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Suggested Citation:"6 References." National Academies of Sciences, Engineering, and Medicine. 2020. Evaluating the Use of Spatially Precise Diurnal Population Data in Aviation Noise Studies. Washington, DC: The National Academies Press. doi: 10.17226/25871.
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Page 57
Page 58
Suggested Citation:"6 References." National Academies of Sciences, Engineering, and Medicine. 2020. Evaluating the Use of Spatially Precise Diurnal Population Data in Aviation Noise Studies. Washington, DC: The National Academies Press. doi: 10.17226/25871.
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Page 58

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57 6 References Ahola, T., K. Virrantaus, J. M. Krisp, and G. J. Hunter. A Spatiotemporal Population Model to Support Risk Assessment and Damage Analysis for Decision‐Making. International Journal of Geographical Information Science, Vol. 21, No. 8, 2007, pp. 935–953. https://doi.org/10.1080/13658810701349078. Bell, K. D. Comparing Methods for Estimation of Daytime Population in Downtown Indianapolis, Indiana. Master’s thesis. Indiana University, 2011. Bhaduri, B. Population Distribution During the Day. Encyclopaedia of GIS, Springer, Boston, Mass., 2008, pp. 880–885. https://doi.org/10.1007/978-0-387-35973-1_1005. Deville, P., C. Linard, S. Martin, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem. Dynamic Population Mapping Using Mobile Phone Data. Proceedings of the National Academy of Sciences of the United States of America, Vol. 111, No. 45, 2014, pp. 15888– 15893. https://doi.org/10.1073/pnas.1408439111. Dhondt, S., C. Beckx, B. Degraeuwe, W. Lefebvre, B. Kochan, T. Bellemans, L. Int Panis, C. Macharis, and K. Putman. Health Impact Assessment of Air Pollution Using a Dynamic Exposure Profile: Implications for Exposure and Health Impact Estimates. Environmental Impact Assessment Review, Vol. 36, 2012, pp. 42–51. https://doi.org/10.1016/j.eiar.2012.03.004. Freire, S. Modeling of Spatiotemporal Distribution of Urban Population at High Resolution–Value for Risk Assessment and Emergency Management. Geographic Information and Cartography for Risk and Crisis Management, Springer, Berlin, Heidelberg, 2010, pp. 53– 67. https://doi.org/10.1007/978-3-642-03442-8_4. Freire, S. C. Modeling Daytime and Nighttime Population Distributions in Portugal Using Geographic Information Systems. Doctoral dissertation. University of Kansas, 2007. Freire, S., A. J. Florczyk, and S. Ferri. Modeling Day-and Nighttime Population Exposure at High Resolution: Application to Volcanic Risk Assessment in Campi Flegrei. Presented at Information Systems for Crisis Response and Management Conference, 2015. Fung, B. Verizon, AT&T, T-Mobile and Sprint suspend selling of customer location data after prison officials were caught misusing it. The Washington Post, June 19, 2018, online. Greaves, S., A. Collins, and N. Bhatia. Disaggregate Assessments of Population Exposure to Aircraft Noise. University of Sydney, Sydney, NSW, 2006. Greger, K. Spatiotemporal Building Population Estimation for Highly Urbanized Areas Using GIS. Transactions in GIS, Vol. 19, No. 1, 2015, pp. 129–150. https://doi.org/10.1111/tgis.12086. Kobayashi, T., R. M. Medina, and T. J. Cova. Visualizing Diurnal Population Change in Urban Areas for Emergency Management. Professional Geographer, Vol. 63, No. 1, 2011, pp. 113– 130. https://doi.org/10.1080/00330124.2010.533565.

58 McKenzie, B., W. Koerber, A. Fields, M. Benetsky, and M. Rapino. Commuter-adjusted population estimates: ACS 2006-10. Washington, DC: Journey to Work and Migration Statistics Branch. U.S. Census Bureau, 2010. McPherson, T. N., and M. J. Brown. Estimating Daytime and Nighttime Population Distributions in U.S. Cities for Emergency Response Activities. (No. LA-UR-03-8388) Los Alamos National Lab, Los Alamos, N.M., 2003. Eagan, M. E., and R. Gardner. ACRP Synthesis 16: Compilation of Noise Programs in Areas Outside DNL 65. Transportation Research Board, Washington, D.C., 2009. https://doi.org/10.17226/14271. Rapino, M., et al. How Can We Best Visualize Worker Movement Throughout the Day? U.S. Census Paper No. 2014-01, 2014. Rose, A. N., E. M. Weber, J. J. Moehl, M. L. Laverdiere, H. L. Yang, M. C. Whitehead, K. M. Sims, N. E. Trombley, C. A. Whitlock, and B. L. Bhaduri. LandScan USA 2017 Raster Data Set, Oak Ridge National Laboratory, 2018. Schultz, T. J. Synthesis of Social Surveys on Noise Annoyance. Journal of the Acoustical Society of America, Vol. 64, No. 2, 1978, pp. 377–405. https://doi.org/10.1121/1.382013. Sleeter, R., and N. Wood. Estimating Daytime and Nighttime Population Density for Coastal Communities in Oregon. Proc., Annual Conference of the Urban and Regional Information Systems Association, Vancouver, Canada, 2006, September, pp. 26–29. Vovsha, P., J. E. Hicks, R. Anderson, G. Giaimo, and G. Rousseau. Integrated Model of Travel Demand and Network Simulation. Proc., 6th Conference on Innovations in Travel Modeling, Denver, Colo., 2016. Williams, N. E., T. A. Thomas, M. Dunbar, N. Eagle, and A. Dobra. Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data. PLoS One, Vol. 10, No. 7, 2015, p. e0133630. https://doi.org/10.1371/journal.pone.0133630.

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Knowing where people are at different times of the day potentially enables the design of airspace routes that minimize the environmental impact to a shifting population on the ground.

The TRB Airport Cooperative Research Program's ACRP Web-Only Document 48: Evaluating the Use of Spatially Precise Diurnal Population Data in Aviation Noise Studies examines the potential role of spatiotemporal population data in aviation noise studies.

Aviation noise analysis has traditionally focused on modeling the noise from an average day of operations. There is potential to move from this static approach to identifying high-aircraft-noise areas to a dynamic method of assessing aircraft noise experienced by people where they are as they move about the day and night.

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