Exploratory Spatial Data Analysis of the Distribution of Multiple Crimes: A Case Study of Three Coastal Cities

Sim, Sunhui; Miller, A. W.

doi:10.1080/23754931.2015.1014277

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…In the case of finding the causes of crime, understanding the physical and social environment is essential (Sim & Miller, 2016). In many cases, crimes may have some geographic conditions (Chainey, 2014).…”

Section: Probable Causes Of Geographic Clusteringmentioning

confidence: 99%

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Spatial Distribution of Crime in Bangladesh: An Analysis

Khan¹,

Talukder²

2021

jplc

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Crime mapping has become popular as a way of analysing the spatial distribution of crime, and its use enables effective crime prevention strategies to be employed based on the analysis of hotspots. However, in Bangladesh crime mapping using Geographic Information System (GIS) is still in its infancy. This paper intends to focus on the spatial distribution of crime throughout the major administrative divisions of Bangladesh and to explore how it varies according to region. Its objective is to fill the existing knowledge gap in terms of the spatial distribution of crime in Bangladesh with the help of GIS software. All associated issues behind any crime patterns in different regions have been analysed here using references from established research. This study also illustrates the trend analysis of crime in the last three years, from 2016 to 2018, to understand the patterns of crime distribution as per present administrative divisions. It also focuses on the relative frequency of incidents of crime in all the regions of Bangladesh. Additionally, this study indicates that areas with big cities (Dhaka and Chattogram) are more prone to harbouring a greater number of offenders than other areas. As an initial study using GIS-based computerised crime mapping in Bangladesh, this research may have an important role in showing division wise crime distribution.

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Section: Probable Causes Of Geographic Clusteringmentioning

confidence: 99%

“…For this, individual data referring to this information are required. For analysing burglary, larceny, and auto theft, Global Moran autocorrelation (Moran scatterplots) is an excellent way to analyse incidents (Sim & Miller, 2016). For analysing rape incidents, spatial scan statistic has been used in many studies.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Crime in Bangladesh: An Analysis

Khan¹,

Talukder²

2021

jplc

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“…Moran's I and Geary's c are two most commonly used measures of and test for spatial autocorrelation. This fundamental concept has a long history in spatial analysis (Getis, 2008), not only in geography, but also in other disciplines such as criminology, ecology, and epidemiology (see e.g., Brown, 1982; Legendre, 1993; Rosenberg et al, 1999; Sim and Miller, 2016). The two measures were first introduced by Moran (1948) and Geary (1954), respectively, and were popularized by Cliff and Ord (1969, 1973).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Moran's I and Geary's c in Multivariate Spatial Pattern Analysis

Lin

2022

Geographical Analysis

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This article compares multivariate spatial analysis methods that include not only multivariate covariance, but also spatial dependence of the data explicitly and simultaneously in model design by extending two univariate autocorrelation measures, namely Moran's I and Geary's c. The results derived from the simulation datasets indicate that the standard Moran component analysis is preferable to Geary component analysis as a tool for summarizing multivariate spatial structures. However, the generalized Geary principal component analysis developed in this study by adding variance into the optimization criterion and solved as a trace ratio optimization problem performs as well as, if not better than its counterpart the Moran principal component analysis does. With respect to the sensitivity in detecting subtle spatial structures, the choice of the appropriate tool is dependent on the correlation and variance of the spatial multivariate data. Finally, the four techniques are applied to the Social Determinants of Health dataset to analyze its multivariate spatial pattern. The two generalized methods detect more urban areas and higher autocorrelation structures than the other two standard methods, and provide more obvious contrast between urban and rural areas due to the large variance of the spatial component.

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