Simulating Virginia’s State Responsible Confined …b.3cdn.net/crjustice/3fdb14329195f15346_jkm6ibyqu.pdf · Simulating Virginia’s State Responsible Confined Forecast ... How

Simulating Virginia’s State Responsible Confined Forecast

Tama S. Celi, Ph.D.Research & Forecast Manager

Virginia Department of [email protected]

mailto:[email protected]

Forecasting in Virginia

• Appropriations Act requires a Consensus process that is overseen by the Secretary of Public Safety.

• Participants: Department of Corrections, Department of Juvenile Justice, Department of Criminal Justice Services, Compensation Board, Department of Planning and Budget, Joint Legislative Audit and Review Committee, State Police, VA Supreme Court and the Criminal Sentencing Commission.

• Consensus Process includes:– Technical Committee; technical experts from associated agencies review and vote on

competing models.– Liaison Committee; middle managers from associated agencies review and make

recommendations based on any impending internal policy or procedural changes.– Policy Committee; policy makers and agency heads review and make adjustments based on

impending policy changes.• Major Topics

– Trends– Admissions– Confined– Accuracy

Virginia Department of CorrectionsResearch & Forecast Unit

October 20122

Why Change?Need for More Information and transparency• Changing policy• New data systems• Reversals in trends

Issues with Old Model• Based on the Parole system (abolished in VA in 1995) using information from released

offenders (i.e. percent of sentence served)• Assumed normal distributions among the Most Serious Offense (MSO) groups

– Assigned Minimum, Mean and Maximum Sentence (in months) for each offense group– Example: Assault MSO (Min=12.0, Mean=51.9, Max=684.0) but the Median was 34.0 months

• 71% of Assaults have a sentence <52.0 months (MSOs are not normally distributed)

– Impact: Old Model was keeping offenders too long, resulting in erroneously high forecasts

• When no female data was available to put into the model, called for male data to be used

• When no offenders existed for a particular group, assumed 1000 and used the last year’s profile information

Virginia Department of Corrections Research & Forecast Unit October 2012 3

Policy Changes

• Parole was abolished in Virginia in 1995.

• As inmates sentenced under Truth‐in‐Sentencing (TIS) have become the majority of the DOC confined population (~84%), simulation modeling has become a more powerful forecasting tool than ever before.

• The surety of Length of Stay (LOS) of TIS sentences provides concrete information upon which to build the model. Release dates are calculated and known when the inmate is admitted to the DOC and these dates are available for use in forecast.

Virginia Department of Corrections Research & Forecast Unit October 2012

4

• Strengths– Uses Estimated Release Date (ERD) of the ~37,000 current inmates

– Uses the most current sentencing/LOS information for future Admissions

• Weaknesses– If the Admissions forecasts are “OFF”; the simulation model will be similarly “OFF”

– As with other methods, if sentencing policy or practice for future Admissions or Release change substantially, the simulation model will not capture that change until future years


VA DOC Simul8 Model

Day 0: Existing Confined Population is loaded into the model• Number is End of Month (EOM) population of Day 0• Profiles are based on existing Confined Population

Special Release Calculations base on Probability: As they are imported into the model, each inmate has a date for Death and Other* Release calculated. If PE, a Discretionary Release Date is also calculated.

• Death:– Males – 3 year average rates based on age and race– Females – 3 year average rate applied randomly (no significant patterns identified)

• Other:– Males – most recent year’s release rates applied randomly by crime type (V/PPO/D)– Females – most recent year’s release rate applied randomly

• Discretionary Parole: Parole Board Grant Rates by gender and crime type for the latest year are used since discretionary release rates change from year to year

How the VA DOC Simul8 Model Works

*Includes Release by Court Order, Pardon/Commutation, etc.


Confined inmates are released on their Expected Release Date(ERD) starting on Day 1 unless:

• IF Death Date < ERD, Other Date and Discretionary Date; THEN Released on Death Date

• IF Other Date < ERD, Discretionary Date and Death Date; THEN Released on Other Date

• IF Discretionary Date < ERD, Other Release Date and Death Date; THEN Released on Discretionary Date



How the VA DOC Simul8 Model Works• Admissions begin to enter on Day 1• NCC forecasts are distributed into more specific offense groups

• Total Admissions = NCC + Parole Violators • The model randomly chooses among similar recent admissions to derive a profile for future new Admissions

• ERD is calculated based on Total Expected Length of Stay (TELOS)* of profile assigned to each Admission


*TELOS = (Expected Release Date – New Commit Date) for NCC;TELOS = (Expected Release Date – Revocation Date) for Parole Violators


• As Admissions enter, each inmate has a date for Death and Other Release calculated. If PE, a Discretionary Release Date is also calculated.

• Admissions are released on calculated ERD unless:– IF Death Date < ERD, Other Date and Discretionary Date; THEN Release

on Death Date

– IF Other Date < ERD, Discretionary Date and Death Date; THEN Release on Other Date

– IF Discretionary Date < ERD, Other Release Date and Death Date; THEN Release on Discretionary Date


Expected Release Dates*

• For Current TIS Inmates, Good Time Expected Release Date (ERD) is used

• For Current PE Inmates, Mandatory Parole Release Date is used

• For Future Admissions, TELOS for most recent Admissions is used to calculate Expected Release Date

*Date an inmate is expected to be released based on the current rate at which he is earning good time credits


VADOC Simul8 Model


Testing the New Model

• In order to be able to test the model, in 2010, we re‐ran the 2007 Forecast with the simulation model using 3 scenarios:– The actual admissions for the known period supplemented with the 2007 admissions projections

– The adopted 2007 admissions projections– The actual admissions for the known period supplemented with the 2010 admissions projections


Comparison of ForecastsActual EOM

Adopted 2007

Forecast

Adopted 2008

ForecastSimlu8 Actual/ 2007 Forecast

Simul8 2007 Forecast

Simul8 Actual/ 2010 Forecast

FY2008 38,826 39,347 38,825 39,285 38,845

FY2009 38,387 40,305 39,621 38,280 39,942 38,270

FY2010 37,724 41,292 40,563 37,790 40,497 37,645

FY2011 37,503 42,399 41,552 39,685 41,247 37,343

FY2012 37,159 43,559 42,527 41,094 42,075 37,277

FY2013 44,744 43,526 42,511 43,175 37,556

Avg Growth Rate -1.1% 2.6% 2.4% 1.9% 1.9% -0.7%

Average Monthly Absolute Error

Adopted 2007 Forecast


Simlu8 Actual/ 2007 Forecast


Simul8 Actual/ 2010 Forecast

FY2008 172 64 142 70

FY2009 1,309 614 73 1,103 87

FY2010 2,808 2,173 112 2,283 118

FY2011 4,390 3,580 1,319 3,339 79

FY2012 5,858 4,833 3,297 4,522 150

Avg Annual Error 2,907 2,800 973 2,279 101


Comparison of Forecasts

35,000

37,000

39,000

41,000

43,000

45,000

Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13

Actual EOM



Simlu8 Actual/2007 Forecast


Simul8 Actual/2010 Forecast


Comparison of Forecasts


30,000

32,000

34,000

36,000

38,000

40,000

42,000

44,000

46,000

48,000

50,000

Jan‐00Jul‐00Jan‐01Jul‐01Jan‐02Jul‐02Jan‐03Jul‐03Jan‐04Jul‐04Jan‐05Jul‐05Jan‐06Jul‐06Jan‐07Jul‐07Jan‐08Jul‐08Jan‐09Jul‐09Jan‐10Jul‐10Jan‐11Jul‐11Jan‐12Jul‐12Jan‐13Jul‐13Jan‐14Jul‐14Jan‐15Jul‐15Jan‐16Jul‐16Jan‐17Jul‐17Jan‐18

SR Pop

ulation

EOM

Actual EOM

2004 Forecast (Prophet)



2007 Forecast (Prophet/DPB)

2008 Forecast (DPB)

2009 Forecast (DPB)

2010 Forecast (Simul8)

2011 Forecast (Simul8/DPB)

2012 Forecast 1 (Simul8)



Tools for Development & Validation

• Customized output is exported into Excel to provide information on admissions, releases by type, etc.– Releases forecasted by regular releases, mandatory parole, discretionary parole, death and other

– Admissions are counted– Results split by gender and offense types

• Start with running back in time to validate, then move the start date to the relevant forecast period


Producing Forecasts

• Running the model– Produces result of single run through the chosen forecast period

– Used for development, validation and testing

• Trials– Multiple runs that produce confidence interval and mean– This is what is done to produce a forecast– Trials can take considerable time


Other Uses

• Subpopulation Forecasts: use to produce a Sexually Violent Predator (SVP) Predicate Release Forecast

• Legislative Impact Analysis: Can adjust populations to reflect potential policy changes

• Potential Future Uses– Tool for planning types of beds needed– Tool to determine resource needs– Tool to develop reentry planning– Expand forecasts to predict Community Corrections population– Develop micro‐models for resource allocation, staff

development and operational needs


Documents

Simulating Virginia’s State Responsible Confined …b.3cdn.net/crjustice/3fdb14329195f15346_jkm6ibyqu.pdf · Simulating Virginia’s State Responsible Confined Forecast ... How