A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Enhancing Trust-based Data Analytics for Forecasting Social Harm
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.
Enhancing Trust-based Data Analytics for Forecasting Social Harm
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.
Enhancing Trust-based Data Analytics for Forecasting Social Harm
Chowdhury, Nahida Sultana (author) / Raje, Rajeev R. (author) / Pandey, Saurabh (author) / Mohler, George (author) / Carter, Jeremy (author)
2020-09-28
2919138 byte
Conference paper
Electronic Resource
English
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| Online Contents | 2021