Spatial distribution of criminal events in Lithuania in 2015–2019

Figures & data

Figure 1. Estimated crime density for the five main types of events, for 2015–2019.

Map of Lithuania show crime density as a surface, low to high values represented by diverging colour scale.

Figure 2. Dynamics of crime events in 2015–2019.

Column chart representing absolute numbers and shares of registered events in 2015, 2016, 2017, 2018 and 2019: five main types of events in open spaces and indoors, and other events.

Figure 3. Dynamics of crime events in open space, number of incidents.

Graphs representing absolute numbers of events by type in 2015, 2016, 2017, 2018 and 2019.

Figure 4. Structure of crime events, average for 2015–2019.

Two pie charts representing structure of crime in Lithuania in open space and indoors.

Figure 5. Crime events in very sparsely populated cells (average for 2015–2019).

Map of Lithuania with hexagonal cells for very sparsely populated areas, the average yearly number of events, represented by background colour, is mainly less than five.

Table 1. Estimation of types of dynamics for the cell C_i.

CiT	Values of Cij in the set for 2015–2019	Type of dynamics CTi	Symbol
SI	Only SI and I	SI (substantial increase)
SI	SI, I, S; up to one D	I (increase)
I	Up to two S; no SD, no D
I or S or D	I, S, D; no SI, no SD	S (stability)
Any	Both SI and SD; any other	O (ocillation)
D	Up to two S, no SI, no I	D (decrease)
SD	SD, D, S; up to one I
SD	Only SD and D	SD (substantial decrease)

Figure 6. Estimated log-transformed relative risk surface, 2015–2019.

Map of Lithuania showing surface of relative risk of crime, represented by diverging colour scale.

Figure 7. (a) high relative risk areas (p ≤ .05); (b) low relative risk areas (p ≤ .05).

Two schematic maps of Lithuania showing high relative risk areas mainly in south-eastern part and low relative risk areas mainly in western part.

Data availability statement

The spatial data used in this study have been generated from original event point data via aggregation into a hexagonal lattice. Precise point data on crime are sensitive and cannot be made publicly available. Aggregated data have been published online to provide possibilities for interactive access, viewing and analysis of the data in different aspects. The data have been published as a feature service using the ArcGIS online platform. This service can be further used in combination with other spatial data, as a thematic layer in applications and in decision making tools or for direct access to the data.

An operational dashboard has been constructed, that provides a glimpse into spatial distribution and main statistics of crime events and their layers. The dashboard can be used for simple territorial analysis. This method of publishing data online is based on technological architecture that can be used to update the data most efficiently and in a way that ensures continuity for data service or operational dashboard users.

Online data and relevant information are available at https://lietuvoskartografija.lt/mokslas-visiems/scientific-publications-projects/spatial-distribution-of-criminal-events-over-lithuania-in-2015–2019.