Comparison of Different Spatial Interpolation Techniques to Thematic Mapping of Socio-Economic Causes of Crime Against Women

Rastogi, Aamil; Sridhar, Smriti; Gupta, Rajiv

doi:10.1109/sieds49339.2020.9106690

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…This section elaborates on the array of statistical tools harnessed to unearth the underlying dynamics of crime in Central Java. The research employs a suite of rigorous techniques to uncover the intricate relationships between socio-economic factors and crime rates in the region [42].…”

Section: Analytical Techniquesmentioning

confidence: 99%

Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis

Humairoh,

Purwaningsih,

Saifullah

et al. 2022

SITech

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Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java

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Section: Analytical Techniquesmentioning

confidence: 99%

Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis

Humairoh,

Purwaningsih,

Saifullah

et al. 2022

SITech

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“…In our application, we have utilized linear interpolation. The IDW was selected as the suitable spatial interpolation technique for thematic mapping of socio-economic causes of crime after a comparative study of the various spatial interpolation techniques [35]. Once this procedure was carried out for every village considered in the area of analysis, the latitude and longitude for these points were obtained from Google maps.…”

Section: Crime Hotspot Identification Using Arcgismentioning

confidence: 99%

“…We have customized a set of features of firebase, i.e authentication, authorization, and role-based access control to ensure that the data is stored securely in the firebase database. The GIS technique for spatial interpolation for thematic mapping of socio-economic causes of crime, i.e the Inverse Distance Weighted was selected after a comparative study of the various spatial interpolation techniques which are briefly described in Section III, Subsection D and is elaborated at depth in our prior work [35]. The comparison was done with three popular techniques, i.e Kringing, IDW, and Spline interpolation techniques.…”

Section: Testing and Validationmentioning

confidence: 99%

A Holistic Framework for Crime Prevention, Response, and Analysis With Emphasis on Women Safety Using Technology and Societal Participation

et al. 2021

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Ensuring women's safety in smart cities is a need of the hour. Even though several legal and technological steps are adopted worldwide, women's safety continues to be an international concern. Criminal records are maintained by law enforcement agencies and are most often not available to the public in an easily comprehensible form. While some wearable devices and mobile applications are available which are touted to aid in ensuring women's safety, they utilize limited societal intervention and are not very efficient in ensuring the safety of the women as and when required. Most often the crime response, crime analysis, and crime prevention schemes are not integrated, leading to gaps in ensuring women's safety. Our major contribution is in developing a holistic system encompassing the three crucial aspects, i.e crime analysis and mapping, crime prevention, and emergency response by leveraging societal participation for women safety management. This work applies the Geographic Information System (GIS) for the identification of hotspots and patterns of crime. The proposed system uses data generated from the mobile application and/or wearable gadget prototyped as a part of this work along with the criminal history records for crime response, analysis, and prevention. The system for the hotspot identification is demonstrated for the Pilani town in the Jhunjhunu district in the state of Rajasthan, India, and can be easily scaled up geographically and utilized as a safety strategy for smart cities. While the common man is provided a costeffective solution via the developed mobile application or wearable gadget, the various components are integrated into a website for supervisory management and can be utilized by law enforcement agencies.

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“…Rastogi et al [19] signified the applications of GIS in crime visualization and crime detection and investigation are indispensable response capabilities are more commonly implemented using a combination of data from many or-ganizations and outlets. Via an internet search of cases in police records, both through past observation, investigators may identify the suspects that have been recently been detained in certain areas and determine the crime's hot spots.…”

Section: Spatial Interpolation and Autocorrelation In Crime Hotspot Mappingmentioning

confidence: 99%

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

Panigrahy¹

2021

Preprint

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GIS has proven its proficiency in many fields but still require some more observations. The spatial interpolation which has seen widespread used in diverse domains with multiple applicability. Currently, some commercial GIS or analytics program packages now provide spatial interpolation technique including inverse distance weighting (IDW), kriging, triangulated irregular networks (TIN) interpolation, kernel density interpolation (KDE). Further, the two spa-tial autocorrelation techniques i.e., also known as LISA indicators that comprised of Moran’s I and Getis-Ord Gi* techniques. Amalgamation of spatial interpolation and spatial autocorrelation are being shown in this paper. For theimplementation, related enabling technologies are summarized. Further, this paper entailed the literature survey of multiple papers comprising the associated works carried out in the domain of spatial interpolation and spatial autocorre-lation areas of GIS. The proposed framework is a generalized framework with four tiers to realize geospatial systems. This paper scrutinized geospatial crime dataset for understanding the proposed framework realistically. Numerous results are produced to prove the efficiency of the proposed model. The paper entailed weighted-overlay analysis, symbological heatmap analysis, choropleth analysis, kernel density interpolation, and Getis-Ord Gi* spatial autocorrelation analysis implemented to practically realize the essence of geospatial crime analytics. Finally, future research directions are recapitulated, where the task of interpolation and autocorrelation might be merged with techniques of artificialintelligence, machine learning, and cloud computing paradigm, serverless com-puting framework and many such in order to enhance the proposed structurewith lessened restrictions.

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Comparison of Different Spatial Interpolation Techniques to Thematic Mapping of Socio-Economic Causes of Crime Against Women

Cited by 7 publications

References 1 publication

Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis

Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis

A Holistic Framework for Crime Prevention, Response, and Analysis With Emphasis on Women Safety Using Technology and Societal Participation

Geospatial Crime Analytics: A GIS-Based Approach Towards Prediction of Crime Hotspots

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