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Predicting Inmate Overcrowding to Im...

Alameri, Mayed Ali,

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Predicting Inmate Overcrowding to Improve Facility Management /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Inmate Overcrowding to Improve Facility Management // Mayed Ali Alameri.
Author:	Alameri, Mayed Ali,
Description:	1 electronic resource (38 pages)
Notes:	Source: Masters Abstracts International, Volume: 87-06.
Contained By:	Masters Abstracts International87-06.
Subject:	Law enforcement. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32401769
ISBN:	9798270240653

Predicting Inmate Overcrowding to Improve Facility Management /
Alameri, Mayed Ali,

Predicting Inmate Overcrowding to Improve Facility Management /Mayed Ali Alameri. - 1 electronic resource (38 pages)

Source: Masters Abstracts International, Volume: 87-06.

Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which geographical characteristics are a strong proxy. The results present a demonstration of scalable interpretable forecasting system, which allows transition to proactive, data-driven strategic planning and ensure safety and efficiency in the facilities.

English

ISBN: 9798270240653Subjects--Topical Terms:

607408
Law enforcement.
Subjects--Index Terms:

Machine learning

Predicting Inmate Overcrowding to Improve Facility Management /
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245 1 0 $a Predicting Inmate Overcrowding to Improve Facility Management / $c Mayed Ali Alameri.
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500 $a Source: Masters Abstracts International, Volume: 87-06.
500 $a Advisors: Modak, Sanjay Committee members: Ibrahim, Ayman.
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520 $a Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which geographical characteristics are a strong proxy. The results present a demonstration of scalable interpretable forecasting system, which allows transition to proactive, data-driven strategic planning and ensure safety and efficiency in the facilities.
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650 4 $a Law enforcement. $3 607408
650 4 $a Law. $3 600858
650 4 $a Criminology. $3 533274
653 $a Machine learning
653 $a Inmate overcrowding
653 $a Criminal justice
653 $a Forecasting
653 $a Predictive analytics
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690 $a 0398
690 $a 0454
690 $a 0627
710 2 $a Rochester Institute of Technology. $b Professional Studies: Data Analytics. $e degree granting institution. $3 3814649
720 1 $a Modak, Sanjay $e degree supervisor.
773 0 $t Masters Abstracts International $g 87-06.
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