GIS-Based Crime Analysis Paper

GIS Based Crime Hotspot Mapping And Analysis Using

Interpolation Method

Surya U,

Master of Engineering,

Department of Computer science and Engineering,

Avinashilingam University,Coimbatore-641108.

Phone.No:9500701371, E Mail:[e-mail]

Abstract--- Crime is the major activity which is uniform in all aspects. One of the major actions that have to be performed by crime investigation department are mitigation hot spot of locations where the number of crimes are happening more.In this paper Radial Basis Function (RBF) is introduced which predict values that can vary above the maximum or below the minimum of the measured values and triangulation with linear interpolation method to provide a flexible structure that can

GIS functions combined with capabilities of location identification devices such as GPS ,facilitate tracking the movement of high risk inmates or at risk personnel throughout an area .GPS device is more cost effective for crime analsiis to come up with the information for patrol officers to do themselves.

3.METHODOLOGY

To implement the Hotspot detection the methobology shown in the figure 3.1 followed .It consists of six steps for detecting the crime hotspots .Dataset collection is the basic steps for implementation .In the second step the latitude and longitudinal values of the area fed in to the excel file .In the third step files are transformed to the GIS environment and the data are digitized and crimes are analysed and mapped using interpolation

RBF methods estimates values that can vary above the maximum or below the minimum of the estimate values. In radial basis interpolation method, there is a framework that controls the smoothness of the effective surface. The resulting values of the methods are based on a mathematical function that minimises overall surface curvature, generating quite smooth surfaces. The disimilarity among them are slight, so the generated surfaces are almost same. A formula f, which minimises the following factor is an example of RBF technique and more specifically of the exact spline method: A(f) +∑_(i=1)^n▒〖W_i^2 [f(X_i )-Y(X_i)]^2 〗 where y(xi)=z(xi)+ε(xi) is the source of stochastical error, where z is the measured value of an non saptial at point xi and epsilon is the associated stochastical error. A(f) represents the flatten surface of the function f and the second term represents its proximity to the data .

4.1.2 Triangular With Linear Interpolation