Spatial distribution of criminal events in Lithuania in 2015–2019

Beconytė, Giedrė; Govorov, Michael; Balčiūnas, Andrius; Vasiliauskas, Darius

doi:10.1080/17445647.2021.2004940

Cited by 4 publications

(7 citation statements)

References 32 publications

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“…Our previous research shows that the share of open space events is a potential regionshaping factor in Lithuania. For example, the gradual decrease in the number of events in open spaces from 2015 to 2019 (45%) was relatively larger than of those in premises (14%); the growth of open-space crime (that is opposite to overall trend) is solely concentrated in Kaunas [14]. In Vilnius city, the ratio of the number of violent incidents in open spaces varied between different areas during the COVID-19 pandemic [40].…”

Section: Datamentioning

confidence: 96%

“…The history of the cartography of crime, spanning almost two centuries, has taken us from isolated mapping techniques to sophisticated analyses and design of instruments for reducing the risk of crime. For a more detailed overview of previous crime mapping research and for the arguments underlying the importance of national data-driven studies, see our previous publication [14].…”

Section: Previous Researchmentioning

confidence: 99%

“…In our previous papers [14,17], we provided a more detailed summary of geographical crime research in Lithuania. These studies also include the inventory and analytical maps of the crime situation.…”

Section: Previous Researchmentioning

confidence: 99%

“…• Violent crime (VIO), such as homicide, murder, assault, manslaughter (bodily injury), sexual assault, rape, robbery, abduction and harassment; • Property offences (PRO), such as theft, destruction or damage to property; • The infringement of public policy (IPP), such as breaches of the peace, illicit consumption of alcohol, noise, littering, etc. The choice of the regionalisation variables was made on the basis of previous studies, which show that there are considerable spatial differences in their dispersion in Lithuanian cities, and the ratio of VIOs to PROs markedly differs in sparsely and densely populated areas [14,40]. In addition to these three types, the RERP database includes traffic accidents, drug possession and distribution accidents and unclassified ("other") accidents.…”

Section: Datamentioning

confidence: 99%

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Areas of Crime in Cities: Case Study of Lithuania

Beconytė,

Gružas,

Spiriajevas

2023

IJGI

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In all countries, cities and their suburbs are the most densely populated areas. They are also the places visited by the largest number of tourists and one-day visitors, who inevitably run the risk of becoming victims of crime. It is, therefore, important, not only at national but also at the international level, to know the structure of urban crime and identify urban areas that differ in terms of their criminogenic situation. This requires a geographical approach and regionalisation based on the quantitative data that can offer it. This paper presents the results of a study using big data regarding violent crime, property crime and infringements against public order registered by the police in 2020 in the territories of three major Lithuanian cities and their suburbs (n = 149,239). Events in open spaces were separately addressed. A series of experiments were carried out using several spatial clustering methods. The automatic zoning procedure method that gave the best statistical results was then tested with different combinations of parameters. In each city, seven types of areas of urban crime were identified. Maps of crime areas (regions) were created for each city. The results of the regionalisation have been interpreted from a socio-geographical point of view and conform with previous sociological urban studies. Seven types of areas of crime have been identified, which are present in all the cities studied and, according to a preliminary assessment, roughly correspond to the socio-demographic and urban zones of each city. The maps of crime areas can be applied for crime prevention planning and communication, real estate valuation, strategic urban development planning and other purposes.

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Section: Datamentioning

confidence: 96%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 2 more Smart Citations

Areas of Crime in Cities: Case Study of Lithuania

Beconytė,

Gružas,

Spiriajevas

2023

IJGI

Self Cite

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“…Also, different types of crime show different distribution and dynamics. Whereas the total crime constantly decreased from 2015 (Beconytė et al, 2021), it was not the case with violent crime (Figure 1). The number of violent crime events also increased in 2020 -the first year of the Covid-19 pandemic.…”

Section: Introductionmentioning

confidence: 94%

Violent crime in Lithuania: trends and patterns in 2015–2020

Beconytė¹,

Gružas

Govorov

2022

AGILE GIScience Ser.

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Abstract. The paper presents the results of analysis of spatial distribution of violent crime in Lithuania. Two periods are compared: 2015–2019 that can be characterized as a period with relatively stable crime dynamics and 2020, the year of Covid-19 pandemic. Violent crime (events that have elements of direct threat to a person) was chosen because it is the type of crime that causes the most harm and because the worrying trend of its growth has been observed against a backdrop of declining overall crime. We demonstrate how the distribution of violent crime had changed in Lithuania in 2020 compared to the trends of 2015–2019 and, specifically, during the two lockdown periods of 2020 – between March 3 and June 17 and from 4 November to the end of the year.

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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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Spatial distribution of criminal events in Lithuania in 2015–2019

Cited by 4 publications

References 32 publications

Areas of Crime in Cities: Case Study of Lithuania

Areas of Crime in Cities: Case Study of Lithuania

Violent crime in Lithuania: trends and patterns in 2015–2020

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

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