Issue link: https://geospatial.trimble.com/en/resources/i/1415423
Machine Learning + Data Mining + High Powered computing 1. 2. 3. 4. 5. Big Data (Geospatial data + non-spatial data) GIS/LI systems Visualize and understand Describe Predict Prescribe Real-world phenomena in particular locations HOW GEOAI WORKS? AI MEANS THE ABILITY OF A MACHINE TO LEARN A TASK. GEOAI REFINES THE SCOPE OF THAT LEARNING TO SPATIALLY ORIENTED TASKS us, linking locations in which we live and work, or people/elements we interact with, to explore their potential role in influencing health outcomes. ere is also extensive research into GeoAI being used for hypothe- sis generation, conducting new data linkages and predicting disease occurrence. Evalua- tion of hypotheticals helps people answer questions like "what if " — What if there were no stay at home orders? What if we open restaurants? What if we open public transport? is facilitates the evaluation of potential policy decisions. For instance, if one could take pat- terns such as the direction of COVID-19 outbreak in an area, and then correlate that with other datasets such as air travel, public transportation or cities with high popula- tion density, and put it all on a predictive model along with local environment such as humidity or temperature, there could be some models that could come out of it, which could help local authorities to tackle the problem more specifically. According to Luis Sanz, CEO, CARTO, innovative statistical methods and computa- tional tools can be used for public health sur- veillance including spatio-temporal models for disease risk prediction, cluster detection, and travel-related spread of disease. is work can inform strategic policy in reducing the burden of diseases. Location analytics provide useful tools to model behaviors and inform actions. "From maps that analyze the genetic profile of the virus as it spreads from place to place, to AI techniques that make sense of human move- ment data, we can enhance our understand- ing of viral transmission, determine if public health recommendations are being followed and predict whether travel bans and other measures will quell the spread of disease," adds Dr Geraghty. "Increasingly geospatial data is being included in more complex models used to inform early warning systems, model disease transmission and evaluate impacts of public health interventions," points out Dr Kristine Belesova, Deputy Director, Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine. For instance, the Centre uses advanced modeling techniques to link data on infec- tious diseases with climate and other environmental changes and develop early warning systems driven by Earth Observa- tion data. Other examples include identifying spatial and environmental risk factors for infectious diseases by applying geo-statistical and Machine Learning approaches, using aerial (drone) and satellite-based Remote Sensing data to assess how ecological and environmental changes impact infectious disease transmission. "We are in uncharted territory as a microscopic virus is now disrupting our entire planet. e COVID-19 pandemic has revealed the need to implement systems that proactively manage infectious disease risks which, in our rapidly changing world, are increasing in frequency, scale and impact," says Dr Kamran Khan, Founder & CEO, BlueDot, and an infectious disease specialist. GeoAI to tackle COVID-19 We all know contract tracing has emerged as one of the primary methods in COVID-19 www.geospatialworld.net | May-June 2020 35