Detecting and mapping crime hot spots based on improved attribute oriented induce clustering

Crime mapping is a very effective method for detecting high-crime-density areas known as hot spots. Crime hot spot is an area where the number of criminal or disorder events is larger than that in any other places, or an area where people have a higher risk of victimization. There are many theories and methods in common use by far. They explain different types of crime phenomena that occur at different geographic levels. The method which is used most widely for detecting crime hot spots is the spatial clustering in the original crime data. The application of spatial ellipses to attempting to distinguish crime hot spots has a long tradition in crime mapping. These methods do not represent the actual spatial distribution of crime and often mislead the researchers into focusing on areas of low crime importance within an ellipse. And they also depend on strict prerequisite, complex computing and a number of parameters must be entered. So it is very necessary and important to preprocess the spatial information of original data. The original data include crime events such as event time, event class, event spatial information and event object. These data have many attributes at different levels. So the attribute oriented induce method is chosen to deal with these data. According to the experiments of using traditional attribute oriented induce method, the results show that some crime event attributes are overly generalized. This paper presents an improved attribute oriented induce method and algorithm related to crime hot spots detecting, and a simple mapping method is depicted. Experiments show that the map is clearer and more precise than those by the traditional methods.