Data clustering and rule abduction to facilitate crime hot spot prediction

Ian D. Wilson; Jonathan Corcoran; Owen M. Lewis; Andrew Ware

doi:10.1007/3-540-45493-4_80

Data clustering and rule abduction to facilitate crime hot spot prediction

Ian D. Wilson, Jonathan Corcoran, Owen M. Lewis, Andrew Ware

Cyfadran Cyfrifiadureg, Peirianneg a Gwyddoniaeth

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

5 Dyfyniadau (Scopus)

Crynodeb

Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.

Iaith wreiddiol	Saesneg
Teitl	Computational Intelligence
Is-deitl	Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings
Golygyddion	Bernd Reusch
Cyhoeddwr	Springer
Tudalennau	807-821
Nifer y tudalennau	15
ISBN (Argraffiad)	3540427325, 9783540427322
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/3-540-45493-4_80
Statws	Cyhoeddwyd - 1 Ion 2001
Digwyddiad	7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001 - Dortmund, Yr Almaen Hyd: 1 Hyd 2001 → 3 Hyd 2001

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol	2206 LNCS
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

Cynhadledd

Cynhadledd	7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001
Gwlad/Tiriogaeth	Yr Almaen
Dinas	Dortmund
Cyfnod	1/10/01 → 3/10/01

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

Mynediad at Ddogfen

10.1007/3-540-45493-4_80

Dyfynnu hyn

Wilson, I. D., Corcoran, J., Lewis, O. M., & Ware, A. (2001). Data clustering and rule abduction to facilitate crime hot spot prediction. Yn B. Reusch (Gol.), Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings (tt. 807-821). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 2206 LNCS). Springer. https://doi.org/10.1007/3-540-45493-4_80

Wilson, Ian D. ; Corcoran, Jonathan ; Lewis, Owen M. et al. / Data clustering and rule abduction to facilitate crime hot spot prediction. Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings. Gol. / Bernd Reusch. Springer, 2001. tt. 807-821 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{60f2ad146f454ed5934b84e14b82e26c,

title = "Data clustering and rule abduction to facilitate crime hot spot prediction",

abstract = "Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.",

author = "Wilson, {Ian D.} and Jonathan Corcoran and Lewis, {Owen M.} and Andrew Ware",

year = "2001",

month = jan,

day = "1",

doi = "10.1007/3-540-45493-4_80",

language = "English",

isbn = "3540427325",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "807--821",

editor = "Bernd Reusch",

booktitle = "Computational Intelligence",

address = "Germany",

note = "7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001 ; Conference date: 01-10-2001 Through 03-10-2001",

}

Wilson, ID, Corcoran, J, Lewis, OM & Ware, A 2001, Data clustering and rule abduction to facilitate crime hot spot prediction. yn B Reusch (gol.), Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), cyfrol. 2206 LNCS, Springer, tt. 807-821, 7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001, Dortmund, Yr Almaen, 1/10/01. https://doi.org/10.1007/3-540-45493-4_80

Data clustering and rule abduction to facilitate crime hot spot prediction. / Wilson, Ian D.; Corcoran, Jonathan; Lewis, Owen M. et al.
Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings. gol. / Bernd Reusch. Springer, 2001. t. 807-821 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 2206 LNCS).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad i gynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Data clustering and rule abduction to facilitate crime hot spot prediction

AU - Wilson, Ian D.

AU - Corcoran, Jonathan

AU - Lewis, Owen M.

AU - Ware, Andrew

PY - 2001/1/1

Y1 - 2001/1/1

N2 - Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.

AB - Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.

U2 - 10.1007/3-540-45493-4_80

DO - 10.1007/3-540-45493-4_80

M3 - Conference contribution

AN - SCOPUS:33846649283

SN - 3540427325

SN - 9783540427322

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 807

EP - 821

BT - Computational Intelligence

A2 - Reusch, Bernd

PB - Springer

T2 - 7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001

Y2 - 1 October 2001 through 3 October 2001

ER -

Wilson ID, Corcoran J, Lewis OM, Ware A. Data clustering and rule abduction to facilitate crime hot spot prediction. Yn Reusch B, golygydd, Computational Intelligence: Theory and Applications - International Conference, 7th Fuzzy Days, Proceedings. Springer. 2001. t. 807-821. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45493-4_80

Data clustering and rule abduction to facilitate crime hot spot prediction

Crynodeb

Cyfres gyhoeddiadau

Cynhadledd

NDC y CU

Mynediad at Ddogfen

Ôl bys

Dyfynnu hyn