AAAI Publications, Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

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Integrating Environmental Data, Citizen Science and Personalized Predictive Modeling to Support Public Health in Cities: The PULSE WebGIS
Enea Parimbelli, Daniele Pala, Riccardo Bellazzi, Cecilia Vera-Munoz, Vittorio Casella

Last modified: 2018-06-20

Abstract


The percentage of the world’s population living in urban areas is projected to increase significantly in the next decades. This makes the urban environment the perfect bench for research aiming to manage and respond to dramatic demographic and epidemiological transitions. In this context the PULSE project has partnered with five global cities to transform public health from a reactive to a predictive system focused on both risk and resilience. PULSE aims at producing an integrated data ecosystem based on continuous large-scale collection of information available within the smart city environment. The integration of environmental data, citizen science and location-specific predictive modeling of disease onset allows for richer analytics that promote informed, data-driven health policy decisions. In this paper we describe the PULSE ecosystem, with a special focus on its WebGIS component and its prototype version based on New York city data.

Keywords


geographical information system;smart city;citizen science; data integration; decision support system; visual analytics; predictive modeling; environmental risk factors;

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