Introduction

The original inspiration for my decision to look into the state of women’s incarceration for my thesis (and thus this project) came from the book Data Feminism by Catherine D’Ignazio and Lauren Klein. I wanted to look into an issue where gendered data gaps presented themselves and there was room for a data feminist approach when tackling the problem. In the end, this led me to looking at how a better, gender-responsive definition of reentry success that goes beyond recidivism alone and an assessment of the spatialization of reentry-related resources might be precursors for a better predictor of reentry success than sentencing/risk algorithms which often reinforce bias and discrimination. However, this is not where the project began.

Challenges and Insights

This project changed shape quite a bit over the course of the semester for several reasons, a few of which reflected the very core of the issues which led me to the topic of women’s incarceration in the first place.

First, much of the available and accessible data on incarceration is not disaggregated by gender in a meaningful way or in a way that is conducive to mapping. For example, one of my original goals for this project was to map gender-specific incarceration rates for New York City, but as of this point I’ve still been unable to do so due my inability to locate gender-specific geographic incarceration rate data. On the other hand, I’ve been able to find some NYPD data that’s disaggregated by gender, such as jail admissions datasets, but these datasets lack important information necessary for further spatial analysis (such as what borough or NTA each person being admitted lives in, etc.) and alone are only enough to provide raw counts of individuals belonging to a single moment in time.

The second issue I encountered was a lack of data transparency and availability in general, particularly when I wanted to extend my analysis beyond NYC into Kansas City. At one point in the semester in order to stay aligned with the direction of my thesis, I wanted to shift my focus to look at the state of women’s incarceration in Kansas City, Missouri, and thus I needed to look at the carceral landscape from a data perspective. From the very start of that effort I knew the challenges I would face were going to be far more than I could tackle in the space of half of a semester, from the lack of open and available data to the hesitancy of anyone in a position of power to speak to the data transparency issues because of the election year. In the end, this experience gave me insight into how behind some states and cities in the U.S. are when it comes to having open, available data and just how critical this is for addressing problems that we know exist on a national scale but that we don’t have the specific data to “prove” on a local level. In other words, the problems that women under carceral control in New York face are almost certainly faced by women under carceral control in Missouri (and probably to an even greater extent because New York is an overall progressive state which has seen relative improvement to their carceral statistics over the past few decades whereas the same cannot be said for Missouri), but activists might have a harder time making the case for the issue in Missouri without the necessary data. Although no fruitful data analysis came of this detour, there was valuable insight to be gained regardless.

Reentry and Recidivism

The incredibly spatialized nature of incarceration is well-documented and easily observable when mapped. Also well-documented are the risk factors that tend to accompany high incarceration rates, both spatially and socioeconomically, as well as the trends and circumstances that seem to impact how likely someone is to reoffend after release (recidivism). Recidivism has become the dominant measure of success upon reentry into society for the previously incarcerated; in other words, whether or not someone is viewed as successful post-incarceration is often judged primarily, if not entirely, based on whether or not they reoffend. One of the ways this is most clearly demonstrated is in the use of Risk Assessment tools or algorithms, which are being used more and more in the criminal-legal system to predict whether someone is likely to do something like reoffend. These algorithms are used at multiple points in the criminal-legal process and for purposes ranging from assisting in determining whether someone is eligible for bail or pretrial release to the length and severity of sentencing and post-release supervision. These algorithms are based on the same narrow, black and white frameworks that have led to the common definition of success upon reentry to be synonymous with recidivism rates, not to mention the fact that these algorithms are known to produce harmful, biased outcomes. For example, one of the most commonly used risk assessment tools has been found to systemically overclassify women in higher risk groupings (Hamilton, 2019).

This narrow definition of success upon reentry which guides risk assessment tools and algorithms ignores the nuanced reality of reentry post-incarceration, particularly for women, as well as the spatial factors that might play a role in one’s ability to reintegrate effectively into their community.

Gender, Reentry, and Success Beyond Recidivism

Gender has a significant impact on an individual’s experiences at nearly every point in the carceral system and this is no different when it comes to reentry. For example:

“With regard to health-related needs of reentering persons, clear gender differences appear both in the type of issues that are most prevalent and in the type of treatment that is most effective.” The Harvard University Institute of Politics Criminal Justice Policy Group, 2019.

Given the need for a better definition of success upon reentry in general, but particularly for women, I began an effort to map the resources that could be associated with a definition of reentry success that extends well beyond recidivism. The dimensions of this three-pronged definition of reentry-related success is visualized in the table below which has been adapted from: Buck Willison, J. (2023). Measuring Reentry Success Beyond Recidivism. U.S. Department of Justice, Bureau of Justice Assistance.

With this definition of recidivism in mind, I’ve begun the process of mapping sites that serve as the physical manifestations of the measures for successful reentry, or places where individuals could seek out the resources that would be key to successful reentry, overlaid atop a basemap of incarceration rates by NTA. This is also in an attempt to investigate whether, like incarceration itself, successful reentry is spatialized based on the resources available within individuals’ communities that they return to, since reestablishing one’s place within their community is known to be essential for well-being post-release. I’ve started by mapping essential sites like hospitals and mental health treatment facilities, which I knew were going to be present throughout the city regardless of incarceration rate, and then am shifting to resources that are more specific to meeting post-incarceration needs (such as substance-use disorder treatment facilities).

I’ve also mapped the NYCHA housing developments atop the incarceration rates because of a study I came across during this research process which found much higher incarceration rates in census tracts with more NYCHA developments. I think this could be an interesting thing to keep in mind as I build on my analysis of the spatialization of successful reentry because this correlation between incarceration and NYCHA tracts might have something to do with excess surveillance and policing, which could also impact the likelihood of previously incarcerated individuals having more interactions with police, etc.

Mental Health Treatment Facilities

Hospitals and Clinics

Substance-Use Disorder Treatment Facilities

Future Work

I plan to continue building on this map, adding additional, more successful reentry-specific resources to continue investigating the possibility of the spatialization of successful reentry based on the distribution of resources.

Data and References: https://www.prisonpolicy.org/origin/ny/2020/nyc_nta.html

https://nationalreentryresourcecenter.org/sites/default/files/inline-files/Look%20Beyond%20Recidivism_March%2029%202023.pdf

Buck Willison, J. (2023). Measuring Reentry Success Beyond Recidivism. U.S. Department of Justice, Bureau of Justice Assistance.

Hamilton M. The sexist algorithm. Behav Sci Law. 2019 Mar;37(2):145-157. doi: 10.1002/bsl.2406. Epub 2019 Mar 31. PMID: 30931534.

J. Holder, I. Calaff, B. Maricque, V.C. Tran, Concentrated incarceration and the public-housing-to-prison pipeline in New York City neighborhoods, Proc. Natl. Acad. Sci. U.S.A. 119 (36) e2123201119,https://doi.org/10.1073/pnas.2123201119 (2022).

Link(s) to full project:

• Link 1 - felt.com/map/Incarceration-Rates-by-NTA-sWfz8UfQTcS9Bp65piKy1sC?loc=40.7711,-73.9863,11.69z&share=1