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[Projection of prisoner numbers].

Rainer Metz, Werner Sohn

    Archiv Fur Kriminologie
    |October 1, 2015
    PubMed
    Summary

    Understanding prison occupancy rates is vital for judicial administration. Statistical modeling, particularly time-lagged analysis, helps predict prisoner numbers by identifying key influencing factors.

    Area of Science:

    • Criminology
    • Statistics
    • Judicial Administration

    Context:

    • Prison occupancy rates are critical for national judicial administration and penal facility planning.
    • Predicting prisoner numbers is complex due to interdependent factors and the failure of traditional criminal policy explanations.
    • Understanding past trends is essential for future penal system development.

    Purpose:

    • To identify and statistically model factors influencing past prisoner number development.
    • To explore the utility of time-lagged statistical modeling for predicting prison populations.
    • To address the complication of differing dynamics between national and foreign prisoner populations.

    Summary:

    • Statistical and time series analyses were employed to identify criminological factors influencing prisoner numbers.

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  • Time-lagged predictive modeling proved effective in forecasting prisoner population changes, using Hesse's data as a case study.
  • Separate models are needed to account for distinct trends in national versus foreign inmate populations.

    • Impact:

    • Provides a data-driven approach to understanding and forecasting prison populations.
    • Informs judicial administration and penal facility planning with more accurate predictive tools.
    • Highlights the need for nuanced modeling that considers diverse prisoner demographics.