Criminality Analysis in the Czech Republic using Self-Organizing Maps
|Author:||Bc. Pavla Mikulíková|
|Year:||2020 - summer|
|Leaders:|| doc. Ing. Tomáš Cahlík CSc.
|Work type:|| Bachelors
|Awards and prizes:|
|Abstract:||Crime represents one of the most persistent social problems all around the world. To
understand the motivation for criminal behaviour, a thorough analysis of its plausible
determinants is necessary. This bachelor thesis aims at exploring whether the method
of self-organizing maps, a data mining tool, can help in the investigation of the Czech
criminal phenomena. To date, no academic study has tried to uncover potential patterns in the Czech crime data employing this type of artificial neural network. It is a
visualisation method which maps observations based on their multi-dimensional features
into a two-dimensional grid, and at the same time, the similarity between observations
is preserved by locating similar observations close to each other. For the analysis, the
dataset consisting of 75 Czech districts and 18 variables was used. However, the optimal
choice of parameters of the model can be seen as a possible limitation of this method.
The final outcome of the model consists of six clusters of districts with various levels of
crime rates and other characteristics. Our results showed that self-organizing maps can
provide an interesting insight into the crime problem, and social sciences can benefit from
its application in many research areas.