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It is quite evident that majority of the population lives in urban area todaythan in any time of the human history. This trend seems to increase in comingyears. A study [5] says that nearly 80.7% of total population in USA stays inurban area. By 2030 nearly 60% of the population in the world will live in ormove to cities. With the increase in urban population, it is important to keepan eye on criminal activities. By doing so, governments can enforce intelligentpolicing systems and hence many government agencies and local authorities havemade the crime data publicly available. In this paper, we analyze Chicago citycrime data fused with other social information sources using network analytictechniques to predict criminal activity for the next year. We observe that aswe add more layers of data which represent different aspects of the society,the quality of prediction is improved. Our prediction models not just predicttotal number of crimes for the whole Chicago city, rather they predict numberof crimes for all types of crimes and for different regions in City of Chicago.

Spatio-temporal prediction of crimes using network analytic approach