelwood, it still has some vacant properties and many houses in average or fair condition, which could potentially attract attention from bad investors in the future, if they aren’t already there.

2

Note: The graph highlights outliers indicating that some properties in poor or average condition are sold for significantly inflated values. The edges of each box in the plot indicate the interquartile range of sale values for each property condition. The flat line within each box is the median. The dotted lines are the outliers. For our assessment, we considered all valid sales from the property assessments data maintained by the WPRDC. Also, we used zip code 15207 to distinguish Hazelwood from the rest of Allegheny County. Although 15207 covers parts of Glen Hazel and Greenfield, it represents the challenges we aim to address.

3

Investor Behavior – Who Are These “Bad” Investors?

A neighborhood stands to benefit if houses in poor or unlivable conditions are purchased and redeveloped. However, some investors are only interested in buying and reselling houses to make a quick profit, without improving their condition or spending on maintenance. Unlike “rehabbers”, “flippers” and “milkers” buy and keep properties in distressed conditions, and hope to sell them off for a profit as quickly as possible. Such behavior does not attract the healthy investors, and also negatively affects the community and quality of life in the neighborhood.

4

We decided to look at data driven ways to identify and predict bad behavior using the property assessments data maintained by the WPRDC. Although we do not have any information on house owners, our cognitive solution identifies and flags potentially bad investments and highlights insightful characteristics.

The data does not have class labels that identify bad investments. We ran a k-means clustering algorithm on ownership duration, property value appreciation, and sale price to set our class labels for good and bad investments needed for analytical modeling. We avoided using general definitions for flippers to determine class boundaries as that could ignore any hidden patterns and add bias. For example, any house resold within 12 months for less than $100,000 is potentially a transaction conducted by a flipper. However, using this information alone to set class boundaries ignores several transactions where the property was held for only a little more than a year. The results from clustering directed us to target parcels that were

  • Owned for less than three years, and,
  • The property value depreciated, or appreciated less than 15%, or was sold for less than $90,000.

Any parcels that met the above condition were flagged as properties owned by potentially bad investors, while the rest were labeled as unsuspicious.

5

6Once we were able to set the labels for good and bad investments, we ran few classification algorithms to predict investor behavior. We excluded 20% of the data for testing, and trained the remaining 80% data on different classifiers like Gradient Boosting, Random Forest, and Conditional Inference Trees (a modeling technique based on unbiased recursive partitioning). All returned an AUC between 0.70-0.72. Gradient Boosting and Random Forest with feature selection returned marginal improvements, but not significant.

 

Our primary challenge was to not let our results get affected by any bias in the data. A majority of the records are not classified as bad investments, and around 90% of the parcels are not red flagged. This makes sense, as we cannot expect the property market to be completely overrun by flippers. Additionally, the assessments data has missing values for some fields, including those which determine our class labels. For example, some parcels do not have previous sale records, making it difficult to determine how long the house was held before it was sold. We cannot assume that this is missing data, as the property may not have changed ownership more than once in its lifetime. Further, the assessments data set gives us the parcel characteristics as of now, and not as when the property was last sold. For example, the condition of a property may have either improved or deteriorated after it was last sold four years ago, but we have no way to find out. For this reason, the simpler models did not perform well. Logistic regression proved to be computationally expensive with a large number of categorical variables, and decision trees performed poorly on test data.

7

We have shared our detailed analysis on GitHub.

 

Our Proposal – Assess and Address

A city like Pittsburgh would like to identify and avoid bad investor activity. However, in an effort to maintain housing affordability, the city cannot drive away potentially good investments that can develop and enrich its vibrant community. To maintain this balance, we propose an Assess and Address framework that gives actionable recommendations.

8

Assess

Confirm Results with In-Person Observation

Our analytical model can highlight and flag properties that are potentially at a risk of being owned by flippers. The city inspectors and community leaders can export a list of such at-risk targets, look up information on their owners or landlords, and monitor their behavior.

Address

Sanction Bad Practices

Install practices and systems that would discourage landlords from mistreating their tenants and violating requirements for minimum standards. Our suggestions include:

  • Minimum Property Standards established by the Allegheny County Health Department for rental properties.
  • Rental Registration – Force landlords to act responsibly towards tenants. A good example is the Probationary Rental Occupancy Permit (PROP) set by the city of Raleigh, NC, which aims to ensure better housing quality for tenants and discourages landlords to violate City Codes.

Foster Positive Practices

These are programs that would encourage good investors and community homeowners to invest in enriching and developing the city’s community spirit. Some suggestions and examples of their implementation include:

 

We hope to take this initiative forward with the help of SUDS at Carnegie Mellon University and present our findings to the city council at Pittsburgh.

 

Nick Kharas graduated from Carnegie Mellon University with a Masters degree concentrating in Data Analytics and Business Intelligence. Prior to his time at CMU, he was a business intelligence and data warehousing SME at a Japanese multinational financial holding company. When not a data buff, he enjoys travel, sport and meeting new people. Click here to check out his work on GitHub. You can also connect with Nick at https://www.linkedin.com/in/nickkharas.

Claire Jacquillat is an MBA candidate at Carnegie Mellon’s Tepper School of Business. She focuses her studies on Operations management and Operations research. She is the president of Tepper Data Analytics Club where she strive to foster an integrated use of business analytics in various industries. Before starting her MBA at the Tepper School, she worked as a strategist in Sales Enablement for a Fortune 500 company. You can connect with Claire at https://www.linkedin.com/in/clairejacquillat/en

Erin Yanacek is an MBA candidate at Carnegie Mellon’s Tepper School of Business. In summer 2017, Erin will join McKinsey as a Summer Associate. Prior to business school, Erin was a classical musician. She founded a non profit organization, the Chamber Orchestra of Pittsburgh, and toured internationally performing and teaching classical trumpet. You can connect with Erin at https://goo.gl/BEWz1L

Maksim Khaitovich is an MBA candidate at Carnegie Mellon’s Tepper School of Business. In summer 2017, Maksim will join A. T. Kearney as Summer Data Science Associate. Prior to business school Maksim worked as an engineer and IT consultant in fintech and wireless communications. You can connect with Maksim at https://www.linkedin.com/in/maksim-khaitovich-828a2b47/

Criminal Justice Work Night highlight: do police from smaller units use force more often?

At our first Work Night a few weeks ago, SUDS members dug into data on crime and criminal justice–particularly from the 2013 Law Enforcement Management and Administrative Statistics (LEMAS) survey. One student, Kee Won Song, pulled together some interesting initial insights and a sweet chart in just a few hours. He writes:
I am interested to see if we can identify factors that contribute to use of force incidents.  Specifically, I am interested to see if factors like employee demographics, education level of employees, size of department, participation in academic research (which we might also use to assign a score for ‘transparency’), budget, training methods, number of specialized units, use of data/computers in evaluating performance etc. have any affect on the frequency of use of force.  I did not get to analyze many of these factors, however, this is one figure that I produced that plots use of force incidents (expressed as incidents per employee) against total employees (full-time plus part-time):

LEMAS surveyIt’s hard to say that anything substantive can be gleaned from the visualization but it might allow us to further focus on smaller departments that have a high use of force rate (or identify outliers for further analysis).

Kee Won Song is a full-time MPM student at CMU, who is also completing Masters of Sustainability at Chatham; his interests include researching the impacts of unconventional oil and gas extraction on air quality, particularly in underprivileged communities.

Update: Additions to Police Department Map

Screen Shot 2016-07-19 at 19.20.02
This article was written as part of SUDS’ partnership with the
Alliance for Police Accountability.

We have updated our post on Allegheny County’s police departments to correct a number of omissions. We’ve made the color scheme simpler so the map is easier to read. And it looks like we touched on a hot issue.

The broader conversation

In our previous post, we highlighted the overlap between departments. We didn’t mention the size of each department. However, several people are talking about the number of small police departments in the US.

In an interview for Heinz blog, CMU’s own Professor Daniel Nagin said that:

“Part of the issue with training is that, in the United States, there are over 18,000 police departments, and most of them are very small. And when you have these little police departments, the capacity to properly train the police officers and establish a culture of accountability is really limited. So I think there’s an important need to consolidate the number of police departments that exist nationwide, for a variety of reasons.”

At The Conversation, Paul Hirschfield discusses how “localism” distinguishes American from European police:

“Each of America’s 15,500 municipal and county departments is responsible for screening applicants, imposing discipline and training officers when a new weapon like Tasers are adopted. Some underresourced departments may perform some of these critical tasks poorly.

“To make matters worse, cash-strapped local governments like Ferguson, Missouri’s may see tickets, fines, impounding fees and asset forfeitures as revenue sources and push for more involuntary police encounters.”

On Meet the Press, Chuck Todd interviewed former Philadelphia Police Commissioner Charles Ramsey. When Todd asked, “…frankly, we’ve seen a lot of these negative interactions between police and African Americans have actually taken place in smaller, suburban departments. Is there a discrepancy between training [sic]?”, Ramsey replied:

“Well, I mean, you raise a great issue. There are approximately 18,000 departments in the United States. In my opinion, far too many. And we need to look at a long-term goal. More regionalization, better training, more consistency in policy and procedures.

“In your larger cities, where you have a lot of diversity, obviously you have officers that are very accustomed to dealing with a variety of people. We still have parts in our country where that’s not the case. We need to bring people together, but we need more consistency in terms of the training that’s provided, the selection and hiring of individuals. All those kinds of things need to happen. But in my opinion, we have too many police departments. I would try to cut the number in half, maybe by, in the next ten years or so. Because you are always going to have these kinds of issues as long as you have this many departments with different policies, procedures, training and the like.”

At The Daily Beast, Lauren Caroll and Jon Greenburg discuss Ramsey’s argument: Could a cut in the number of police departments reduce police killings?

So how do we cut the number of departments? In Allegheny County, we have two models for reducing municipal police departments: (i) closing a department and contracting out police work to another municipality, or (ii) merging existing departments to create joint ones.

Seventeen municipalities don’t have their own police force, like Dravosburg Borough, in which policing is “contracted out to the McKeesport Police Department”. Both joint and contractor departments have advantages over smaller departments. Information is automatically shared between the municipalities, training and hiring practices are standardized, and larger departments can provide specialized services.

Northern Regional Police Department is the only merged police department in Allegheny County. It is a collaboration between Bradford Woods Borough, Pine Township, Marshall Township and Richland Township; all four municipalities have a say in how the department is run. Joint departments some additional advantage over contractors. The people policing each municipality are more likely to be locals, and residents are likely to have more say in how the departments are run.

However, we (the authors) don’t know as much about non-municipal police departments, such as the Port Authority Police, CMU Police or UPMC police. Given the recent shooting by PAT Officer O’Malley, it seems likely that the problems of small departments and inconsistent training can also exist in transit, school and private police departments. We have a new open question: do these types of organizations ever get rid of their police, and go back to relying on civic police departments?

Updates, additions and answers to questions

Thank you to Eat That Read This for highlighting our original blog post, and to the Alliance for Police Accountability and WPRDC for tweeting it. We received helpful feedback as a result of the increased attention:

  • Thank you to Mx Daria Phoebe for pointing out that Norfolk Southern rail police are accredited, and sending us an overview of rail policing. We have added the Norfolk Southern department to the map.
  • Thank you to JI Swiderski for pointing us to a complete list of municipal police departments, and answering our question about the Northern Regional Police Department. We’ve added forty municipal police departments to the map, including Northern Regional. For the seventeen municipalities that contract out their police services, we have added who they contract out to. 

We also discovered a new department ourselves:

Future directions

We presented the map at the Alliance for Police Accountability’s last community meeting, and got several suggestions on other things that we could map out, including:

  • The demographic makeup of police departments vs. makeup of the communities they serve. The community data is available through the US Census, and the Pittsburgh Police provide annual reports with data on their demographic makeup. We will need to contact the other police departments to learn their demographic makeup.
  • Map out all incidents investigated by Pittsburgh’s Citizens’ Police Review Board, and investigate whether there is a “chilling effect”, i.e. a decrease in calls to police in nearby areas after the incident.

The list of police departments was low-hanging fruit, because all the information was already online. These questions will take longer to answer because we will have to gather more data.

If you have relevant data, or more suggestions for things we could investigate, we would like to hear from you. You can comment here or contact us at silver@cmu.edu and lrenaud@andrew.cmu.edu.

Lauren Renaud is a masters student in Data Analytics and Public Policy at CMU.
Lizzie Silver is a PhD student in Logic, Computation and Methodology and a masters student in Machine Learning at CMU.
In their spare time they participate in Students for Urban Data Systems at CMU, and do research for the Alliance for Police Accountability.

The Overlapping Police Departments of Allegheny County

By Lizzie Silver and Lauren Renaud

banner

This article was written as part of SUDS’ partnership with the Alliance for Police Accountability.

“Did you know that there are over one hundred police departments in Allegheny County alone?” Prof David Harris offered this factoid at the panel discussion following the April 12 screening of Peace Officer. It sat at the back of our minds until finals were over and we started looking for a new mapping project. Spoiler alert: we were ‘only’ able to count 85 departments. [Update: we’re up to 127 departments. Details here.]

Recently, two adverse interactions between the police and the public involved officers from at least two departments: the Port Authority (PAT) Police and the City of Pittsburgh Police. The first incident involved fighting between youths and police officers at the T station downtown. In the second incident, PAT Police Officer Brian O’Malley shot and killed Bruce Kelly Jr, raising questions about PAT police policies. As Tony Norman writes, “Why the disparity in response between the officers? Why did only two of [at least eight officers] fire if Mr. Kelley was so dangerous?”

Police departments work together to solve problems when their jurisdictions overlap. Officers from different departments must coordinate with each other in high-pressure, high-stakes situations. Different departments sometimes have different training or different policies, so the way they interact with the public can vary. After the Peace Officer screening, Pittsburgh Chief of Police Cameron McLay mentioned that training on implicit bias will be available to City of Pittsburgh officers in the near future. However, that training will not be available to Port Authority officers, and officers from many other departments that have jurisdiction within the City of Pittsburgh.

Furthermore, the laws that govern Pittsburgh only apply within city limits. Recently Pittsburgh decriminalized possession of less than 30 grams of marijuana. So if you are walking down S Braddock Ave between Biddle and Whitney, smoking a blunt, the local police can only fine you $100. But walk past Overton St, and suddenly you’re committing a criminal offense that could cost you $5000, land you in prison for 30 days, and give you a criminal record, because Swissvale is outside city limits.

Even if the law is the same in different areas, the “unwritten rules” can differ. As Ryan Deto writes for the Pittsburgh City Paper:

“Mount Lebanon police cited Esquivel-Hernandez on March 26 for driving without a valid license and without insurance. He paid his fine on April 21, and according to his U.S. District Court case, he was identified as undocumented on April 25. Mount Lebanon police have not returned multiple calls requesting comment about its communication policy with ICE.
[…]
Since 2014, Pittsburgh Police have had an unwritten policy not to initiate communication with ICE about undocumented immigrants. The department will comply if ICE initiates contact. However, there is no indication that other smaller police forces in Allegheny County have adopted similar policies.”

Different police departments are also held accountable by different bodies. For example, the Citizens’ Police Review Board (CPRB) can investigate complaints against the the City of Pittsburgh police department, but the Port Authority Police do not answer to the CPRB, only to the District Attorney. DA Stephen Zappala exonerated Officers O’Malley and Rivotti for shooting Mr Bruce Kelly Jr, a questionable but unsurprising result, given how rarely police officers face charges for shooting citizens.

[Update: see also our post about the problems of small police departments.]

We thought it’d be helpful for the public to know where the different police departments’ jurisdictions are located. This may help if:

  1. You are wondering which police departments you are likely to interact with in a given area;
  2. You need to contact the police, and are wondering which department to call; or
  3. You have had an interaction with police officers, but you are not sure which department they were from.

We welcome feedback and additional information. We hope this map helps inform citizens about the different departments that serve them, and helps to start a conversation about standardizing training and policies for officers from different departments.

The Map

[Note: This updated version has more police departments included and a better color scheme. Our original, less complete version can still be accessed here.]

http://carnegiemellon.maps.arcgis.com/apps/View/index.html?appid=813f1c964eef4bf59fbbfc18b1ed418e&extent=-80.3644,40.2634,-79.6448,40.6323

  • Larger version of this map here
  • To see the number of overlapping jurisdictions in any given area, see this map with semi-transparent overlays here.
  • To see both, with the “spyglass” feature for more detail, see this map here.

[Old versions available here, here and here.]

The Departments

Municipal Police

There are 130 municipalities within Allegheny County, of which 70 have their own police department. We matched the county’s list of police departments to a list of municipality borders from the county’s GIS department.

One municipal police department could not be matched by name: the Northern Region Police Dept in Gibsonia, PA. It may be a joint department covering multiple municipalities.

We’ve assumed that the other municipal police departments only cover the areas they are named after. However, some municipalities have joint agreements so that a single department will patrol two municipalities. For example, North Versailles police currently patrol Wilmerding (although from 2017 onward that duty will fall to Allegheny County police). We are not aware of most of these agreements, so we can’t put them on the map. If you have more information about these agreements, please email us at our contacts below.

[Update: Thanks to feedback from JI Swiderski, we have corrected this layer of the map. Allegheny County has 130 municipalities, of which 109 have their own police department, 4 share the Northern Region joint police department, 15 contract their police services out to a nearby municipality, and 2 contract out to state police. Allegheny County’s list of police departments includes information on these contracts. We matched that list to the list of municipality borders from the county’s GIS department, so the updated map shows which PD has jurisdiction in every municipality. More details we’ve updated here.]

County Police

The Allegheny County Police Department’s jurisdiction includes ‘county-owned property’, as well as ‘Pittsburgh International Airport, the County Airport, nine County Parks and other regional parks’. We weren’t sure which ‘other regional parks’ were included, so we only mapped out the county parks. We have marked all county buildings with a spot that extends 250 feet from the center of the building. We got the building locations and the park shapefiles from from the county’s GIS website.

The Allegheny County Police might also patrol municipalities that don’t have their own police departments, but we weren’t sure which ones (if any), so we have not included any on the map.

The Allegheny County Sheriff’s Office also has jurisdiction in Allegheny County, but we weren’t sure what areas it extends to, or whether they do any patrolling, so we have not included them on the map.

Federal and State Police

The federal US Marshalls (Western District PA) has jurisdiction throughout Allegheny County, as do the Pennsylvania State Police

[Update: The PA Fish and Boat Commission polices the rivers.]

Transit Police

The Port Authority (PAT) Police have jurisdiction over PAT routes and nearby areas. We have marked the PAT routes with a line that extends 1/10 of a mile (528 feet) around the route. We think this is a conservative estimate of how far the PAT jurisdiction extends. However, if we widened the buffer much further — say, to 1000 feet — the PAT jurisdiction looked like it blanketed the entire city of Pittsburgh. We got the shapefiles from the City’s GIS website.

Amtrak police have jurisdiction over Amtrak routes and nearby areas. We have marked the Amtrak routes with a line that extends 1/10 of a mile (528 feet) around the route. We got the shapefiles from ArcGIS.

[Update: Thanks to Mx Daria Phoebe for pointing out that Norfolk Southern rail police are also accredited.]

School Police

Most of the universities in Pittsburgh have their own police. Their jurisdictions extend across the college campuses, to student dorms, and nearby areas. For example, Carnegie Mellon University Police’s “primary patrol zone… includes all campus property, Off Campus Housing & Sites, and residential areas immediately in the vicinity of the CMU Main Campus.” We do not have shapefiles for the college campuses, so instead of tracing the shape of each campus, we’ve just put big circles on the map (centered on each campus, with a radius of half a mile). There is one circle, half mile radius each for:

We left the Community College of Allegheny County (CCAC) off the map. Each of their campuses has “a Director of Safety and Security who is also a sworn police officer”, but we felt that one officer per campus was too few to count as a “department” for the purposes of this map.

Hospital Police

UPMC employs police. St Clair Hospital Police “will issue citations to those who violate parking and traffic regulations”. Neither hospital has a webpage describing their police departments; they may simply employ police officers alongside security officers. However their police have arrest powers and are employed by the hospital so we put them on the map. We assume that their jurisdictions cover the hospital complexes. We have put circles on the map for each UPMC building complex and for St Clair Hospital. We weren’t sure how large to make the circles, so we put circles with radius of both 250 feet and 500 feet around the center of the buildings; you can turn one overlay off if you prefer.

Humane Society Police

Humane Society Police Officers enforce animal cruelty laws. They are appointed per county so their jurisdiction covers the whole of Allegheny County. There are a number of Humane Society Police Officers serving Allegheny County.

Assumptions and Open Questions

We’ve tried to be conservative in our estimates of where the departments’ jurisdictions extend to, but we may have made some mistakes. We also have some open questions, such as:

  • What kind of authority, if any, do Block Watch Groups have?
  • Do some correctional facilities have their own police? Do corrections officers have arrest powers in PA?
  • Does the Allegheny County Police Department only have jurisdiction over buildings that house county departments and services, or all county-owned buildings?
  • Should the Allegheny County Sheriff’s Office count as another department? If so, what is its jurisdiction?
  • Do UPMC Police cover all UPMC hospitals, or only certain ones? What sort of authority do they have?
  • Which police department(s) have jurisdiction over the municipalities that do not have their own police force? We believe it may be the County Police or that some departments that are listed as a specific municipality may also cover their neighboring towns, but we need more clarification on this.

This map may not be complete. If you know about another police department within Allegheny County, please send us a link to the police department’s webpage, or let us know who we can contact to confirm the department’s existence. Also, if we have made a mistake, please let us know so we can correct it. You can contact us at silver@cmu.edu and lrenaud@andrew.cmu.edu.

Lauren Renaud is a masters student in Data Analytics and Public Policy at CMU.
Lizzie Silver is a PhD student in Logic, Computation and Methodology and a masters student in Machine Learning at CMU.
In their spare time they participate in Students for Urban Data Systems at CMU, and do research for the Alliance for Police Accountability.

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.

Want to host open data? You can with CKAN!

By Matt Cleinman

If you visit many government open data websites, you may notice that they all start to look very, very similar.  (For some examples, look at the UK national government, Washington DC, and our own Western Pennsylvania Regional Data Center.)  Your eyes are not going numb from looking at datasets – it’s that many are powered by CKAN.  

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What is CKAN?  It’s a behind-the-scenes secret that helps make open data possible.  In their words:

CKAN is a powerful data management system that makes data accessible – by providing tools to streamline publishing, sharing, finding and using data. CKAN is aimed at data publishers (national and regional governments, companies and organizations) wanting to make their data open and available.

Even better, this web application is open source, meaning that anyone can see the sourcecode and add features for their implementation.  Even-even better, it is designed to easily incorporate extensions so that any organization that uses CKAN can add your feature.

As part of the Master of Information Systems Management degree from Heinz College, all students participate in a client-driven group capstone project in their final semester.  Being a member of SUDS, I was delighted that my team was assigned to work with the City of Philadelphia, as every other group had a large corporate client.

Tim Wisniewski, Philadelphia’s Chief Data Officer, had several exciting ideas for projects.  With our end-of-semester time constraint in mind, we chose a CKAN extension that would streamline Philadelphia’s open data workflow.  (Some of his other proposals will be tackled by future MISM capstone teams!)

CKAN is wonderful, but does not allow for data dictionaries (or “metadata”) to be stored for each dataset. Philadelphia currently handles this by using a separate system to track the data dictionaries.  Most datasets contain a link to the metadata mixed in with the links to the data – and those links go to the metadata server.

What is metadata? If you’ve ever asked someone what column D in a spreadsheet represented, you have asked for metadata – it’s the information about the data.  “Column D is the number of clients impacted by the project described in Column A.  It should be a positive integer.”

pic2Our challenge: Learn about CKAN development and write an extension that allows native handling of data dictionaries.  Great documentation is available, but CKAN is a fairly complex system.  It uses Jinja for the frontend, Python on the backend, a PostgreSQL database, and many more technologies.  Luckily our team brought a diverse skillset to the project.

I’ll spare you the gory details, but we eventually got our extension working and tested.  Like CKAN itself, the extension is open-source, and we’ve been excited by the interest in it so far.  You can view our extension on GitHub.

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CKAN was a perfect project for us: Large enough to be complex and somewhat bewildering at first, but understandable enough to be able to deliver the final product.  It stretched our skills, but in a manageable way.  For individuals looking to push their web app development abilities, consider contributing to CKAN – sharpen your skills while contributing to the open data movement!

Matt Cleinman is a recent grad of the Heinz College MISM program (’15). While writing this post, he realized he never actually learned why the application is named CKAN.