Browsing by Subject "social harm"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    CDASH: Community Data Analytics for Social Harm Prevention
    (IEEE, 2018-09) Pandey, Saurabh; Chowdhury, Nahida; Patil, Milan; Raje, Rajeev R.; Shreyas, C. S.; Mohler, George; Carter, Jeremy; Computer and Information Science, School of Science
    Communities are adversely affected by heterogeneous social harm events (e.g., crime, traffic crashes, medical emergencies, drug use) and police, fire, health and social service departments are tasked with mitigating social harm through various types of interventions. Smart cities of the future will need to leverage IoT, data analytics, and government and community human resources to most effectively reduce social harm. Currently, methods for collection, analysis, and modeling of heterogeneous social harm data to identify government actions to improve quality of life are needed. In this paper we propose a system, CDASH, for synthesizing heterogeneous social harm data from multiples sources, identifying social harm risks in space and time, and communicating the risk to the relevant community resources best equipped to intervene. We discuss the design, architecture, and performance of CDASH. CDASH allows users to report live social harm events using mobile hand-held devices and web browsers and flags high risk areas for law enforcement and first responders. To validate the methodology, we run simulations on historical social harm event data in Indianapolis illustrating the advantages of CDASH over recently introduced social harm indices and existing point process methods used for predictive policing.
  • Thumbnail Image
    Item
    Enhancing Trust-based Data Analytics for Forecasting Social Harm
    (IEEE, 2020-09) Chowdhury, Nahida Sultana; Raje, Rajeev R.; Pandey, Saurabh; Mohler, George; Carter, Jeremy; School of Public and Environmental Affairs
    First responders deal with a variety of “social harm” events (e.g. crime, traffic crashes, medical emergencies) that result in physical, emotional, and/or financial hardships. Through data analytics, resources can be efficiently allocated to increase the impact of interventions aimed at reducing social harm -T-CDASH (Trusted Community Data Analytics for Social Harm) is an ongoing joint effort between the Indiana University Purdue University Indianapolis (IUPUI), the Indianapolis Metropolitan Police Department (IMPD), and the Indianapolis Emergency Medical Services (IEMS) with this goal of using data analytics to efficiently allocate resources to respond to and reduce social harm. In this paper, we make several enhancements to our previously introduced trust estimation framework T-CDASH. These enhancements include additional metrics for measuring the effectiveness of forecasts, evaluation on new datasets, and an incorporation of collaborative trust models. To empirically validate our current work, we ran simulations on newly collected 2019 and 2020 (Jan-April) social harm data from the Indianapolis metro area. We describe the behavior and significance of the collaboration and their comparison with previously introduced stand-alone models.
  • Thumbnail Image
    Item
    Improving social harm indices with a modulated Hawkes process
    (Elsevier, 2018-06) Mohler, George; Carter, Jeremy; Raje, Rajeev; Computer and Information Science, School of Science
    Communities are affected adversely by a range of social harm events, such as crime, traffic crashes, medical emergencies, and drug use. The police, fire, health and social service departments are tasked with mitigating such social harm through various types of interventions. While various different social harm indices have been proposed for allocating resources to spatially fixed hotspots, the risk of social harm events is dynamic, and new algorithms and software systems that are capable of quickly identifying risks and triggering appropriate public safety responses are needed. We propose a novel modulated Hawkes process for this purpose that offers flexible approaches to both (i) the incorporation of spatial covariates and leading indicators for variance reduction in the case of rarer event categories, and (ii) the capture of dynamic hotspot formation through self-excitation. We present an efficient l1-penalized EM algorithm for estimating the model that performs feature selection for the spatial covariates of each incident type simultaneously. We provide simulation results using data from the Indianapolis Metropolitan Police Department in order to illustrate the advantages of the modulated Hawkes process model of social harm over various recently introduced social harm indices and property crime Hawkes processes.
  • Thumbnail Image
    Item
    Trust Estimation of Historical Social Harm Events in Indianapolis Metro Area
    (IEEE, 2019-10) Pandey, Saurabh; Chowdhury, Nahida; Raje, Rajeev R.; Mohler, George; Carter, Jeremy; School of Public and Environmental Affairs
    Social harm involves incidents resulting in physical, financial, and emotional hardships such as crime, drug overdoses and abuses, traffic accidents, and suicides. These incidents require various law-enforcement and emergencyresponding agencies to coordinate together for mitigating their impact on society. In this paper, we discuss the enhancements made to Community Data Analytic for Social Harm Prevention (CDASH) - a system that we have created for analyzing historical social harm events. CDASH predicts `hot-spots’ and displays them graphically to law-enforcement officials. The enhanced system, called Trusted-CDASH (T-CDASH), superimposes a trust estimation framework on top of CDASH. We discuss the importance and necessity of associating a degree of trust with each social harm incident reported to T-CDASH. We also describe different trust models that can be incorporated for assigning trust while examining their impact on prediction accuracy of future social harm events. To validate the trust models, we run simulations on historical social harm data of Indianapolis metro area, illustrating the behavior of each trust model and exploring their significance.