UNDERSTANDING THE SPATIOTEMPORAL PATTERN OF URBAN CRIME IN YENAGOA LOCAL GOVERNMENTAREA OF BAYELSA STATE, NIGERIA

Bitrus Eniyekenimi DAUKERE (), Effiong Ekpo () and Isaac A. Gbiri ()
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Bitrus Eniyekenimi DAUKERE: Department of Geography and Environmental Management, Postal: Ahmadu Bello University, Zaria, Nigeria, https://fssunilorinedu.org/ijbss/index.php
Effiong Ekpo: Department of Geographic Information System, Postal: Federal School of Surveying, Oyo, Nigeria, https://fssunilorinedu.org/ijbss/index.php
Isaac A. Gbiri: Department of Geographic Information System, Postal: Federal School of Surveying, Oyo, Nigeria, https://fssunilorinedu.org/ijbss/index.php

Ilorin Journal of Business and Social Sciences, 2021, vol. 23, issue 1, 1-14

Abstract: Crime has unceasingly posed not only a big threat to individuals but has also raised great concerns all over the world. This study applies geospatial techniques to analyze the spatial patterns of urban crime in Yenagoa Local Government Area of Bayelsa State, Nigeria. Crime data were obtained from five Divisional Police Headquarters in Yenagoa Local Government Area of Bayelsa State. The administrative map of the area was obtained from the Ministry of Land and Survey, Bayelsa State which was used as the base map. Average Nearest Neighbour (ANN) analysis was used to examine the distributional pattern while Kernel Density Estimation (KDE) was used to identify the hotspots of crime events in the area. Descriptive statistics was also used to examine the spatiotemporal variation of crime incidence. The result of the findings reveals that theft/stealing had the highest incidence rate of 39.4% while forgery was the least committed crime with 0.3%. ANearest Neighbour Ratio (NNR) of 0.499657 at 0.01% level of significance indicates the pattern of distribution of crime events as clustered. The result of the analysis of KDE shows that crime was clustered at Swali, Yenagoa, Ovom, Ekeki, Okaka, Yenezue-gene, Biogbolo, Opolo, Edepie and Agudama-Epie respectively.The results of the findings on the temporal pattern of crime revealed a constant increase of crime events from 2013 to 2015. The study therefore concludes that more police personnel be deployed in the hotspots areas to effectively control and prevent crime.

Keywords: Crime; Hotspots; Kernel Density Estimation; Average Nearest Neighbour. (search for similar items in EconPapers)
Date: 2021
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