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1 Introduction Kernel density estimation (KDE) is a multipurpose nonparametric technique. It is used to estimate the probability density function (PDF) and probability mass function (PMF) of a random variable, intensity function of a point process, relative risk function, spatial regression function, and other quantitative measures. It can be used for exploratory and confirmatory analysis of spatial and temporal data, as well as cartographic visualization.
5. Discussion and Conclusions Detailed analysis of the distribution of crime in Lithuania [,] allowed asserting that crime patterns are relatively homogeneous within a 500 to 2000 m radius and very strongly correlated with population density, especially in densely populated areas. Variations of violent crime are inhomogeneously distributed and usually have seasonal character.
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