Browsing by Author "Pandey, Saurabh"

Now showing 1 - 4 of 4
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    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.
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    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.
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    The Indianapolis harmspot policing experiment
    (Elsevier, 2021-05) Carter, Jeremy G.; Mohler, George; Raje, Rajeev; Chowdhury, Nahida; Pandey, Saurabh; Computer and Information Science, School of Science
    Purpose This 100-day experiment explored the impact of a dynamic place-based policing strategy on social harm in Indianapolis. Scholars have recently called for place-based policing to consider the co-occurrence of substance abuse and mental health problems that correlate within crime hot spots. Moreover, severity is not ubiquitous across harmful events and should thus be weighted accordingly. Methods Harmspots and hotspots were operationalized for this experiment and both received proactive police activities. Evaluation analyses includes multivariate point processes and hawkes processes to determine experimental effects. Survey data was collected via telephone surveys, was weighted for demographic representativeness, and analyzed using Poisson regression. Results Results indicate proactive policing in dynamic harmspots can reduce aggregated social harm. No statistical deterrence effect was observed in crime hotspots. Proactive police activity in harmspots was associated with higher arrest rates, though not disproportionate across race and ethnicity, nor was there an effect on incidents of use of force. A two-wave pre/post community survey indicated Indianapolis citizens believe data-driven policing to be useful, though perceptions vary across demographic groups with moderate trust around computer algorithms. Conclusion Place-based policing strategies should consider social harm events as a method to operationalize proactive policing. Observed effects are consistent with those of hotspots policing while enabling cities to broaden the set of harms experienced by varying communities. Harmspot policing may also position municipalities to maximize social service delivery at places beyond policing.
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    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.