Crime forecasting and prevention using spatial data mining

This paper focuses on finding safe spot and hotspots using spatial data mining and predicting crime. spatial data mining was applied to a real data set in Los Angeles, and here the study took upon itself the application of the block algorithm using the mean block algorithm K in two stages, the first stage is  clustering was performed on the geographical data represented as two features, longitude and latitude, on the coordinates and here represents the spatial data, which is the focus of the study, the result which extract from this stage is , obtain labeled class  and to find clusters in the given spatial data in a separate data frame for the coordinators, the second stage, used the K-mode cluster algorithm on the (CRM_DESC, WEAPON_DESC, PREMIS_DESC)  classified the crime based on weapon used, premise and crime description to extract classified data under separate crime type data. The last step is to integrate the data extracted from the first stage with the data extracted from the second stage to know the distribution of crimes over Los Angeles regions and counties, in addition to supporting by reviewing the extent of the spread of crimes.