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.

In Europe, especially in Eastern Europe, geographic research in criminology deals mainly with data analysis and accompanying cartographic communication through the visualisation of crime maps is less developed. Therefore, there is still a dearth of crime maps that could potentially not only show the facts, but also portray the criminal landscape of the city and tell the reader a story in a way that stimulates thinking, and motivates deeper analysis and spatial reasoning. This set of maps, which were designed by the authors, represent specific characteristics, density and temporal distribution of crimes in the city of Vilnius in 2014. It also shows changes in the crime rate from 2012 to 2014. The maps represent generalised data that was derived from detailed tabular data on reported criminal activities in 2012, 2013, and 2014. The primary map reveals some primary factors that allow understanding of spatio-temporal patterns of crimes in modern Vilnius: prevalent crime types with specific territorial and temporal distribution (crimes of violence, property crimes, and distribution/possession of drugs); changes in distribution over the examined period of 3 recent years; overall crime rate and detailed structure of 9 types of crimes, the rate of burglaries and seasonality of violent crimes for all the 21 administrative districts of the city. Original cartographic signs have been designed for the depiction of 11 characteristics. The maps are supplemented by 3D visualisations and a chart that shows changes in the overall crime rate and juvenile crime numbers in the decade from 2004 to 2014. The reference scale is 1:100,000 for the Main map and 1:300,000 for complementary maps.

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.

Two toponymical maps presented in this paper show 2332 inhabited places of Lithuania that have names (oikonyms) associated with vegetation and animals. The maps and the dataset are the outcomes of a project that is aimed at combining the outcomes of professional onomastic research with an environment that fosters exploration. The oikonyms were extracted from the reference base dataset of Lithuania by means of an automated algorithm. Original cartographic symbols have been designed for depicting categories and species. The multiscale map application, with its exploratory tools, makes it easy to see spatial distribution of geographic names related with particular groups of plants and animals. Analysis of map data enables the assertion that local toponymy reflects a distribution of species that were characteristic to the territory over past centuries. The maps are supplemented by comparative density maps and statistical charts. The reference scale of the main maps is 1: 500,000.

Please click here to download the map associated with this article. The Map of 21st Century Conflicts in Europerepresents collated information on five types of conflict and acts of terrorism within the geographic boundaries of Europe in the 21st century (January 2000–May 2010). It is compiled from data collected progressively by three teams of MSc students in Cartography at Vilnius University in Lithuania, in 2006, 2008 and 2010, totalling approximately 4,000 hours. The main goal of the project is to create an informative wall map, primarily for the general public, showing the spatial distribution of political, economic, ethnic, religious and social conflicts (not restricted to armed conflicts), as well as major terrorist incidents, while providing additional information to contrast with presently available maps that either are not specifically designed for visual analysis or encompass only a part of these conflicts, much smaller areas or shorter time spans. The project included planning, analysis of feasibility, data collection, verification, generalisation and filtering, classification of information on the conflicts, building the GIS database, analysis of data, and cartographic visualisation. The map described in this paper was finalised in 2010 and designed with special focus on the principles of cartosemiotics. It represents 118 conflicts and 35 major terrorist incidents. The remainder of the information is represented via summary choropleth maps and charts showing the statistics of conflicts in European countries by type, with textual descriptions. The relational database and exhaustive project documentation are available for further updates and development.