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 presents the results of snow load calculations in Alaska based on measurements of snow depth and snow water equivalent from 1950 to 2017 acquired from the Global Historical Climate Network (GHCN). After cleaning and validation of data, snow depth and snow water equivalent values were analyzed to find the best statistical distribution using several numerical criteria and visual assessment of the best fit. The best-fit distributions were then compared with the lognormal distribution and used to calculate the 50-year mean recurrence intervals. The relationships between snow depth and snow water equivalent were analyzed. Regression models were built for both the best-fit and lognormal distributions. Finally, these regression equations were used to estimate snow water equivalent for stations where only snow depth was recorded.