In order to join virtual sessions, you must be registered and logged-in(Were you registered for the in-person meeting in Denver? if yes, just log in.) 
Note: All session times are in Mountain Daylight Time.

Leveraging Multiple Methodologies For High-Resolution Mapping of Data-Limited Subpopulations: A Case Study Using ORNL’s UrbanPop to Strengthen LandScan USA

Authors: Nathan Trombley*, Oak Ridge Institute for Science and Education
Topics: Population Geography, Land Use, Quantitative Methods
Keywords: Dasymetric mapping, Method synthesis, Gravity modeling
Session Type: Paper
Presentation File: No File Uploaded

The Human Dynamics Group at Oak Ridge National Laboratory is engaged in multiple avenues of research focused around high-resolution population mapping. LandScan USA (LSUSA) for instance, utilizes building detection derived from machine learning on satellite imagery, land use information, and other ancillary data to disaggregate published population counts to units of higher spatial (~90 meter) and temporal (daytime and nighttime) resolution. UrbanPop utilizes multi-variate survey records alongside aggregate population counts at higher spatial resolution to produce synthetic populations at the full demographic and spatial detail. Oftentimes, these methodologies are more powerful together than on their own. A case study of this symbiosis involves the US Armed Forces population, for which important input data for both methodologies is not available. LSUSA aims to distribute various subpopulations during the daytime, such as students in schools and civilian workers at workplaces, but comprehensive data on place of work for the military is not available. As a work around for this issue, LSUSA makes use of other Census summary tables, but these refer to the US population broadly as opposed to the military specifically. Synthesizing separate variables into outputs with the full attribute resolution is the specialty of UrbanPop, and by leveraging this methodology we can produce military workplace estimates that avoid questionable assumptions and that align better with published information where available. Any data-intensive project will almost certainly have some gaps in input data requiring creative problem solving and considering established methodologies from other lines of research may be the best way forward.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login