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The National Academies

ACRP 02-84 [Anticipated]

High Resolution Spatiotemporal Population Distribution Data and Modeling for Aviation Noise Research Studies

  Project Data
Funds: $300,000
Staff Responsibility: Joseph D. Navarrete
Fiscal Year: 2018

This project has been tentatively selected and a project statement (request for proposals) is expected in November 2017. The project statement will be available on this world wide web site. The problem statement below will be the starting point for a panel of experts to develop the project statement.

While the number of people impacted by aircraft noise is an important issue, consideration is often limited to a spatial assessment rather than the temporal aspects of a population’s distribution.  Yet in many instances, the distribution of a population can vary greatly throughout the day as people move among various locations (e.g., home, work, school, and recreational activities). It is also likely that an individual’s annoyance for aircraft noise varies by activity and location.  The recent availability of high-resolution population distribution data in both spatial and temporal domains (e.g., the Oak Ridge National Laboratory’s LandScan USA dataset) provides the opportunity to estimate population both spatially and temporally, and population scientists have been able to improve spatial granularity further by estimating facility occupancy from open source data.  In addition, social media and other human-enabled sensors will play an increasing role in this field of research by offering greater temporal value and context for a better understanding of population dynamics.  Research is needed to understand how high-resolution population distribution data in both spatial and temporal domains could be used to enhance our understanding of aircraft noise impacts.

The objective of this research is to explore and demonstrate the utility of population density models for use in aviation noise impact research, and to identify implementation opportunities and future research needs.

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