IWARN: A people-centered approach for early warning

Early warning is the activity of the mitigation phase concerned with monitoring precursors of a potential hazard to decide whether it is evolving to real risk and eventually initiate an early response. The first step consists of collecting updated and reliable data to support situational awareness from emergency operators. Data-centered Early Warning Systems (EWS) are focused on gathering data and run simulations to support decision-makers. A more sustainable approach consists of a people-centered EWS that takes profit from citizens who act as intelligent sensors collecting and sharing purposeful information. This people-centered approach can contribute to raising community awareness of the local environment and its vulnerabilities. In this paper, we introduce iWarn, a system relying upon mobile to integrate citizens in this process. The system has been developed following an action research approach to involve different stakeholders, including professionals, volunteers and citizens.This work is partly funded by “Comunidad de Madrid en el marco del convenio plurianual con la Universidad Carlos III Madrid en su línea de actuación Excelencia para el Profesorado Universitario-V Plan Regional de Investigación Científica e Innovación Tecnológica 2016-2020” the Spanish Ministry of Economy and Competitiveness Project TIN2016-77690-R “PACE

IWARN: A people-centered approach for early warning

Early warning is the activity of the mitigation phase concerned with monitoring precursors of a potential hazard to decide whether it is evolving to real risk and eventually initiate an early response. The first step consists of collecting updated and reliable data to support situational awareness from emergency operators. Data-centered Early Warning Systems (EWS) are focused on gathering data and run simulations to support decision-makers. A more sustainable approach consists of a people-centered EWS that takes profit from citizens who act as intelligent sensors collecting and sharing purposeful information. This people-centered approach can contribute to raising community awareness of the local environment and its vulnerabilities. In this paper, we introduce iWarn, a system relying upon mobile to integrate citizens in this process. The system has been developed following an action research approach to involve different stakeholders, including professionals, volunteers and citizens

Establishing a Data Science for Good Ecosystem: The Case of ATLytiCS

Data science for social good (DSSG) initiatives have been championed as worthy mechanisms for transformative change and social impact. However, researchers have not fully explored the systems by which actors coordinate, access data, determine goals and communicate opportunities for change. We contribute to the information systems ecosystems and the nonprofit volunteering literatures by exploring the ways in which data science volunteers leverage their talents to address social impact goals. We use Atlanta Analytics for Community Service (ATLytiCS), an organization that aids nonprofits and government agencies, as a case study. ATLytiCS represents a rare example of a nonprofit organization (NPO) managed and run by highly-skilled volunteer data scientists within a regionally networked system of actors and institutions. Based on findings from this case, we build a DSSG ecosystem framework to describe and distinguish DSSG ecosystems from related data and entrepreneurial ecosystems