The paper presents the results of the investigation of the applicability of spatiotemporal kernel density estimation (KDE) methods for density mapping of violent crime in Lithuania. Spatiotemporal crime research helps to understand and control specific types of crime, thereby contributing to Sustainable Development Goals. The target dataset contained 135,989 records of the events registered by the police of Lithuania from 2015–2018 that were classified as violent. The research focused on choosing appropriate KDE functions and their parameters for modeling the spatiotemporal point pattern of this particular type of crime. The aim was to estimate density, mass, and intensity function(s) so that they can be used in further confirmatory spatial modeling. The application-driven objective was to obtain reliable and practically interpretable KDE surfaces of crime events. Several options for improving and extending the investigated KDE methods are demonstrated.

This paper addresses the use of nonparametric kernel density estimation (KDE) to estimate point‐based data density in spatial modeling using Geographic Information Systems (GIS). The paper highlights challenges in selecting the appropriate settings for generating the best fitting KDE surfaces and validating their accuracy, as many GIS packages lack sufficient tools for this purpose. The paper focuses on providing guidelines for choosing the best bivariate KDE surface to approximate point patterns, using principles of machine learning for evaluation of the accuracy of KDE using internal and external metrics. Performance evaluation is based on the mass‐preservation property of spatial point processes with the introduction of metrics such as residuals, cross‐validation errors, and out‐of‐sample errors. These approaches are demonstrated on statistical data for violent crime in Lithuania but can be applied to other datasets with spatial point patterns.

Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

The article presents the preliminary results of spatio-temporal analysis of the events registered by Lithuanian police in 2015–2019. Such analyses contribute to understanding of crime as a socially and spatially bound phenomenon. Distribution of crime is complex, inhomogeneous and depends on various characteristics of locations. Of all 3.48 million of events registered in the five-year period, five groups were selected for analysis: crime of violence, thefts, destruction of or damage to property, drug-related crime and public nuisance, presuming that these five groups best represent the crime situation in the country. The total amount of analysed records was 1.69 million. Point events have been aggregated into hexagonal cells 25 square kilometer each, covering the entire territory of the country. The crime rate was calculated based on the population data for each cell. Appropriate methods for the temporal and probability density analysis of crime were selected and applied to the cells, yielding relative crime probability density maps for all events and, separately, for open space events. The density information was complemented by the cells with a stable significant growth of the crime rate for five years. Thus the most problematic areas are clearly seen and can be further analysed in detail. The multi-scale lattice maps for each of the five groups of crime have been designed and made publicly available at the Lithuanian spatial information portal geoportal.lt where they can be laid over various available geospatial data and used for spatial insights.