Where people in prison come from:
The geography of mass incarceration in Pennsylvania
by Emily Widra and Benjamin Geffen
September 2022
Press release
One of the most important criminal legal system disparities in Pennsylvania has long been difficult to quantify: Which communities throughout the Commonwealth do incarcerated people come from? Anyone who lives in or works within heavily policed and incarcerated communities intuitively knows that certain neighborhoods disproportionately experience incarceration. But data have never been available to document how many people from each community are imprisoned with any real precision.1
But now, thanks to a redistricting reform that ensures incarcerated people are counted correctly in their home legislative districts, we can understand the geography of incarceration in Pennsylvania. Pennsylvania is one of over a dozen states that have ended prison gerrymandering (although Pennsylvania ended it only for the 2020 legislative redistricting process) and now count incarcerated people where they legally reside — at their home address — rather than in remote prison cells. This type of reform, as we often discuss, is crucial for ending the siphoning of political power from disproportionately Black and Latino communities, to pad out the mostly rural, predominantly white regions where prisons are located. And when reforms like Pennsylvania’s are implemented, they bring along a convenient side effect: In order to correctly represent each community’s population counts, states must collect detailed statewide data on where imprisoned people call home, which is otherwise impossible to access.
Using these redistricting data, we find that in Pennsylvania, incarcerated people come from all over the Commonwealth, and unsurprisingly, the largest number of imprisoned people are from the state’s most populous city: Philadelphia. However, what may be surprising, is that a handful of less populous and more rural counties in western Pennsylvania — like Venango, Jefferson, and Warren counties — have some of the highest imprisonment rates per 100,000 residents,2 indicating that people all over Pennsylvania are affected by the Commonwealth’s reliance on mass incarceration.
In addition to helping policymakers and advocates effectively bring reentry and diversion resources to these communities, these data have far-reaching implications. Around the country, high imprisonment rates are correlated with other community problems related to poverty, employment, education, and health. Researchers, scholars, advocates, and politicians can use the data in this report to advocate for bringing more resources to their communities.
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Incarcerated people come from all over Pennsylvania — but disproportionately from some places more than others
Our analysis of the state’s adjusted redistricting data shows both that incarcerated people come from every portion of the state and that some communities bear the heaviest burden from mass incarceration. The state reallocated almost two-thirds of the state prison population3 to addresses outside of the facility and the locations of those people serve as the basis of this report’s analysis.
In order to make apples-to-apples comparisons of the prevalence of incarceration between counties, cities and other communities of different sizes, this report uses imprisonment rates expressed as a number of people in prison per 100,000 residents.4 For the purposes of this analysis looking only at the numbers of people who were successfully reallocated to specific non-prison addresses in the state, Pennsylvania has an imprisonment rate of 206 per 100,000 residents.
County trends
Most broadly, we find that incarcerated people in Pennsylvania come from every corner of the Commonwealth: every single one of the 67 counties is missing a portion of its population to prisons. Unsurprisingly, the county with the highest number of imprisoned residents (7,019)5 is the most populous county, Philadelphia County (which is the entirety of the city of Philadelphia), with over 1.6 million residents. Philadelphia County also has the second-highest county imprisonment rate in the Commonwealth: 436 imprisoned people per 100,000 county residents.
But high incarceration is not just a problem for the most populous counties. Out of Pennsylvania’s top five highest imprisonment counties, three — Venango, Jefferson, and Warren — are rural counties in the western part of the state with significantly higher poverty rates than most of the state. Venango County, which has the highest county imprisonment rate in the state (452 per 100,000 county residents) has a population of just over 50,000. Venango also has a poverty rate higher than most Pennsylvania counties or the Commonwealth at large: 14% of Venango County residents live in poverty, compared to 11% of Pennsylvanians statewide. Jefferson County and Warren County also have imprisonment rates almost twice the statewide rate of 206 per 100,000: 398 and 331 per 100,000 county residents, respectively, as well as poverty rates higher than the state average, at 14% and 13% respectively. This fits with nationwide trends: people in prison tend to have been among the poorest people in the country before their incarceration.
In addition, Pennsylvania communities — and western Pennsylvania communities, in particular — have faced high rates of opioid use over the past decade.6 We know that people who use opioids are at heightened risk for arrest and criminal-legal system involvement. In fact, 21% of 2020 court commitments to Pennsylvania state prisons were for “narcotic drug offenses,” suggesting that communities with high rates of opioid use are particularly vulnerable to arrest, conviction, and imprisonment.7 Nationally, 20% of people in state prisons report that they have used heroin at some point, and half of people in state prisons meet criteria for a substance use disorder. Because we know people are at heightened risk for returning to drug use and overdosing upon release from prison, and that incarceration fuels the cycle of poverty, these findings suggest that poor, rural communities in western Pennsylvania are in need of focused resources and support to reduce the risk of criminal legal system involvement and break the cycle of poverty.
City trends
Philadelphia is home to more than 7,000 of the people in Pennsylvania state prisons. With an imprisonment rate of 436 people in state prison per 100,000 city residents, the city is disproportionately affected by incarceration: Philadelphia is home to 12% of the Commonwealth’s population, but 26% of the prison population hails from Philadelphia. Given that the criminal legal system unfairly targets communities of color — particularly Black communities — Philadelphia’s high imprisonment rate may be of no surprise, as the city’s population is 41% Black, while the statewide population is only 12% Black.
In terms of imprisonment rates — the number of people in state prisons per 100,000 city residents — a handful of less populous cities appear to be imprisoning large portions of their populations. The city of Chester — just a few miles southwest of Philadelphia along the Delaware River — has the highest citywide imprisonment rate in the Commonwealth: 1,181 per 100,000 city residents, with 380 of its 32,000 residents in state prison. As in Philadelphia, the disproportionate impact of the criminal legal system on communities of color is apparent in Chester, where the city population is predominantly Black (72%) (which is six times higher than the statewide average), and where Black people are disproportionately arrested (making up 79% of arrests by the Chester Police Department). In addition, the city has a poverty rate higher than the rate in nearby Philadelphia and three times higher than the statewide average. Poorer communities of color, like Chester, have experienced decades of systematic oppression and divestment by the public and private sectors, as well as a history of over-policing— leaving them particularly vulnerable to a modern-day reliance on mass incarceration.
Other cities with high imprisonment rates include the state capital of Harrisburg (1,145 per 100,000), Coatesville (964 per 100,000), and a number of cities in western Pennsylvania like Uniontown (972 per 100,000) and Oil City (856 per 100,000). One thing these four high-imprisonment cities have in common is an elevated poverty rate. In Oil City, almost 18% residents live in poverty, compared to the statewide poverty rate of 11%. The poverty rates in Coatesville, Uniontown, and Harrisburg are all more than double the statewide rate, with more than one-fifth of those city populations living below the poverty line. The high rates of imprisonment in these cities with particularly high poverty rates may not be surprising, given that poor people and their families and communities are disproportionately impacted by the criminal legal system.
On the other hand, in Pittsburgh — the second-largest city with the second highest number of people in prison (839 residents) — the citywide imprisonment rate (276 per 100,000) is actually lower than most Pennsylvania cities. Compared to Philadelphia, Pittsburgh has a larger white population (66% white compared to 39%) and a somewhat wealthier population with a higher median household income and a lower poverty rate. Even despite the lower overall city imprisonment rate in Pittsburgh, we still see the effect of over-policing communities of color: while Black people are only 39% of the city’s population, they make up 55% of arrests made by the Pittsburgh Police Department.
Neighborhood trends
Within cities and counties, imprisonment tends to be concentrated in a relatively small number of geographic areas and neighborhoods.
Philadelphia
Among the country’s 30 largest cities, Philadelphia is the second most racially segregated city in the nation (after Chicago). And research has shown that policing tends to target poor communities and neighborhoods composed of people of color, in particular, Black people. This is true in Philadelphia, where the city’s population is 41% Black, but 64% of the people arrested by the Philadelphia Police Department are Black.
While we do not have arrest or incarceration rates by income level, we do know that Philadelphia residents tend to be poorer than the rest of the Commonwealth: 23% of Philadelphia residents live in poverty, compared to 11% of all Pennsylvania residents. While poverty in Philadelphia is spread across the city, and not necessarily limited to a handful of neighborhoods, we know that poor Black and Hispanic or Latino people of all income levels are far more likely than whites to live in areas with high poverty rates.
With this in mind, it is not surprising to see that high imprisonment rate neighborhoods are scattered throughout the city. While people are incarcerated in state prison from every single Philadelphia city ZIP code,8 more than half of incarcerated Philadelphians come from just eleven of the city’s 46 ZIP codes: these eleven ZIP codes are home to 51% of the city’s imprisoned population, but only 26% of the city’s residents. The communities missing the greatest portion of their residents to prisons are concentrated in North Philadelphia: North Philadelphia — East, North Philadelphia — West, and Nicetown.9 Not only does Nicetown have one of the highest imprisonment rates in the city (943 per 100,000), but it also has the dubious distinction of being home to the most imprisoned Philadelphians (496 residents).
Historically, Philadelphia is one of the starkest examples of redlining in the country. In the 1930s, the federal government rated the “riskiness” of real estate investment in different neighborhoods, resulting in rating non-white neighborhoods as “hazardous” and beginning a cycle of disinvestment in these predominately Black and immigrant neighborhoods. A 2019 study of formerly redlined neighborhoods in over 100 cities found that these neighborhoods are lower-income and are more likely to be home to Black and Hispanic or Latino residents. The neighborhoods with the highest imprisonment rates in 2020 are also the neighborhoods that were “redlined” in the mid-20th century.
For example, the neighborhoods of North Philadelphia (East, West, and Nicetown) — with the highest imprisonment rates in the city — include regions of the city labeled “definitely declining” and “hazardous” for investment during redlining because of the predominantly Black population, as well as the growing community of immigrants — specifically Italian, German, and Jewish immigrants — in the late 1930s. Today, these three communities have high rates of poverty (between 36% and 44% of residents living in poverty compared to the citywide poverty rate of 23%) and are predominately non-white communities: North Philadelphia — West is 93% Black, North Philadelphia — East is 58% Black, and Nicetown is 52% Black (compared to the citywide population that is 41% Black).10
In West Philadelphia, the imprisonment rate (791 per 100,000) in the West Market neighborhood (ZIP code: 19139) is higher than the citywide average and higher than 42 other city ZIP codes. The West Market community was labeled “hazardous” during redlining because of the concentration of Black residents and Italian immigrants. West Market’s population was 85% Black in 2020 and the neighborhood had one of the lowest median household incomes in the city in 2019 (almost four times lower than the median household income in Center City — Society Hill). The poverty rate in West Market was 31%, compared to the 6% poverty rate in the mostly white community of Center City — Society Hill (ZIP code: 19106).11
Decades of systematic oppression and divestment from these poorer communities of color — which we know are overpoliced — have left these historically redlined communities, like West Philadelphia and North Philadelphia, particularly vulnerable to Pennsylvania’s modern-day reliance on mass incarceration.
Pittsburgh
In Pittsburgh, neighborhood imprisonment rates are correlated with the poverty status and racial demographics of the community. The neighborhood of Larimer is 82% Black and 42% below the poverty line. Regent Square — with no neighborhood residents in prison on Census Day 2020 — has a population that is 97% white and only 1% of residents live in poverty.
With 303,810 residents, Pittsburgh is the second-most-populous city in Pennsylvania, with roughly one-fifth the population of Philadelphia. It also has a lower imprisonment rate (276 per 100,000) than Philadelphia (436 per 100,000).
In many ways, this trend is to be expected, based on what we know about the effects of poverty and race on criminal-legal system involvement: Pittsburgh is somewhat wealthier than Philadelphia: 20% of Pittsburgh residents in poverty, compared to 23% Philadelphia residents in poverty. Pittsburgh is also a predominately white city: 66% of Pittsburgh residents are white, compared to 39% of Philadelphia residents who are white. But, just like the rest of the country, policing in Pittsburgh tends to target poor communities and neighborhoods composed of people of color, in particular, Black people: the city’s population is 23% Black, but 55% of the people arrested by the Pittsburgh Police Department are Black.
But what the residence data allow us to explore is an even more local analysis of neighborhood imprisonment rates. At this local level, it’s clear that some neighborhoods in Pittsburgh are much more vulnerable to imprisonment than others. More than half of everyone incarcerated from Pittsburgh, for example, come from just 15 of the city’s 90 neighborhoods. Eight neighborhoods are missing at least 1% of their residents to state prisons, and seven of the neighborhoods have imprisonment rates greater than 1,000 per 100,000, more than three times the citywide imprisonment rate.
In fact, of the ten neighborhoods with the highest imprisonment rates in the city, eight are majority-Black communities. In contrast, all six neighborhoods with zero people in state prison on the day of the 2020 Census are predominantly white. These differences in the Pittsburgh communities facing high and low imprisonment rates reflects the broader racial disparities in the criminal-legal system.
At the neighborhood level in Pittsburgh, research has shown that 76% of the 17 majority-Black neighborhoods and only 6% of the 50 predominantly white neighborhoods have poverty rates higher than 30%. Across the city, Black neighborhoods with high rates of poverty appear to bear the brunt of Pittsburgh’s imprisonment. Inevitably, citywide racial disparities in policing flow through the criminal-legal system and are reflected in the high imprisonment rates in poor, Black Pittsburgh neighborhoods. Among the 74 neighborhoods with at least 1,000 residents, the highest imprisonment rate is in the Beltzhoover neighborhood at 1,467 per 100,000. Notably, Beltzhoover is a predominantly Black neighborhood with almost 59% of residents in 2018 identifying as Black.12
While all communities are missing some of their members to prisons, in places where large numbers of adults — parents, workers, voters — are locked up, incarceration has a broader community impact. The large number of adults drained from a relatively small number of geographical areas seriously impacts the health and stability of the families and communities left behind.13
What are the differences between high- and low-incarceration communities?
Across the country, researchers have connected high local incarceration rates with a host of negative outcomes for the people who live there. In a prior analysis of where incarcerated people in Maryland are from, the Prison Policy Initiative found that Baltimore communities with high rates of incarceration were more likely to have high unemployment rates, long average commute times, low household income, a high percentage of residents with less than a high school diploma or GED, decreased life expectancy, high rates of vacant or abandoned properties, and higher rates of children with elevated blood-lead levels, compared to neighborhoods less impacted by incarceration.
Research has revealed similar correlations14 in communities around the country:
- Life expectancy: A 2021 analysis of New York State census tracts found that tracts with the highest incarceration rates had an average life expectancy more than two years shorter than tracts with the lowest incarceration rates, even when controlling for other population differences.15 And a 2019 analysis of counties across the country revealed that higher levels of incarceration are associated with both higher morbidity (poor or fair health) and mortality (shortened life expectancy).
- Community health: A nationwide study, published in 2019, found that rates of incarceration were associated with a more than 50% increase in drug-related deaths from county to county. And a 2018 study found that Black people living in Atlanta neighborhoods with high incarceration rates are more likely to have poor cardiometabolic health profiles.
An analysis of North Carolina data from 1995 to 2002 revealed that counties with increased incarceration rates had higher rates of both teenage pregnancy and sexually transmitted infections (STIs). A 2015 study of Atlanta also found that census tracts with higher rates of incarceration had higher rates of newly diagnosed STIs.16 - Mental health: A 2015 study found that people living in Detroit neighborhoods with high prison admission rates were more likely to be screened as having a current or lifetime major depressive disorder and generalized anxiety disorder.
- Exposure to environmental dangers: A 2021 study found that people who grew up in U.S. census tracts with higher levels of traffic-related air pollution and housing-derived lead risk were more likely to be incarcerated as adults, even when controlling for other factors.
In New York City, neighborhood incarceration rate is associated with asthma prevalence among adults. Similarly, in our 2020 analysis of New York City neighborhoods, we found higher rates of asthma among children in communities with high incarceration rates.17 - Education: In a 2020 Prison Policy Initiative analysis of incarcerated New Yorkers’ neighborhoods of origin, we found a strong correlation between neighborhood imprisonment rates and standardized test scores.18 And a 2017 report on incarceration in Worcester, Massachusetts, found that schools in the city’s high-incarceration neighborhoods tended to be lower-performing. What’s more, students in those neighborhoods faced more disciplinary infractions.
- Community resources and engagement: A 2018 study found that throughout the country, formerly incarcerated people (as well as all people who have been arrested or convicted of a crime) are more likely than their non-justice-involved counterparts to live in a census tract with low access to healthy food retailers. And the 2017 report on Worcester, Massachusetts, revealed that high-incarceration neighborhoods had lower voter turnout in municipal elections.
We already have this wealth of data showing that incarceration rates correlate with a variety of barriers and negative outcomes. The data in this report build on this work by helping identify which specific neighborhoods throughout Pennsylvania are systematically disadvantaged and left behind. Pennsylvania residents can use the data in this report to examine granular local-level and statewide correlations and choose to allocate needed resources to places hardest hit by incarceration.
Implications & uses of these data
These 10 data tables provided here have great potential for community advocacy and future research.
First and most obviously, these data can be used to determine the best locations for community-based programs that help prevent involvement with the criminal legal system, such as offices of neighborhood safety and mental health response teams that work independently from police departments. The data can also help guide reentry services (which are typically provided by nonprofit community organizations) to areas of Pennsylvania that need them most.
But even beyond the obvious need for reentry services and other programs to prevent criminal legal system involvement, our findings also point to geographic areas that deserve greater investment in programs and services that indirectly prevent criminal legal involvement or mitigate the harm of incarceration. After all, decades of research show that imprisonment leads to cascading collateral consequences, both for individuals and their loved ones. When large numbers of people disappear from a community, their absences are felt in countless ways. They leave behind loved ones, including children, who experience trauma, emotional distress, and financial strain. Simultaneously, the large numbers of people returning to these communities (since the vast majority of incarcerated people do return home) face a host of reentry challenges and collateral consequences of incarceration, including difficulty finding employment and a lack of housing. People impacted by the criminal legal system tend to have extremely diminished wealth accumulation. And those returning from prison and jail may carry back to their communities PTSD and other mental health issues from the trauma they’ve experienced and witnessed behind bars. Lastly, investing in core community resources to mitigate structural issues like poverty, such as housing and healthcare, will reduce vulnerabilities for criminal legal system contact.
And since we know place of origin correlates with so many other metrics of wellbeing, we can and should target these communities for support and resources beyond what we typically think of as interventions to prevent criminal legal system contact. In communities where the state or city has heavily invested in policing and incarceration (i.e. the high-incarceration neighborhoods we find in our analysis), our findings suggest that those resources would be better put toward reducing poverty and improving local health, education, and employment opportunities.
For example, we know that large numbers of children in high incarceration areas may be growing up with the trauma and lost resources that come along with having an incarcerated parent, and that these children are also more likely to experience incarceration. The information in this report can help with planning and targeting supports, resources, and programming designed to not only respond to the harms caused by incarceration, but disrupt the cycle of familial incarceration.
We invite community leaders, service providers, policymakers, and researchers to use these data to make further connections between mass incarceration and various outcomes, to better understand the impact of incarceration on their communities.
Methodology & data
This report capitalizes on the unique opportunity presented by Pennsylvania’s ending of prison gerrymandering for state legislative districts, which allows us to determine accurately for the first time where people incarcerated in state prisons come from. In this report’s linked datasets, we aggregate these data by a number of useful statewide geographies such as counties, state legislative districts, congressional districts, and for some citywide geographies such as neighborhoods in Philadelphia, Pittsburgh, and Allentown.
This section of the report discusses how we processed the data, some important context and limitations on that data, and some additional context about the geographies we have chosen to include in this report and appendices. The goal of this report is not to have the final word on the geographic concentration of incarceration, but to empower researchers and advocates — both inside and outside of the field of criminal justice research — to use our dataset for their own purposes. For example, if you are an expert on a particular kind of social disadvantage and have some data organized by county, zip code, elementary school district, or other breakdown and want to add imprisonment data to your dataset, we probably have exactly what you need in a prepared appendix described below.
This report and its data are one in a series of similar reports we are releasing in 2022, focusing on 13 states — California, Colorado, Connecticut, Delaware, Maryland, Montana, Nevada, New Jersey, New York, Pennsylvania, Rhode Island, Virginia, and Washington — which counted incarcerated people at home for redistricting purposes, and therefore also made this analysis possible. This report can also be seen as a template for other states because while not all states have ended prison gerrymandering, many departments of corrections already have near-complete home residence records in an electronic format. States that have not yet ended prison gerrymandering should be encouraged to continue improving their data collection, and to share the data (under appropriate privacy protections) so that similar analyses could be performed.
How we processed the data
In Pennsylvania, the Pennsylvania Department of Corrections provided home addresses of most people in state prisons on Census Day 2020 to the independent Legislative Reapportionment Commission, so that imprisoned people could be removed from the redistricting populations reported by the Census for the facilities’ locations and properly credit people to their home communities. The adjusted dataset was then made available for state and local officials to use to draw new legislative boundaries. As a side effect, this groundbreaking dataset allows researchers to talk in detail for the first time about where incarcerated people came from.
Creating the tables in this report required several steps which were expertly performed by Peter Horton at Redistricting Data Hub:
- Downloading Pennsylvania’s adjusted redistricting data, which contains the Commonwealth’s entire population, with the people incarcerated in state prisons reallocated to their home addresses (the state reallocated only people with sentences ending on or before April 1, 2030).
- Subtracting the Commonwealth’s redistricting data from the original Census Bureau P.L. 94-171 redistricting data, to produce a file that represented the number of incarcerated people the Commonwealth determined were from each census block statewide. Census blocks that showed a net gain of population following the reallocation were the Census blocks that incarcerated people were reallocated to, and the amount of that change was the number of people from that block who were incarcerated in a state prison on Census day. (For a different analysis that focused on both the net gains and net decreases in individual census blocks and then aggregated to counties and the final redistricting plans, see Peter Horton’s report for Redistricting Data Hub on Pennsylvania.)
- Aggregating these block-level counts of incarcerated people to each of the geography types available in the report. In cases where a census block containing an incarcerated person’s home address straddles the boundary between two geographies, the incarcerated population was applied to the geography that contained the largest portion of the census block’s area.
- Calculating imprisonment rates for each geography, by first calculating a corrected population that shows the Census 2020 population plus the number of incarcerated people from that geography; and then dividing the number of incarcerated people by the corrected total population, and then multiplying it by 100,000 to get an imprisonment rate per 100,000.
Important context and limitations on this data
Our analysis in this report documents the home addresses of 26,819 people in state prisons, which is less than the total state prison population of approximately 44,000.19 These numbers are different for a variety of reasons, including both important and minor policy choices made when ending prison gerrymandering and others are just the practical outcome of valiant efforts to improve federal Census data, or the process of repurposing that dataset for this entirely different project.
From the perspective of improving democracy in Pennsylvania, the Commonwealth’s reallocation efforts were successful, reducing both the unearned enhancement of political representation in prison-hosting areas and reducing the dilution of representation in the highest-incarceration districts. From the perspective of using that data to discuss the concentration of incarceration, some readers may want to be aware of some the reasons why our report discusses the home addresses of 26,819 people when they may be aware that the state prison system had more people on Census day:
- The Legislative Reapportionment Commission chose to exclude from the reallocation two groups of people: people serving sentences of life without parole — we know that in 2020, the Pennsylvania Department of Corrections reported 5,375 people serving sentences of life without parole, which accounted for about 12% of the state prison population — and people with more than 10 years left to serve on their sentences.
- Some people in Pennsylvania state prisons are from other states and therefore were not reallocated to homes in Pennsylvania.
- Some addresses were unknown or could not be located for the reallocation. For example, an address on file may be incomplete or may contain only the notation “homeless” which of course cannot be applied to a specific home census block.
- Anyone whose home address by coincidence happens to be in a census block that contains a correctional facility would have been properly reallocated for purposes of ending prison gerrymandering, but their presence at that location would not, because of how we created our dataset, be apparent in this report.
Similarly, this report doesn’t reflect the other groups of people incarcerated from particular communities who are not reflected in these data, because 20 they were:
- Incarcerated in a federal prison, because states do not have the power to require home address data from federal agencies.
- Incarcerated in another state’s prison system. States cannot require other states to share this information, and the fact that so many states are ending prison gerrymandering is too new of a phenomenon for them to have had the chance to enter into inter-state data sharing agreements.
- Incarcerated in a local jail, in Pennsylvania or elsewhere; because the Commonwealth’s effort to remedy prison gerrymandering was focused on state prisons.21
About the geographies
We’ve organized the data in this report around several popular geographies, as defined by the federal government, by the state, or by individual cities, with the idea that the reader can link our data to the wealth of existing social indicator data already available from other sources.
Unfortunately, the reader may desire data for a specific geography that we have not made available — for example, their own neighborhood, as they conceive of its boundaries. Often, there was not a readily accessible and official map that we could use that defined that boundary; so where the reader has this need, we urge the reader to look for other geographies in our datasets that can be easily adapted to their needs, either one that is similar enough to their preferred geography or by aggregating several smaller geographies together to match your preferred geography.
We also want to caution subsequent users of this data that some geographies change frequently and others change rarely, so they should note the vintage of the maps we used to produce each table. For example, county boundaries change very rarely, and when they do, it is often in extremely small ways. On the other hand, legislative districts may change frequently and significantly, so depending on your goals some specific tables may be more or less applicable for your future use.
Finally, readers should note that occasionally the incarcerated numbers in our tables for some geographies will not sum precisely to the total 26,819 home addresses used in this report. That discrepancy arises because of how census blocks — the basic building block of legislative districts — nest or fail to nest within geographies drawn by agencies other than the Census Bureau.
Footnotes
Villanova researchers Brianna Remster and Rory Kramer published a crucial report in 2018 on the political consequences of counting incarcerated Pennsylvanians at the location of the prison rather than at their home addresses. Their analysis combined a variety of data sources to estimate how many incarcerated people came from each census block in Pennsylvania. This report builds on their groundbreaking work and is based on actual, not estimated, residence locations, thanks to previously unavailable data made public for the first time in 2021 by the Pennsylvania Legislative Reapportionment Commission.
Across the country, criminal legal system data are often poorly tracked, meaning researchers must cobble together information from different sources. But by using complete data from state redistricting committees, this report (and a series of other state reports that the Prison Policy Initiative developed with state partners) are uniquely comprehensive and up-to-date. The series includes two previous reports on Maryland (published in 2015, in collaboration with the Justice Policy Institute) and New York (published in 2020, in collaboration with VOCAL-NY), and our newest reports on California, Colorado, Connecticut, Delaware, Maryland, Montana, Nevada, New Jersey, New York, Virginia, and Washington.
While these reports are the first to use redistricting data to provide detailed, local-level data on where incarcerated people come from statewide, other organizations have previously published reports that focused on individual cities or that provided data across fewer types of geographic areas. For example, the Justice Mapping Center had a project that showed residence data for people admitted to or released from state prisons in a given year for almost two dozen states. That project made those states’ annual admission and release data available at the zip code and census tract levels, most recently mapping 2008-2010 data. Separately, it also mapped the residences of people admitted to state prisons from New York City down to the block level using 2009 data.
Another resource (particularly helpful for states that are not included in our series of reports) is Vera Institute for Justice’s Incarceration Trends project, which maps prison incarceration rates for 40 states at the county level, based on county of commitment (meaning where individuals were convicted and committed to serve a sentence, which is often but not necessarily where they lived). ↩Imprisonment rates per 100,000 are a useful tool for comparison between different geographic regions with varying population sizes. For example, using a rate per 100,000 allows us to compare the frequency of imprisonment between the most populous Pennsylvania counties like Philadelphia County — with over 1.6 million residents — to the smaller, less populated counties, like any one of the 36 Pennsylvania counties with fewer than 100,000 residents. ↩
In the legislative redistricting process, the Legislative Reapportionment Commission counted over 26,800 people in state prisons in their home communities. However, people serving life sentences and people with sentences ending after April 1, 2030 were counted at the location of the prison they are incarcerated in. The data presented in this report is based on the 26,800 people the Commonwealth successfully counted in their home communities, while there were actually 44,000 people incarcerated in Pennsylvania state prisons on Census Day 2020. ↩
As explained in the methodology, this report’s analysis is based on the number of people in state prisons who were reallocated to their home communities as part of the Legislative Reapportionment Commission’s decision eliminating prison gerrymandering in the 2020 legislative redistricting cycle. The incarceration rates used throughout this report are necessary for making apples-to-apples comparisons of imprisonment between specific communities and the state as a whole. For the purposes of comparing incarceration in Pennsylvania with that of other states, other more common metrics would be more useful. For these other uses, we would recommend using other numbers for the statewide incarceration rate, likely either the 308 per 100,000 published by the Bureau of Justice Statistics in Prisoners in 2020 for the number of people in state prison per 100,000 residents, or our more holistic number of 659 per 100,000 residents used in States of Incarceration: The Global Context 2021 that includes people in state prisons, federal prisons, local jails, youth confinement, and all other forms of incarceration. ↩
It is important to note again that this is the number of Philadelphia residents reallocated by the Legislative Reapportionment Commission, and it omits people serving life sentences or terms of incarceration ending after April 1, 2030. At the end of 2019, almost 25% of the state prison population — or more than 11,000 people — were convicted and sentenced in Philadelphia, suggesting that the figure this report uses of 7,019 likely undercounts by several thousand the actual number of Philadelphians imprisoned across the state. ↩
In 2020, Pennsylvania had the fourth highest number of opioid deaths across all 50 states (5,168 deaths). Because actual opioid use cannot be measured accurately on a large scale, opioid deaths and mortality rates are used to approximate opioid use trends. ↩
Not all people imprisoned for “narcotic drug offenses” have an opioid use disorder or even use drugs, but these data do show just how prevalent opioid-involvement is among people who are in the criminal-legal system. We also know that people with opioid dependence are arrested on a broad spectrum of charges and not just for drug offenses. ↩
For the purposes of this report, we use 5-digit ZIP code tabulation areas in the city of Philadelphia to approximate neighborhoods, although we acknowledge that neighborhoods and communities do not always fit neatly within ZIP codes. Other analyses of Census data in Philadelphia have also used ZIP codes to represent neighborhoods, including the 2019 State of the City report from the Pew Charitable Trusts. ↩
Some of the North Philadelphia neighborhoods included (at least in part) in these ZIP codes include: Tioga, Hunting Park, Hartranft, Upper Kensington, West Kensington, Strawberry Mansion, and Allegheny West. ↩
Poverty rates by 5-digit ZIP code tabulation area available in the 2020 Census ACS 5-Year Estimate Table DP05: Age Demographic and Housing Estimates. ↩
Population by race by 5-digit ZIP code tabulation area available in the 2020 Census ACS 5-Year Estimate Table DP05: Age Demographic and Housing Estimates. ↩
In addition, Beltzhoover has the third highest percentage of Hispanic or Latino residents in the city, and we know that Hispanic and Latino people are slightly overrepresented in Pennsylvania state prison systems: 9% of people in Pennsylvania Department of Corrections custody in 2020 were Hispanic or Latino, but the state population is only 8% Hispanic or Latino. ↩
These impacts of incarceration on families and communities include higher rates of disease and infant mortality, housing instability, and financial burdens related to having an incarcerated loved one. For more detailed information on how incarceration impacts families and communities, see On life support: Public health in the age of mass incarceration from the Vera Institute of Justice. ↩
These various correlative findings are once again in line with previous research on health disparities across communities, which have been linked to neighborhood factors such as income inequality, exposure to violence, and environmental hazards that disproportionately affect communities of color. Public health experts consider community-level factors such as these — including incarceration — “social determinants of health.” To counteract these problems, they suggest taking a broad approach, addressing the “upstream” economic and social disparities through policy reforms, as well as by increasing access to services and supports, such as improving access to clinical health care. ↩
We also know that people who have been incarcerated have a shorter life expectancy than people who have not. ↩
There are many additional studies linking incarceration rates and high community rates of STIs, including gonorrhea and chlamydia in North Carolina. ↩
Asthma prevalence has been used as a tool to measure population health in both sociological and public health research because it is easily correlated with environmental factors, like air quality and triggers (i.e. second hand smoke, mold, dust, cockroaches, dust mites), access to appropriate healthcare, and healthcare literacy. See the American Lung Association’s Public Policy Position for a literature review of the relevant public health research. ↩
Again, this finding is consistent with previous research on the relationship between education and imprisonment rates. We previously reported that the high school educations of over half of all formerly incarcerated people were cut short. This is in line with earlier studies showing that people in prison have markedly lower educational attainment, literacy, and numeracy than the general public, and are more likely to have learning disabilities. We also know there are relationships between parental incarceration and educational performance. ↩
The Pennsylvania Department of Corrections reported a population of 44,230 on March 31, 2020. ↩
This list of groups of people who could not be counted at home is yet another set of reasons why the U.S. Census Bureau is the ideal agency to end prison gerrymandering: it is the only party with the ability to provide a complete solution, and it can do this work far more efficiently than the states can. ↩
Unlike people in prisons who are likely to be incarcerated in a state prison outside their home county, people in local jails are more likely to be jailed in their home county. ↩
Acknowledgments
We would like to thank the Redistricting Data Hub, particularly Peter Horton, for providing valuable technical expertise and the key data in the appendix tables. Redistricting Data Hub’s assistance processing the redistricting data and connecting us with other demographic data enabled us to produce and distribute these reports faster and more affordably than would otherwise have been possible.
The Public Interest Law Center would like to thank all of its allies in the effort to end prison gerrymandering in Pennsylvania, including the NAACP Pennsylvania State Conference, Fair Districts PA, the League of Women Voters of Pennsylvania, Common Cause Pennsylvania, NAACP Legal Defense and Educational Fund, Hogan Lovells US LLP, and Brianna Remster and Rory Kramer of Villanova University.
About the organizations
The non-profit, non-partisan Prison Policy Initiative produces cutting-edge research that exposes the broader harm of mass criminalization and sparks advocacy campaigns that create a more just society. In 2002, the organization launched the national movement against prison gerrymandering with the publication of Importing Constituents: Prisoners and Political Clout in New York. This report demonstrated how using Census Bureau counts of incarcerated people as residents of the prison location dilutes the votes of state residents who do not live next to prisons, in violation of the state constitutional definition of residence. Since then, California is one of over a dozen states that have used Prison Policy Initiative’s research to end prison gerrymandering.
The Public Interest Law Center uses high-impact legal strategies to advance the civil, social, and economic rights of communities in the Philadelphia region facing discrimination, inequality, and poverty. The Law Center uses litigation, community education, advocacy, and organizing to secure access to fundamental resources and services. Through the Jeffrey Golan & Frances Vilella-Velez Voting Justice Project, the Law Center protects every citizen’s right to vote by modernizing Pennsylvania’s election system and challenging discriminatory barriers to the ballot box. The Law Center has been at the forefront of voting-rights litigation and advocacy in Pennsylvania, with successes including defeating Pennsylvania’s strict photo identification requirement for voting; winning the nation’s first-ever partisan gerrymandering lawsuit that resulted in the use of a new statewide map for congressional elections; and helping to bring about the end of prison-based gerrymandering for Pennsylvania’s legislative districts.
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