SPATIAL AND COLLATERAL DATA MINING FOR CRIME DETECTION AND ANALYSIS
KIRANMAI CHERUKURI
VNRVJ INSTITUTE OF ENGG. AND TECHNOLOGY,
HYDERABAD, INDIA.
MURALIKRISHNA IV
CENTRE FOR SPATIAL INFORMATION TECHNOLOGY,
JNTU, HYDERABAD, INDIA.
VENUGOPALA REDDY A
OSMANIA UNIVERSITY COLLEGE OF ENGINEERING,
HYDERABAD, INDIA
INTRODUCTION
Data mining techniques can help discovery and exploitation of knowledge, which can aid in many aspects of knowledge management. Information on knowledge falls into three categories:
- Knowledge about the past, which is stable, voluminous, and relatively accurate
- Knowledge about the present, which is unstable, compact, and relatively inaccurate, and
- Knowledge about the future, which is hypothetical.
Data mining, or Knowledge Discovery in Databases (KDD) in simple words is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It deals with the discovery of hidden knowledge, unexpected patterns and new rules from large Databases. Knowledge discovery in databases (KDD) is the process of identifying a valid, potentially, useful and ultimately understandable structure in data. . Spatial data mining methods are applied to extract interesting and regular knowledge from large spatial databases. In practice, data mining has two components: discovery and exploitation. During the discovery component, facts are discovered and represented as information-bearing data. During the exploitation component, these facts are applied to the solution of a specific problem. First, we discover; second, we act. The steps in the process are formulation of the problem, data evaluation, feature extraction and enhancement, prototyping and model evaluation. A simple taxonomy of knowledge discovery techniques looks like the following:
- Manual search
- OLAP
- Knowledge Engineering
- Visualization
- Automated search
- Auto-clustering
- Link analysis
- Regression (white box)
- Rule Induction
Crime Detection is an area of vital importance in Police Department. Police Department of Hyderabad, a Metropolitan city in the state of Andhra Pradesh, India, has a large collection of information recorded by the officers at the time of a particular incident. Crime rate are rapidly changing and improved analysis enables discerning hidden patterns of crime, if any, without any explicit prior knowledge of these patterns. In this background, a study is planned as per the following objectives:
- Extraction of crime patterns by analysis of available GIS based spatial data and attribute statistics,
- Prediction of a crime based on the spatial distribution of existing data and anticipation of crime rate
- Detection of a crime.
- To discern trends to identify analytical solution for police officers which can routinely be used to associate between types of incidents, location, time and descriptive detail of the incident?
These approaches preprocess data to quickly generate relevant results, analyze patterns and co-occurrence of identified concept and develop an automated solution of crime pattern. At this stage let us evaluate the scope of data mining for such tasks as mentioned above under the objectives. Data mining relies on certain concepts like association or aggregation rules which in turn are dependent on grouping of birds of same feather.