Let’s think about an interesting story reported on New York Times in 2004 [26], which shows the benefit of the analysis of geospatial data. Hurricane Frances was approaching Florida’s At-lantic coast across the Caribbean. The executives at Walmart de-cided to adopt one of big data technologies—predictive analytics [27]. Linda M. Dillman, Walmart’s chief information officer, asked her staff to predict what would happen soon based on what had happened when Hurricane Charley landed severalweeks ago. By analyzing the transaction records stored in Walmart’s data ware-house, the company could predict which items were bought just before or after an event (i.e., a hurricane) at a specific region. Peo-ple who had lived in Florida’s Atlantic coast did not increasingly buy some products directly related to hurricanes, e.g., water and flash lights. Surprisingly, strawberry PopTarts increased in sales, by seven times compared with their usual sale rate, just before a hurricane. In addition, the top-selling item immediately before the hurricane was beer. This kind of predictive analytics can be used for reducing the cost of maintaining the inventory and shipping items between warehouses