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using different assumptions about the level of social distancing being practiced within a locality. In China, in the initial days when the virus was spreading, tech major Baidu leveraged AI-powered mapping systems to identify the flow of travel across high-risk areas using Baidu Maps Migration Big Data Platform to provide epidemiologists real- time insights into the virus spread to speed up local preparedness and response efforts. In Taiwan, the government collected mobile data to identify cruise ship passengers who were potentially infected. e geospatial data layer obtained from telecommunications companies were then added to identify and make public potential high-risk areas. "A lot of research institutes or even companies have been using data modeling to get insights into how the disease is spreading and in case of companies how it affects their businesses," says Gladys Kong, CEO, Uber- Media, a Location Intelligence player with an innovative mobile DSP featuring Machine Learning powered optimization. One of UberMedia's partners in the COVID-19 response work — a research institute — looked at people's movement and the distance traveled from home. As more and more cities begin to reopen, this can track how the numbers are changing and help prepare for a second wave. Further, based on predictive modeling, retailers and restaurants can figure out how to forecast their sales, how much staff they need and also some inven- tory, explains Kong. However, many such models currently in use do not generalize well across large geographic areas, since they depend on uni- formity — on the assumption of variables such as health factors, population density and mobility. Role of social media in GeoAI Advancements in AI have also seen a growing interest in real-time syndromic surveil- lance based on social media data. In fact, a common theme across GeoAI applica- tions is the use of novel sources of spatial Big Data, such as social media, electronic health records, satellite Remote Sensing CLASSIFICATION CLUSTERING PREDICTION response. What many don't know is what all can be done with the data collected. Tracking mobile phone of users allows health authorities to collect all personal data including location. us, the authorities can quickly find suspected patients and close contacts through data retrospective analysis, and quarantine and cut off the source of infection in time, explains Zhang Yaqing, Technical Director, Platform Center, SuperMap, which was involved in the COVID-19 response operations in China. Health professionals already deal with an overwhelming amount of data, especially in case of a widespread epidemic. is includes medical history and laboratory test results for each patient. Adding the spatial component on top of these enables experts to analyze and predict who are the people that should be tested or quarantined, or the paern of the disease in a particular area, or where the next wave will come. Authorities across the world are using GeoAI appli- cations to identify hotspots, prevent the disease from spreading and flaen the curve, which is paramount to preventing health- care infrastructure from collapsing. It is also being used to plan for and provide medical expertise and supplies at hotspots. In many countries, analysis of such data is being made publicly available so that citizens can avoid areas with high infection rates. Geo AI for classification, clustering and prediction ere are broadly three key areas of focus for geospatial AI which surround the concepts of classification, clustering and prediction. Classification is about detecting objects or changes in an environment. "For COVID-19 we have a good example with the social distancing information that is being obtained from cellphone data (aggre- gated and anonymized) and shows a change in human mobility paerns compared to pre-COVID-19 time periods," points out Dr Geraghty. Clustering is about analytical models that find statistical paerns in data, such as hotspots of COVID-19 cases. Prediction allows analysts to build forecast scenarios on what may happen next. A great example of AI for prediction is the various hospital surge models being used to anticipate where and when hospitals may be overwhelmed by COVID-19 cases. ose models can be run Detecting objects or changes in an environment E.g. Social distancing information being obtained from cell phone data Identifying statistically significant patterns in data E.g. Running analytical models to find hotspots of COVID-19 cases Allows analysts to build what-next scenarios for forecast E.g. Surge models being used to anticipate where and when hospitals may be overwhelmed by COVID-19 cases www.geospatialworld.net | May-June 2020 36 TECHNOLOGY TRACK

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