Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Black Swan Shootings: A Model for Pr...
~
D'anna, Matthew.
Linked to FindBook
Google Book
Amazon
博客來
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings./
Author:
D'anna, Matthew.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:
259 p.
Notes:
Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:
Dissertations Abstracts International82-06B.
Subject:
Criminology. -
Online resource:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27995753
ISBN:
9798698590750
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings.
D'anna, Matthew.
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 259 p.
Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Thesis (Ph.D.)--George Mason University, 2020.
This item must not be sold to any third party vendors.
Since the late 1990s, mass shootings in the United States, and in particular public mass shootings with high victim counts, have increased in frequency at an alarming and largely unprecedented rate. Most academic studies on mass shootings are descriptive and offender-centric, offering few tangible opportunities for predicting future mass violence. This study takes a different approach. This study develops a county-level spatial threat assessment for identifying locations at high-risk for experiencing or producing 'Black Swan shootings', defined here as an attack involving a perpetrator(s) using firearms to kill or injure a significantly large number of innocent or unwitting people, chosen intentionally or at random. Counties are evaluated for their risk of experiencing these attacks or producing attack perpetrators based on community-level measures for social contagion, public safety, demographics, mental health and substance abuse, and weapons availability. Using data from 18 different sources on mass shootings, 44 events since 1998 are identified. Based on statistical anomaly detection for extreme casualty counts across all mass shootings since 2013, Black Swan shootings are events with either eight killed, 13 wounded, or 15 total casualties. The analysis of these events indicates clear contagion effects over space and time: attacks occur in 17 distinct clusters of counties and most of the time occur within one year of the prior attack, with heightened risk in the 35 days immediately following an attack. Using annual county-level data from 1998 to 2018, results from t tests, Cohen's D effect sizes, and Mann-Whitney U tests indicate that communities which experience Black Swan Shootings or produce their perpetrators have statistically significant higher levels of violence, denser populations, more racial diversity, higher percentages of females, and higher counts of firearm laws compared to areas without such attacks. The spatial threat assessment uses a logistic regression model based on these most significant social factors. Retroactively, each year this model identifies less than 1% of the country as 'high-risk.' The model is deemed analytically viable when the metric score for the accuracy and precision of the count and location of Black Swan shootings in a given year is greater than 50%; this occurs in ten different years, all occurring since 2006. This includes nine specific bullseyes, where the model was an exact match for the county and year of either an attack or the offender residence of a Black Swan Shooting. The model's performance is evaluated, with a discussion on the opportunities for operationalizing these findings to inform on future attacks.
ISBN: 9798698590750Subjects--Topical Terms:
533274
Criminology.
Subjects--Index Terms:
Black Swan
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings.
LDR
:04008nmm a2200445 4500
001
2278373
005
20210628075011.5
008
220723s2020 ||||||||||||||||| ||eng d
020
$a
9798698590750
035
$a
(MiAaPQ)AAI27995753
035
$a
AAI27995753
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
D'anna, Matthew.
$0
(orcid)0000-0002-3552-5857
$3
3556751
245
1 0
$a
Black Swan Shootings: A Model for Predicting the Worst of the Worst Mass Shootings.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2020
300
$a
259 p.
500
$a
Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
500
$a
Advisor: Koper, Christopher S.
502
$a
Thesis (Ph.D.)--George Mason University, 2020.
506
$a
This item must not be sold to any third party vendors.
520
$a
Since the late 1990s, mass shootings in the United States, and in particular public mass shootings with high victim counts, have increased in frequency at an alarming and largely unprecedented rate. Most academic studies on mass shootings are descriptive and offender-centric, offering few tangible opportunities for predicting future mass violence. This study takes a different approach. This study develops a county-level spatial threat assessment for identifying locations at high-risk for experiencing or producing 'Black Swan shootings', defined here as an attack involving a perpetrator(s) using firearms to kill or injure a significantly large number of innocent or unwitting people, chosen intentionally or at random. Counties are evaluated for their risk of experiencing these attacks or producing attack perpetrators based on community-level measures for social contagion, public safety, demographics, mental health and substance abuse, and weapons availability. Using data from 18 different sources on mass shootings, 44 events since 1998 are identified. Based on statistical anomaly detection for extreme casualty counts across all mass shootings since 2013, Black Swan shootings are events with either eight killed, 13 wounded, or 15 total casualties. The analysis of these events indicates clear contagion effects over space and time: attacks occur in 17 distinct clusters of counties and most of the time occur within one year of the prior attack, with heightened risk in the 35 days immediately following an attack. Using annual county-level data from 1998 to 2018, results from t tests, Cohen's D effect sizes, and Mann-Whitney U tests indicate that communities which experience Black Swan Shootings or produce their perpetrators have statistically significant higher levels of violence, denser populations, more racial diversity, higher percentages of females, and higher counts of firearm laws compared to areas without such attacks. The spatial threat assessment uses a logistic regression model based on these most significant social factors. Retroactively, each year this model identifies less than 1% of the country as 'high-risk.' The model is deemed analytically viable when the metric score for the accuracy and precision of the count and location of Black Swan shootings in a given year is greater than 50%; this occurs in ten different years, all occurring since 2006. This includes nine specific bullseyes, where the model was an exact match for the county and year of either an attack or the offender residence of a Black Swan Shooting. The model's performance is evaluated, with a discussion on the opportunities for operationalizing these findings to inform on future attacks.
590
$a
School code: 0883.
650
4
$a
Criminology.
$3
533274
650
4
$a
Geographic information science.
$3
3432445
650
4
$a
Public administration.
$3
531287
650
4
$a
Sociology.
$3
516174
650
4
$a
Law.
$3
600858
650
4
$a
Social research.
$3
2122687
650
4
$a
Public policy.
$3
532803
653
$a
Black Swan
653
$a
Firearm laws
653
$a
Mass shooting
653
$a
Mass violence
653
$a
Prediction
653
$a
United States
653
$a
Future attacks
690
$a
0627
690
$a
0370
690
$a
0398
690
$a
0626
690
$a
0344
690
$a
0630
690
$a
0617
710
2
$a
George Mason University.
$b
Criminology, Law and Society.
$3
2105131
773
0
$t
Dissertations Abstracts International
$g
82-06B.
790
$a
0883
791
$a
Ph.D.
792
$a
2020
793
$a
English
856
4 0
$u
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27995753
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9430106
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login