Understanding what the physical space looked like enabled researchers to identify where people were likely to be and, importantly, where they weren’t. The project began not by counting people but by analyzing and characterizing existing environmental and infrastructure data. “The idea when we started was to provide a realistic estimate of population counts for every square kilometer of the planet,” said Amy Rose, an ORNL senior staff scientist and LandScan project principal investigator. Recognizing that this lack of data could prove fatal when disaster struck, ORNL’s geospatial scientists and human geographers started a project to develop the world’s first reliable, standardized population distribution model. (Learn more about ORNL’s early population modeling work.) Most countries conducted censuses, but the data was wildly inconsistent. In the mid-1990s, the world had no good, consistent data on populations around the globe. Now their suite of LandScan datasets is available online to the global public under a new open-source creative commons license. Researchers at Oak Ridge National Laboratory have spent more than two decades working to solve this challenge through the development of cutting-edge population distribution models. But twenty-five years ago, obtaining that population data was nearly impossible. Everything else - how many people may be in danger from a weather event, who might be impacted by a resource scarcity, and how to efficiently deploy limited resources in response to a crisis - stems from the initial, basic knowledge of population locations. It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
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