  Crime is an unlawful act that carries legal repercussions. Bangladesh has a
high crime rate due to poverty, population growth, and many other
socio-economic issues. For law enforcement agencies, understanding crime
patterns is essential for preventing future criminal activity. For this
purpose, these agencies need structured crime database. This paper introduces a
novel crime dataset that contains temporal, geographic, weather, and
demographic data about 6574 crime incidents of Bangladesh. We manually gather
crime news articles of a seven year time span from a daily newspaper archive.
We extract basic features from these raw text. Using these basic features, we
then consult standard service-providers of geo-location and weather data in
order to garner these information related to the collected crime incidents.
Furthermore, we collect demographic information from Bangladesh National Census
data. All these information are combined that results in a standard machine
learning dataset. Together, 36 features are engineered for the crime prediction
task. Five supervised machine learning classification algorithms are then
evaluated on this newly built dataset and satisfactory results are achieved. We
also conduct exploratory analysis on various aspects the dataset. This dataset
is expected to serve as the foundation for crime incidence prediction systems
for Bangladesh and other countries. The findings of this study will help law
enforcement agencies to forecast and contain crime as well as to ensure optimal
resource allocation for crime patrol and prevention.
