Communities are adversely affected by social harm events such as crime, traffic crashes, medical emergencies, and drug usages. This proposal aims to develop algorithms and software systems for the collection, analysis, and dynamic prediction of social harm events to facilitate appropriate government interventions to improve the quality of life in communities. The project has a significant community engagement component and software developed through the research will be used by the Indianapolis Metropolitan Police Department (IMPD), Indianapolis Emergency Medical Services (EMS), National Alliance of Mental Illness, the Indiana prosecutor's office, and individual citizens for sharing of social harm analytics and collaboration in social harm intervention. This objective will be achieved by: i) creating software systems for cross-agency social harm data integration, ii) developing mathematical models for capturing social harm event dynamics along with public trust and grievance towards police, and iii) conducting a field trial of the developed software system in Indianapolis. The methods developed in the project will also be applicable to other smart and connected communities across the country and could be used for data analytics integration and allocation of resources across government departments. Graduate students from both social science and computing disciplines will be trained in interdisciplinary research methods that span criminal justice, statistics, and computer science. Research interests in the domain of algorithms for heterogeneous data in smart cities will be encouraged through a workshop hosted by the investigators at Indiana University-Purdue University Indianapolis (IUPUI).

Social harm data resides within a disconnected set of community databases and current methodologies for modeling social harm neglect space-time dynamics altogether or focus on a small related subset of event types. Furthermore, interventions are designated in spatial locations for several weeks or months at a time, failing to account for the daily changes in risk of social harm events where crime, traffic crashes, and medical emergencies cluster in different times and locations in communities. Current policing interventions that focus on spatial risk (i.e., hotspots) are often too narrow and seek only to optimize crime reductions. In order to address some of these limitations, this project will develop: i) software systems for heterogeneous social harm data integration, ii) new marked point processes for modeling heterogeneous social harm event dynamics including trust and grievances towards police, iii) optimal control methods for space-time point processes that are lacking in current point process research, and iv) near real time software-human systems for deploying hourly interventions to dynamically changing risk. During phases one and two, the project team will work collaboratively with IMPD's community policing unit and leverage this unit's relationships with local neighborhood watch, faith-based, juvenile diversion, and volunteer groups that are predominantly comprised of minority community members serving largely minority neighborhoods. This collaboration will facilitate broad community buy-in for phase three and enable communication with and recruitment of community groups disproportionately exposed to social harm risk. The last phase of the project will include a randomized controlled trial of heterogeneous data driven policing in Indianapolis in collaboration with IMPD, Indianapolis EMS, Indianapolis Mayor's Office, National Alliance of Mental Illness, Marion County Prosecutor's Office, the Indy Public Safety Foundation, and the general public who will be encouraged to download a version of the application through a press release prior to the trial launch. In the trial, the extent to which police in partnership with community stakeholders can respond to dynamic, heterogeneous social harm hotspots will be investigated and the impact across four types of social harm (crime, traffic crashes, EMS calls for service, and community trust in police within high risk communities) will be measured.