National Crime Analyses and Forecasting: Case Study of North Macedonia
Date Issued
2020-10
Author(s)
Dedinec, Aleksandra
Abstract
Reducing national crime rate is an extremely important, but
also difficult problem. For solving it, it is necessary to discover patterns
of its occurrence, the various factors that influence it and the connection between criminal actions, which can help forecast future events,
especially violent crimes where the police should act immediately. For
this purpose, a database for crime records within 9 years period (2011-
2020) is used in this paper, based on data presented in Crime Map of
North Macedonia1, where the system uses natural language processing
to extract information from the official written reports published by the
Ministry of Interior. From these reports, information about the date, the
location (latitude and longitude), city, type of crime and description are
extracted. Since the accuracy of this database is crucial for the precision
of the crime analysis and forecasting, an additional data verification and
cleaning process was conducted, certain inconsistencies were corrected
and additional detailed information about the municipality and settlement of the crimes was added. Detailed analysis of the historical data is
made in this paper, in terms of the number of crimes per region, year,
month and type of crime, which help in establishing an appropriate forecasting model. Crime forecasting is considered as a classification problem
in this research, and a model based on gradient boosted decision trees
has been developed, where aggregated historical features for each cluster
(based on location) and day combination are used as input. The results of
the model show whether on a given date and location, violent crime will
occur, where a police is needed beforehand that can potentially prevent
the crime.
also difficult problem. For solving it, it is necessary to discover patterns
of its occurrence, the various factors that influence it and the connection between criminal actions, which can help forecast future events,
especially violent crimes where the police should act immediately. For
this purpose, a database for crime records within 9 years period (2011-
2020) is used in this paper, based on data presented in Crime Map of
North Macedonia1, where the system uses natural language processing
to extract information from the official written reports published by the
Ministry of Interior. From these reports, information about the date, the
location (latitude and longitude), city, type of crime and description are
extracted. Since the accuracy of this database is crucial for the precision
of the crime analysis and forecasting, an additional data verification and
cleaning process was conducted, certain inconsistencies were corrected
and additional detailed information about the municipality and settlement of the crimes was added. Detailed analysis of the historical data is
made in this paper, in terms of the number of crimes per region, year,
month and type of crime, which help in establishing an appropriate forecasting model. Crime forecasting is considered as a classification problem
in this research, and a model based on gradient boosted decision trees
has been developed, where aggregated historical features for each cluster
(based on location) and day combination are used as input. The results of
the model show whether on a given date and location, violent crime will
occur, where a police is needed beforehand that can potentially prevent
the crime.
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