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

Department of Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English
Title of host publication	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	738-744
Number of pages	7
ISBN (Electronic)	9781509006250
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2016.7737761
State	Published - 07 Nov 2016
Event	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

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

Conference

Conference	2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Clustering
Crime
Crime analysis
Crime pattern theory
Fuzzy clustering

Access to Document

10.1109/FUZZ-IEEE.2016.7737761

Cite this

Mayorga, D., Melgarejo, M. A., & Obregon, N. (2016). A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. In 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (pp. 738-744). Article 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

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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",

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Mayorga, D, Melgarejo, MA & Obregon, N 2016, A fuzzy clustering based method for the spatiotemporal analysis of criminal patterns. in 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, Canada, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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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. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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