Crime mapping studies have a history of more than 150 years. GIS gives an array of suitable capabilities for crime analysis mapping. The present study utilises GIS to examine the relationships between burglaries and the socio-demographic characteristics in the context of Leicestershire County, UK, with a hypothesis that the socio-economic status can influence the crime patterns and crime rate of particular area. The factors such as population density, unemployment, single households, housing types, occupancy and tenure/ownership are considered for this study. Using the regression analysis models - Geographically Weighted Regression (GWR) and Ordinary Least-Squares (OLS), the variations in the relationships are examined. The variables such as population density, unemployment rate, vacant and rented properties are both locally and globally significant. The practical implication of the regression analysis techniques is that, by exploring the local processes that drive crime levels it is possible to implement crime prevention policies in different locations.