SCC-IRG Track 2: Real-Time Algorithms and Software Systems for Heterogeneous Data Driven Policing of Social Harm

社区受到社会危害事件的不利影响，如犯罪、交通事故、医疗紧急情况和吸毒。这项建议旨在开发用于收集、分析和动态预测社会危害事件的算法和软件系统，以促进适当的政府干预，以提高社区的生活质量。该项目有一个重要的社区参与部分，通过研究开发的软件将被印第安纳波利斯大都会警察局(IMPD)、印第安纳波利斯紧急医疗服务(EMS)、全国精神疾病联盟、印第安纳州检察官办公室和公民个人用于分享社会危害分析和社会危害干预方面的合作。这一目标将通过以下方式实现：i)创建用于跨机构社会危害数据整合的软件系统；ii)开发数学模型以捕捉社会危害事件的动态以及公众对警察的信任和不满；iii)在印第安纳波利斯对开发的软件系统进行实地试验。该项目开发的方法也将适用于全国其他智能互联社区，并可用于数据分析、集成和跨政府部门的资源分配。社会科学和计算机学科的研究生将接受跨学科研究方法的培训，这些方法涵盖刑事司法、统计学和计算机科学。通过印第安纳大学-普渡大学印第安纳波利斯分校(IUPUI)的研究人员举办的研讨会，将鼓励对智能城市中异质数据算法领域的研究兴趣。社会危害数据驻留在一组互不相连的社区数据库和当前的社会危害建模方法中，完全忽视了时空动态，或者集中在一小部分相关的事件类型上。此外，干预措施一次被指定在空间位置上几周或几个月，没有考虑到犯罪、交通事故和医疗紧急情况在社区不同时间和地点聚集的社会危害事件风险的每日变化。目前侧重于空间风险(即热点)的警务干预措施往往过于狭隘，只寻求最大限度地减少犯罪。为了解决其中一些限制，本项目将开发：i)用于整合各种社会危害数据的软件系统；ii)用于模拟包括对警察的信任和不满在内的各种社会危害事件动态的新的标记点过程；iii)当前点过程研究中所缺乏的时空点过程的最佳控制方法；以及iv)用于部署每小时干预以动态变化的风险的近实时软件--人的系统。在第一和第二阶段，项目组将与IMPD的社区警务单位合作，并利用该单位与当地邻里守望、基于信仰的青少年分流和志愿者团体的关系，这些团体主要由主要服务于少数族裔社区的少数族裔社区成员组成。这一合作将促进社区广泛接受第三阶段，并能够与面临不成比例的社会危害风险的社区群体进行沟通和招募。该项目的最后阶段将包括与IMPD、印第安纳波利斯EMS、印第安纳波利斯市长办公室、全国精神疾病联盟、马里恩县检察官办公室、印地公共安全基金会和普通公众合作，在印第安纳波利斯对不同数据驱动的警务进行随机对照试验，鼓励公众在试验启动之前通过新闻稿下载该应用程序的一个版本。在试验中，将调查警察与社区利益攸关方合作在多大程度上能够应对动态的、不同种类的社会危害热点，并将衡量四种类型的社会危害(犯罪、交通事故、EMS服务呼叫和高危社区内的社区对警察的信任)的影响。

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.