One of the governor’s goals related to public safety is the Department of Corrections will reduce its state correction population by 5% by 2020. DOC overall total population directly drives the Department’s budget. The baseline for the goal is the total population on June 30, 2015. On June 30, 2015, the Pennsylvania Department of Corrections overall population was 50,366.

This dataset contains the total number of state corrections population in the Department’s custody at the end of each month, including those in prison, in contracted county jails, in community phases of the State Intermediate Punishment (SIP) program, in Parole Violator Centers (PVCs), and on temporary transfer to other jurisdictions.

DOC publishes a Monthly Population Report to the DOC Website (www.cor.pa.gov). The information published to the website includes the data set and breakdown of populations in each institution.

Three different measures of recidivism (rearrest, reincarceration, and overall recidivism) have been used by the DOC in a recent report to further explore the effects of recidivism on the criminal justice system. The DOC defines rearrest as: “the first instance of arrest after the individual is released from the custody of the DOC.” The DOC defines reincarceration as: “the first instance of returning to the custody of the DOC after the individual is released from the DOC.” The DOC defines overall recidivism as: “the first instance of any type of rearrest or reincarceration after the individual is released from the DOC.”

- The 3-year rearrest rates have been consistently higher than the 3-year reincarceration rates.
- The overall recidivism rates have remained relatively stable.
- From 2000 to 2013, rearrest rates appear to grow consistently before trending downward in recent years (e.g. 3-year rate has been below 50.0% in 4 of the last 5 years).
- The 2013 3-year reincarceration rate was down slightly at 43.7%, still lower than all the 3-year rates prior to 2008.
- The 2013 3-year overall recidivism rate dropped to 60.9%. This was the second lowest overall recidivism rate in the past 13 years (with 2011 being lowest at 59.9%)
- The 2015 1-year rates were all up slightly.

These statistics are based on a cohort of inmates.

This dataset is quarterly data by county, including inmate admissions, average Texas Christian University Drug Screen II (TCU) score, number and percent of inmate admissions who identify opioids as top three drug of choice, inmates with a substance abuse problem, and number and percent who used opioids in the year prior to admission.
This data is available starting with the first Quarter of Calendar Year 2016.

This is a state aggregated filtered view from the original dataset describes as follows: quarterly data by county, including inmate admissions, average Texas Christian University Drug Screen II (TCU) score, number and percent of inmate admissions who identify opioids as top three drug of choice, inmates with a substance abuse problem, and number and percent who used opioids in the year prior to admission.
This data is available starting with the first Quarter of Calendar Year 2016.

This is a monthly Pennsylvania Board of Probation and Parole reentrant employment status by county starting in the calendar year of January 2018 - current. Updates will be loaded monthly as they become available.
Reentrant is a former inmate who is on parole. Former referred to as a parolee.

This data is the monthly number of inmates who are incarcerated and have a minimum sentence of 2 years or less by county who are participating in vocational training, the average number of hours in vocational training, and the number of inmates with vocational certifications

The database was constructed from using the source “Sapphire Emar”. The raw data was condensed to remove any duplicate data that expressed the following: data rows with the same inmate name who had multiple of the same medications or multiple of the same medication type. Then the data sets were sorted by admission dates. Then grouped by quarters or years or both. After that, data was sorted by various descriptor columns: Age Range, Ethnicity, Gender and Drug type.
What is the story the data is telling? The data shows a significantly higher admission rate for Caucasians and Males. The data also shows the age range of 30-34 consistently has the highest percent admission rate. The data also shows a high percentage of inmates receive Vivitrol/Revia as opposed to Suboxone.
What to realize about this data? Although Males and Caucasians have a higher admission rate it is worth noting that this does not take into the account of how many inmates based on gender or ethnicity there are/were in the DOC at a given time. Regardless of Year the Inmate age peaks at 30-34 suggesting they are the most likely to seek treatment.

The database was constructed from using the source “Sapphire Emar”. The raw data was condensed to remove any duplicate data that expressed the following: data rows with the same inmate name who had multiple of the same medications or multiple of the same medication type. Then the data sets were sorted by admission dates. Then grouped by quarters or years or both. After that, data was sorted by various descriptor columns: Age Range, Ethnicity, Gender and Drug type.
What is the story the data is telling? The data shows a significantly higher admission rate for Caucasians and Males. The data also shows the age range of 30-34 consistently has the highest percent admission rate. The data also shows a high percentage of inmates receive Vivitrol/Revia as opposed to Suboxone.
What to realize about this data? Although Males and Caucasians have a higher admission rate it is worth noting that this does not take into the account of how many inmates based on gender or ethnicity there are/were in the DOC at a given time. Regardless of Year the Inmate age peaks at 30-34 suggesting they are the most likely to seek treatment.

The database was constructed from using the source “Sapphire Emar”. The raw data was condensed to remove any duplicate data that expressed the following: data rows with the same inmate name who had multiple of the same medications or multiple of the same medication type. Then the data sets were sorted by admission dates. Then grouped by quarters or years or both. After that, data was sorted by various descriptor columns: Age Range, Ethnicity, Gender and Drug type.
What is the story the data is telling? The data shows a significantly higher admission rate for Caucasians and Males. The data also shows the age range of 30-34 consistently has the highest percent admission rate. The data also shows a high percentage of inmates receive Vivitrol/Revia as opposed to Suboxone.
What to realize about this data? Although Males and Caucasians have a higher admission rate it is worth noting that this does not take into the account of how many inmates based on gender or ethnicity there are/were in the DOC at a given time. Regardless of Year the Inmate age peaks at 30-34 suggesting they are the most likely to seek treatment.