Segmented Population Models: Improving the LandScan USA Non-Obligate Population Estimate (NOPE)

Keywords: LandScan, Segemented Population Models, Digital Trace Data
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 02:00 PM (Eastern Time (US & Canada)) - 2/26/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 28

Abstract

A broad range of analyses and actions including emergency response and hazard mitigation rely on a clear picture of where people are and when they are there. However, estimating population with high spatial and temporal fidelity is a substantial challenge, even in data-rich environments. There are publicly available sources of information available to estimate peoples’ home location, workplace location, and populations associated with other large institutions. However, there are critical gaps in our understanding of how human behavior in the context of social, cultural, and economic activity translates to physical location over time. One such gap is our lack of understanding of the patterns of what could be described as “non-obligate” public behavior. Where do people go when they have nowhere to be? In this talk, we describe methods for improving the spatial allocation of Non-Obligate Population Estimates for LandScan USA using digital trace data on activity in public spaces.