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Quantifying the Simultaneous Effect ...

Ek, Alfieri Daniel.

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Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends./
Author:	Ek, Alfieri Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	34 p.
Notes:	Source: Masters Abstracts International, Volume: 82-07.
Contained By:	Masters Abstracts International82-07.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28157239
ISBN:	9798557028677

Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends.
Ek, Alfieri Daniel.

Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 34 p.

Source: Masters Abstracts International, Volume: 82-07.

Thesis (M.S.)--University of Arkansas, 2020.

This item must not be sold to any third party vendors.

Proper allocation of law enforcement agencies falls under the umbrella of risk terrain modeling (Caplan et al., 2011, 2015; Drawve, 2016) that primarily focuses on crime prediction and prevention by spatially aggregating response and predictor variables of interest. Although mental health incidents demand resource allocation from law enforcement agencies and the city, relatively less emphasis has been placed on building spatial models for mental health incidents events. Analyzing spatial mental health events in Little Rock, AR over 2015 to 2018, we found evidence of spatial heterogeneity via Moran's I statistic. A spatial modeling framework is then built using generalized linear models, spatial regression models and a tree based method, in particular, Poisson regression, spatial Durbin error model, Manski model and Random Forest. The insights obtained from these different models are presented here along with their relative predictive performances. These inferential tools have the potential to aid both law enforcement agencies and the city in properly allocating resources required for suchevents.

ISBN: 9798557028677Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Crime prediction

Quantifying the Simultaneous Effect of Socio-Economic Predictors and Build Environment on Spatial Crime Trends.
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500 $a Source: Masters Abstracts International, Volume: 82-07.
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520 $a Proper allocation of law enforcement agencies falls under the umbrella of risk terrain modeling (Caplan et al., 2011, 2015; Drawve, 2016) that primarily focuses on crime prediction and prevention by spatially aggregating response and predictor variables of interest. Although mental health incidents demand resource allocation from law enforcement agencies and the city, relatively less emphasis has been placed on building spatial models for mental health incidents events. Analyzing spatial mental health events in Little Rock, AR over 2015 to 2018, we found evidence of spatial heterogeneity via Moran's I statistic. A spatial modeling framework is then built using generalized linear models, spatial regression models and a tree based method, in particular, Poisson regression, spatial Durbin error model, Manski model and Random Forest. The insights obtained from these different models are presented here along with their relative predictive performances. These inferential tools have the potential to aid both law enforcement agencies and the city in properly allocating resources required for suchevents.
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