A CLUSTERING-BASED APPROACH FOR IDENTIFYING GROUPS OF MUNICIPALITIES TO SUPPORT THE DIRECTION OF PUBLIC SECURITY POLICIES

Costa, Jefferson Carlos de Oliveira Ribeiro; Silva, Maísa Mendonça

doi:10.1590/0101-7438.2022.042.00257930

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ARTICLES • Pesqui. Oper. 42 • 2022 • https://doi.org/10.1590/0101-7438.2022.042.00257930 copy

A CLUSTERING-BASED APPROACH FOR IDENTIFYING GROUPS OF MUNICIPALITIES TO SUPPORT THE DIRECTION OF PUBLIC SECURITY POLICIES

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The direction of public policies plays an important role in society as a whole, especially in security, which, in addition to being considered a necessity for every citizen, is constitutionally guaranteed. This study presents the use of an unsupervised learning approach for the establishment of clusters among the municipalities in the State of Pernambuco, Brazil, considering some types of representative crimes, aiming to direct actions to prevent and fight crime in order to support policy makers. The k-means algorithm was used as the main tool in the study, using the software R 3.6.1, and recommendations for actions were directed to each of the obtained clusters. To demonstrate the direction, the grouping with the parameter k = 26 was used, referring to the State Security Integration Areas. The results show that the use of a clustering approach for the municipalities provides greater effectiveness in directing actions to combat and prevent crime, given that the municipalities that have the greatest similarities are grouped in the same cluster.

Keywords:
cluster; public security; K-means

Authors, Year	Description
Chapagain et al. (2022CHAPAGAIN P, TIMALSINA A, BHANDARI M & CHITRAKAR R. 2022. Intrusion Detection Based on PCA with Improved K-means. ICEEE International Conference on Electrical and Electronics Engineering, 894: 13-27.)	In this study, the clustering approach based on PCA with k-means is proposed for intrusion detection in a system that detects malicious activities and issues alerts.
Das & Das (2019DAS P & DAS AK. 2019. Graph-based clustering of extracted paraphrases for labelling crime reports. Knowledge-Based Systems, 179: 55-76.)	The authors propose a grouping technique based on graphs to discover crime report labels based on paraphrases extracted from large bodies of unidentified crimes.
Das et al. (2019DAS P, DAS AK, NAYAK J, PELUSI D & DING W. 2019. Group incremental adaptive clustering based on neural network and rough set theory or crime report categorization. Neurocomputing, doi: https://doi.org/10.1016/j.neucom.2019.10.109. https://doi.org/https://doi.org/10.1016/... )	The authors propose an incremental adaptive methodology in a crime reporting group, adapted to provide updated cluster sets. In addition, the study makes comparisons with clustering algorithms to express their effectiveness and statistical significance.
Khan et al. (2019KHAN JR, SAEED M, SIDDIQUI FA, MAHMOOD N & UL ARIFEEN Q. 2019. Predictive Policing: A Machine Learning Approach to Predict and Control Crimes in Metropolitan Cities, Journal of Information in Communication Technology,3(1): 17-26.)	The authors propose a model for the predictive policing system for street crimes in the Karachi-Pakistan region using the k-means tool and Bayesian methods.
Farias et al. (2018FARIAS AMG, CINTRA ME & FELIX AC. 2018. Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 26(3): 429-452.)	This work presents different models related to public security. These models are based on clustering algorithms, on the analysis of formal concept techniques and on the analysis of crime record data collected in Mossoro, Rio Grande do Norte, Brazil.
Wang et al (2018WANG S, WANG X, YE P, YUAN Y, LIU S & WANG F. 2018. Parallel Crime Scene Analysis Based on ACP Approach. IEEE Transactions on Computational Social Systems, 5(1): 244-255.)	They propose a method of tied clues to obtain refined results regarding the phenomenon known in the analysis of crimes as the quasi-repetition effect, adopting a data science perspective, combining correlation coefficient, hierarchical cluster and mining of frequency patterns in a specific order.
Saltos & Cocea (2017SALTOS G & COCEA M. 2017. An Exploration of Crime Prediction Using Data Mining on Open Data. International Journal of Information Technology & Decision Making , 16(5): 1155-1181.)	In this paper, models to predict the frequency of several types of crimes by the LSOA code (an administrative system of areas used by the police in the UK) and the frequency of crimes related to anti-social behavior are presented. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees.
Borg & Boldt (2016)BOLT & BOLDT M. 2016. Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information. International Journal of Information Technology & Decision Making, 15(1): 23-42.	The study investigates the use of clustering algorithms to group similar crime reports based on combined characteristics from the structured form.
Curman et al. (2015CURMAN ASN, ANDRESEN MA & BRANTINGHAM PJ. 2015. Crime and Place: A Longitudinal Examination of Street Segment Patterns in Vancouver. BC. Journal of Quantitative Criminology, 31: 127-147.)	The authors conduct a longitudinal analysis of a 16-year data set using the street segment as the unit of analysis, highlighting the trends in the crimes, and using the group-based trajectory model with the k-means cluster analysis technique.
Tayal et al. (2014TAYAL DK, JAIN A, ARORA S, AGARWAL S, GUPTA T & TYAGI N. 2014. Crime detection and criminal identification in India using data mining techniques. AI & Society, 30(1): 117-127.)	The authors propose an approach for the design and implementation of crime detection and criminal identification for Indian cities using data mining techniques.

Crime	Description
C1	Trade of illegal substances
C2	Act of intentionally killing another person
C3	An offence to a person’s bodily integrity or health
C4	Crime with intent in the previous act and guilt in the subsequent fact
C5	Crime in which there is deceit in killing the victim to subtract something
C6	Appropriation of a vehicle in which the victim is not present
C7	Appropriation of a vehicle through violence or threat
C8	Crime aimed especially at bank branches
C9	Crime that occurs in the transfer of goods
C10	Robbery to commercial establishments, in general
C11	Home invasion and theft

Principal Component	Proportion (%)	Accumulated Proportion (%)
PC1	70.46	70.46
PC2	10.11	80.57
PC3	9.36	89.93
PC4	5.70	95.63
PC5	2.58	98.21
PC6	0.67	98.88
PC7	0.39	99.27
PC8	0.33	99.60
PC9	0.20	99.79
PC10	0.12	99.91
PC11	0.08	100.00

Territory	ISA	ISA Nomenclature
Capital	1	Santo Amaro
	2	Espinheiro
	3	Boa Viagem
	4	Várzea
	5	Apipucos
Metropolitan Region	6	Jaboatão dos Guararapes
	7	Olinda
	8	Paulista
	9	São Lourenço da Mata
	10	Cabo de Santo Agostinho
North Forest Zone	11	Nazaré da Mata
North Forest Zone	16	Limoeiro
South Forest Zone	12	Vitória de Santo Antão
South Forest Zone	13	Palmares
Agreste 01	14	Caruaru
Agreste 01	17	Santa Cruz do Capibaribe
Agreste 02	15	Belo Jardim
Agreste 02	18	Garanhuns
Sertão 01	19	Arcoverde
	20	Afogados da Ingazeira
	21	Serra Talhada
	22	Floresta
Sertão 02	23	Salgueiro
	24	Ouricuri
	25	Cabrobó
	26	Petrolina

Crime	Actions
C1	Investing in educational programs
	Promoting continuous action to dismantle criminal organizations
	Investing in technologies to monitor drug plantations
	Establishing a state intelligence division to combat drug trade
	Deploying police crackdowns
C2	Implementing a plan to combat crimes against life
	Deploying monitoring cameras in specific locations
	Requalifying structures or expanding protection programs to groups that are highly vulnerable to violence
	Strengthening investigative skills of police officers
C3	Implementing a plan to combat crimes against life
	Creating social projects
	Increasing the number of police rounds
	Requalifying structures or expanding protection programs to groups that are highly vulnerable to violence
C4	Implementing a plan to combat crimes against life
	Gradually increasing the number of police officers
	Capacity building through courses on conflict mediation and humanized care
	Support and qualification courses for socially vulnerable groups
C5	Conducting police operations where violence incidence is high
	Reducing bureaucracy and strengthening investigative skills of police officers
	Training professionals on investigation, interview and interrogation techniques
	Deploying monitoring cameras on strategic routes
C6	Expanding information exchange and system sharing among states
	Deploying/expanding the video surveillance and license plate tracking system
	Expanding the use of information technology in vehicle monitoring
	Defining vehicle verification operations in strategic locations
C7	Establishing a task force in vehicle theft investigation
	Deploying (or investing) in police stations to combat vehicle theft
	Expanding information exchange and system sharing among states
	Deploying/expanding the video surveillance and license plate tracking system
C8	Investing in risk management together with the Brazilian Federation of Banks Determining intelligence actions with a multidisciplinary team
C8	Investing in image sharing systems from bank security cameras with law enforcement authorities
C9	Promoting continuous actions to dismantle criminal organizations responsible for cargo theft
	Expanding inspection on highways
	Investing in specialized personnel, such as tax auditors
C10	Investigating and dismantling gangs practicing thefts
	Periodically increasing the coordination of patrols in strategic areas
	Promoting advisory and partnership actions with traders from the regions
C11	Expanding camera monitoring systems
	Identifying the regions with the highest incidence and conduct patrols
	Performing actions in partnership with city halls to identify the lighting need in strategic points

Cluster	Crime	Cluster	Crime
1	C2	14	C11
2	C1	15	C1
3	C1	16	C6
4	C2	17	C6
5	C6	18	C1
6	C6	19	C6
7	C2	20	C10
8	C9	21	C1
9	C2	22	C6
10	C6	23	C2
11	C1	24	C6
12	C1	25	C1
13	C2	26	C2

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[1] *Corresponding author

Cluster	Crime	Cluster	Crime
1	C2	14	C11
2	C1	15	C1
3	C1	16	C6
4	C2	17	C6
5	C6	18	C1
6	C6	19	C6
7	C2	20	C10
8	C9	21	C1
9	C2	22	C6
10	C6	23	C2
11	C1	24	C6
12	C1	25	C1
13	C2	26	C2

Cluster	Crime	Cluster	Crime
1	C2	14	C11
2	C1	15	C1
3	C1	16	C6
4	C2	17	C6
5	C6	18	C1
6	C6	19	C6
7	C2	20	C10
8	C9	21	C1
9	C2	22	C6
10	C6	23	C2
11	C1	24	C6
12	C1	25	C1
13	C2	26	C2

Cluster	Crime	Cluster	Crime
1	C2	14	C11
2	C1	15	C1
3	C1	16	C6
4	C2	17	C6
5	C6	18	C1
6	C6	19	C6
7	C2	20	C10
8	C9	21	C1
9	C2	22	C6
10	C6	23	C2
11	C1	24	C6
12	C1	25	C1
13	C2	26	C2