Abstract:
There had been a colossal upturn in the criminality in the recent past. Law violations
are a common social issue affecting the quality of life and the economic growth of a
city/country. A crime incident is a multi-dimensional complex phenomenon that is
closely associated with temporal, spatial, societal, and ecological factors. Data mining
is a way to extract knowledge out of usually large data sets; in other words, it is an
approach to discover hidden relationships among data by using artificial intelligence
methods of which decision tree is inclusive. The wide range of machine learning
applications has made it an important field ofresearch. Criminology is one ofthe most
important fields for applying data mining. With the rise of crimes, security forces
continuing to demand advanced systems and new approaches to improve crime
analytics and better protect their communities. Crime forecasting is notoriously
difficult. In an attempt to utilize all these factors in crime pattern formulation, we
propose a new feature construction and feature selection framework for crime
forecasting. A new concept of multi-dimensional feature denoted as spatio-temporal
pattern, is constructed from local crime cluster distributions in different time periods
at different granularity levels. We applied Decision tree applied in the context oflaw
enforcement and intelligence analysis holds the promise of alleviating such problem.
This study considered the development of crime prediction prototype model using
decision tree algorithm.
Decision Tree Algorithm and Google Map API are used for the analysis ofthe dataset.
We used dataset for real-world crimes in a city ofthe US, Fort Worth. After gathering
the data, we had built the desired model for the analysis of the required model on
dataset.
