Discovering SpatioTemporal Warning Contexts from non-emergency call reports

In the last years, municipal governments have started to share open data about citizens’ activities in smart city environments. In particular, the Open311 project has fostered the release of non-emergency call reports thus enabling the use of Machine Learning techniques to discover actionable knowledge. To delimit the intervention area and the period of analysis in response to specific non-emergency warning categories, police and municipality officers would need precise spatial and temporal indications. To this end, we present a new approach based on the discovery of multiple-level flipping correlations between non-emergency warning categories and contextual features. We define the new concept of SpatioTemporal Warning Context to characterize the spatial and temporal conditions in which a particular warning category or subcategory is likely (or unlikely) to occur. We carry out a preliminary validation of the proposed approach on real open data acquired from the smart cities of New York City (U.S.) and Turin (Italy).