A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns

Diego Mayorga; Miguel A. Melgarejo; Nelson Obregon

doi:10.1109/FUZZ-IEEE.2016.7737761

A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns

Diego Mayorga
, Miguel A. Melgarejo
, Nelson Obregon

Departamento de Ingeniería Civil

Universidad Distrital Francisco José de Caldas
Pontificia Universidad Javeriana

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

This paper presents a method for analyzing patterns of criminal activity that occur in space and time. The method uses the fuzzy C-means algorithm to cluster criminal events in space. In addition, a cluster reorganization algorithm is included to preserve the order of fuzzy partitions from one time step analysis to another. Order preservation is possible since crime forms relatively stable patterns due to the fixed shape of urban spaces and routine activities of people. The method provides a novel way to analyze criminal directionality, since it generates time series from clustering. A sample database of robberies in San Francisco, USA, is used to test the algorithm. Results show that criminal patterns might be tracked in a simple way.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	738-744
Número de páginas	7
ISBN (versión digital)	9781509006250
DOI	https://doi.org/10.1109/FUZZ-IEEE.2016.7737761
Estado	Publicada - 07 nov 2016
Evento	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 - Vancouver, Canadá Duración: 24 jul 2016 → 29 jul 2016

Serie de la publicación

Nombre	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

Conferencia

Conferencia	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
País/Territorio	Canadá
Ciudad	Vancouver
Período	24/07/16 → 29/07/16

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 16: Paz, justicia e instituciones sólidas

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10.1109/FUZZ-IEEE.2016.7737761

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Mayorga, D., Melgarejo, M. A., & Obregon, N. (2016). A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. En 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (pp. 738-744). Artículo 7737761 (2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2016.7737761

@inproceedings{ac63a9056ab9489f9baddb16be3bb030,

title = "A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns",

abstract = "This paper presents a method for analyzing patterns of criminal activity that occur in space and time. The method uses the fuzzy C-means algorithm to cluster criminal events in space. In addition, a cluster reorganization algorithm is included to preserve the order of fuzzy partitions from one time step analysis to another. Order preservation is possible since crime forms relatively stable patterns due to the fixed shape of urban spaces and routine activities of people. The method provides a novel way to analyze criminal directionality, since it generates time series from clustering. A sample database of robberies in San Francisco, USA, is used to test the algorithm. Results show that criminal patterns might be tracked in a simple way.",

keywords = "Clustering, Crime, Crime analysis, Crime pattern theory, Fuzzy clustering",

author = "Diego Mayorga and Melgarejo, \{Miguel A.\} and Nelson Obregon",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

year = "2016",

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series = "2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Mayorga, D, Melgarejo, MA & Obregon, N 2016, A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. En 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016., 7737761, 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, Institute of Electrical and Electronics Engineers Inc., pp. 738-744, 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, Vancouver, Canadá, 24/07/16. https://doi.org/10.1109/FUZZ-IEEE.2016.7737761

A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. / Mayorga, Diego; Melgarejo, Miguel A.; Obregon, Nelson.
2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 738-744 7737761 (2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016).

Producción: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns

AU - Mayorga, Diego

AU - Melgarejo, Miguel A.

AU - Obregon, Nelson

PY - 2016/11/7

Y1 - 2016/11/7

N2 - This paper presents a method for analyzing patterns of criminal activity that occur in space and time. The method uses the fuzzy C-means algorithm to cluster criminal events in space. In addition, a cluster reorganization algorithm is included to preserve the order of fuzzy partitions from one time step analysis to another. Order preservation is possible since crime forms relatively stable patterns due to the fixed shape of urban spaces and routine activities of people. The method provides a novel way to analyze criminal directionality, since it generates time series from clustering. A sample database of robberies in San Francisco, USA, is used to test the algorithm. Results show that criminal patterns might be tracked in a simple way.

AB - This paper presents a method for analyzing patterns of criminal activity that occur in space and time. The method uses the fuzzy C-means algorithm to cluster criminal events in space. In addition, a cluster reorganization algorithm is included to preserve the order of fuzzy partitions from one time step analysis to another. Order preservation is possible since crime forms relatively stable patterns due to the fixed shape of urban spaces and routine activities of people. The method provides a novel way to analyze criminal directionality, since it generates time series from clustering. A sample database of robberies in San Francisco, USA, is used to test the algorithm. Results show that criminal patterns might be tracked in a simple way.

KW - Clustering

KW - Crime

KW - Crime analysis

KW - Crime pattern theory

KW - Fuzzy clustering

UR - https://www.scopus.com/pages/publications/85006711825

U2 - 10.1109/FUZZ-IEEE.2016.7737761

DO - 10.1109/FUZZ-IEEE.2016.7737761

M3 - Conference contribution

AN - SCOPUS:85006711825

T3 - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

SP - 738

EP - 744

BT - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

Y2 - 24 July 2016 through 29 July 2016

ER -

Mayorga D, Melgarejo MA, Obregon N. A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. En 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 738-744. 7737761. (2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016). doi: 10.1109/FUZZ-IEEE.2016.7737761

A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns

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