國立虎尾科技大學 |

Social Network Analysis in Predictive Policing = Concepts, Models and Methods /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Social Network Analysis in Predictive Policing/ by Mohammad A. Tayebi, Uwe Glässer.
Reminder of title:	Concepts, Models and Methods /
Author:	Tayebi, Mohammad A.
other author:	Glässer, Uwe.
Description:	XI, 133 p. 43 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-319-41492-8
ISBN:	9783319414928

Social Network Analysis in Predictive Policing = Concepts, Models and Methods /
Tayebi, Mohammad A.

Social Network Analysis in Predictive PolicingConcepts, Models and Methods /[electronic resource] :by Mohammad A. Tayebi, Uwe Glässer. - 1st ed. 2016. - XI, 133 p. 43 illus. in color.online resource. - Lecture Notes in Social Networks,2190-5428. - Lecture Notes in Social Networks,.

Introduction -- Social Network Analysis in Predictive Policing -- Structure of Co-offending Networks -- Organized Crime Group Detection -- Suspect Investigation -- Co-offence Prediction -- Personalized Crime Location Prediction -- Concluding remarks -- References.

This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

ISBN: 9783319414928

Standard No.: 10.1007/978-3-319-41492-8doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Social Network Analysis in Predictive Policing = Concepts, Models and Methods /
LDR:03051nam a22004215i 4500 001 982399
003 DE-He213
005 20200630081247.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319414928 $9 978-3-319-41492-8
024 7 $a 10.1007/978-3-319-41492-8 $2 doi
035 $a 978-3-319-41492-8
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Tayebi, Mohammad A. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1114498
245 1 0 $a Social Network Analysis in Predictive Policing $h [electronic resource] : $b Concepts, Models and Methods / $c by Mohammad A. Tayebi, Uwe Glässer.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XI, 133 p. 43 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Social Networks, $x 2190-5428
505 0 $a Introduction -- Social Network Analysis in Predictive Policing -- Structure of Co-offending Networks -- Organized Crime Group Detection -- Suspect Investigation -- Co-offence Prediction -- Personalized Crime Location Prediction -- Concluding remarks -- References.
520 $a This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.
650 0 $a Data mining. $3 528622
650 0 $a Police. $3 802138
650 0 $a Physics. $3 564049
650 0 $a Computer security. $3 557122
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Policing. $3 1105100
650 2 4 $a Applications of Graph Theory and Complex Networks. $3 1113468
650 2 4 $a Systems and Data Security. $3 677062
700 1 $a Glässer, Uwe. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1274515
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319414911
776 0 8 $i Printed edition: $z 9783319414935
776 0 8 $i Printed edition: $z 9783319823683
830 0 $a Lecture Notes in Social Networks, $x 2190-5428 $3 1258143
856 4 0 $u https://doi.org/10.1007/978-3-319-41492-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)