Advancing Smart and Resilient Cities with Big Spatial Disaster Data
Xuan Hu, Jie Gong · Auerbach Publications eBooks · 2018
Severe weather events such as hurricanes, ice storms, surge, and flooding have been occurring across the U.S and around the world, threatening places where economic and industrial activities are heavily concentrated. These extreme events are now increasing observed and monitored with a loosely coupled network of geospatial sensors. Analysis of these datasets offers tremendous opportunities in improving the resilience and adaptability of cities in the face of future natural disasters. Despite the high values in these data sets, the vast size and complex processing requirements of these new data sets make it challenging to effectively use them in city management applications, in particular emergency situations. In this chapter, we will characterize the basic anatomy of big spatial disaster data to highlight the challenges and opportunities in using these emerging data sets in city management applications during extreme events, and will present our research progresses in designing data analytics frameworks during extreme events to integrate, share, and process these large data sets for an array of critical disaster management tasks. A central component of our study is on how to use big data infrastructure to accelerate the processing of the massive amount of geospatial data, in particular streaming data, such that crucial insights can be extracted from the data within a realistic time-bound and time-sensitive decisions can be made to optimize city operations during extreme events.