Them in a random distribution (Clark and Evans, 1954). Points and their nearest neighbors with the expected distance between It compares the observed mean of the distance between Nearest neighbor statistic is commonly used in spatial pattern analysis because Opposed to retrospective, detection of disease outbreaks. Methods are not quite useful for this purpose and new methods are in need.ДЇarrington and Beale (1998) provide a summary of the motivation for prospective, Identification of the change of geographic patterns of criminal activity.

Therefore, it is very important to develop methods for the quick Quick detection of emerging and declining geographic clusters of criminalДЄctivity. This project is to develop statistical methods and monitoring models for the Identify areas of declining activity in a timely manner, to aid in theДevelopment of appropriate and effective responses. вЂњhot spotsвЂќ of increasing criminal activity. Besides, they are static and do not take into accountДЄs we all know, an important element of effective law enforcementДЄnd community policing efforts is the quick identification of emergent Kinds of maps, we can only form a very general picture of the whereabouts of Types of crime events in Buffalo, NY, the year of 1996, namely, arson, burglary, and drug. The following maps are the distribution of three Practice in crime cluster detection is using Kernel Density Estimation to This type of approach is widely adopted in spatialДЄnalysis of point data, including crime analysis. Used to determine the likelihood of the null hypothesis of no spatial clustersДЄround point locations. Observation retrospectively, that is, in a sense statistical tests are

Recently, a lot of efforts have been given to the study ofДetecting вЂњhot spotsвЂќ or geographic clusters around location points, termed as

Integration between gis and cyme