DOI: https://doi.org/10.14710/geoplanning.10.2.179-184

Sonic Justice and Tree Equity: Exploring Spatial Correlations between Aviation-Related Noise, Demographics, and Tree Canopy

*Mayra I Rodriguez-González orcid  -  Hartford County Extension Center, University of Connecticut, Farmington, Connecticut, USA, United States
Kevin G Torres-Garrido orcid  -  Independent Researcher, former fellow of the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Quito, Ecuador, Ecuador

Citation Format:
Abstract

The intricate relationship between aviation-related noise pollution, demographic factors, and tree canopy cover can hold significant implications for targeted interventions promoting environmental equity, sonic justice, and sustainable urban development. This study offers a geospatial exploration of these interconnections within the continental United States by employing National Transportation Noise Pollution data from the United States Department of Transportation alongside tree canopy cover from the United States Geological Survey’s National Land Cover Database and demographic data from the American Community Survey in a correlation analysis. Our analysis reveals stark disparities in noise exposure levels, notably underscoring that low-income and predominantly Hispanic neighborhood shoulder a disproportionate burden of aviation-related noise. Moreover, a correlation between aviation-related noise pollution and low tree canopy cover suggests a potential avenue for utilizing nature as a buffer against heightened noise levels. However, recognizing the delicate equilibrium between fostering a thriving tree canopy and ensuring aviation safety highlights a need for innovative urban planning solutions capable of simultaneously addressing sonic injustice and tree inequity.

Fulltext View|Download
Keywords: Noise Pollution, Sonic Justice, Tree Equity, Spatial CorrelationNoise Pollution, Sonic Justice, Tree Equity, Spatial Correlation

Article Metrics:

Article Info
Section: Planning Perspective
Language : EN
Recent articles
Assessing Smart Growth Implementation and Its Impact on Urban Sprawl: A Content and GIS-Based Analysis Modelling Groundwater Vulnerability to Contamination using DRASTIC Model through Geospatial Techniques over Northern Kwazulu-Natal, South Africa Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia Sonic Justice and Tree Equity: Exploring Spatial Correlations between Aviation-Related Noise, Demographics, and Tree Canopy More recent articles
Most cited articles
LAND PRICE MAPPING OF JABODETABEK, INDONESIA SPATIAL VARIETY AND DISTRIBUTION OF TRADITIONAL MARKETS IN SURAKARTA AS POTENTIAL FACTORS IN IMPROVING SPATIAL-BASED MANAGEMENT PEMANFAATAN CITRA SATELIT UNTUK PENENTUAN LAHAN KRITIS MANGROVE DI KECAMATAN TUGU, KOTA SEMARANG GIS-BASED ANALYSIS FOR ASSESSING LANDSLIDE AND DROUGHT HAZARD IN THE CORRIDOR OF MT. MERAPI AND MT. MERBABU NATIONAL PARK, INDONESIA SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY More cited articles

  1. Alquezar, R. D., & Macedo, R. H. (2019). Airport noise and wildlife conservation: What are we missing?. Perspectives in ecology and conservation, 17(4), 163-171.https://doi.org/10.1016/j.pecon.2019.08.003">[Crossref] 

  2. Basner, M., Clark, C., Hansell, A., Hileman, J. I., Janssen, S., Shepherd, K., & Sparrow, V. (2017). Aviation noise impacts: state of the science. Noise & health, 19(87), 41. https://doi.org/10.4103%2Fnah.NAH_104_16">[Crossref] 

  3. Collins, T. W., Nadybal, S., & Grineski, S. E. (2020). Sonic injustice: Disparate residential exposures to transport noise from road and aviation sources in the continental United States. Journal of Transport Geography, 82, 102604. https://doi.org/10.1016/j.jtrangeo.2019.102604">[Crossref] 

  4. Esri. (2011). ArcGIS Pro 3.1.0. Redlands, CA: Environmental Systems Research Institute. https://pro.arcgis.com">

  5. Esri. (2023). How zonal statistics tools work. Redlands, CA: Environmental Systems Research Institute. https://doc.
     arcgis.com/en/arcgis-online/analyze/how-zonal-statistics-works.htm

  6. Kaltenbach, M., Maschke, C., & Klinke, R. (2008). Health consequences of aircraft noise. Deutsches Ärzteblatt International, 105(31-32), 548. https://doi.org/10.3238%2Farztebl.2008.0548">[Crossref] 

  7. Korol, E., Shushunova, N., Feoktistova, O., Shushunova, T., & Rubtsov, O. (2018). Technical and economical factors in green roof using to reduce the aircraft noise. In MATEC Web of Conferences 170 (01081). EDP Sciences. https://doi.org/10.1051/matecconf/201817001081">[Crossref] 

  8. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2023a). IPUMS National Historical Geographic Information System: Version 18.0 [2020 Census Block Groups – Shapefile]. Minneapolis, MN: IPUMS. http://doi.org/10.18128/D050.V18.0">[Crossref] 

  9. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2023b). IPUMS National Historical Geographic Information System: Version 18.0 [2017-2021 American Community Survey – Tabular]. Minneapolis, MN: IPUMS. http://doi.org/10.18128/D050.V18.0">[Crossref] 

  10. Metz, I. C., Ellerbroek, J., Mühlhausen, T., Kügler, D., & Hoekstra, J. M. (2020). The bird strike challenge. Aerospace, 7(3), 26. https://doi.org/10.3390/aerospace7030026">[Crossref]

  11. Microsoft Corporation. (2023). Microsoft Excel v. 2311. https://office.microsoft.com/excel">

  12. Mobini, M., & Sabzehparvar, M. (2022). Visual landing based on the human depth perception in limited visibility and failure of avionic systems. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4320101">[Crossref] 

  13. National Atlas of the United States. (2014). Global Map: Airports of the United States, 2014. National Atlas of the United States. https://purl.stanford.edu/hh676zz3630">

  14. Ow, L. F., & Ghosh, S. (2017). Urban cities and road traffic noise: Reduction through vegetation. Applied Acoustics, 120, 15-20. https://doi.org/10.1016/j.apacoust.2017.01.007">[Crossref] 

  15. Penman-Aguilar, A., Talih, M., Huang, D., Moonesinghe, R., Bouye, K., & Beckles, G. (2016). Measurement of health disparities, health inequities, and social determinants of health to support the advancement of health equity. Journal of public health management and practice: JPHMP, 22(Suppl 1), S33. https://doi.org/10.1097%2FPHH.0000000000000373">[Crossref] 

  16. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org">

  17. Riley, C. B., & Gardiner, M. M. (2020). Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities. PLoS One, 15(2), e0228499. https://doi.org/10.1371/journal.pone.0228499">[Crossref] 

  18. Trudeau, C., King, N., & Guastavino, C. (2023). Investigating sonic injustice: A review of published research. Social Science & Medicine, 115919. https://doi.org/10.1016/j.socscimed.2023.115919">[Crossref] 

  19. United States Department of Transportation – Bureau of Transportation Statistics. (2022). National Transportation Noise Map. https://www.bts.gov/geospatial/national-transportation-noise-map">

  20. United States Geological Survey. (2023). NLCD CONUS 2020 Tree Canopy Cover. v2021-4. https://www.mrlc.gov">


Last update:

No citation recorded.

Last update: 2024-11-22 12:56:07

No citation recorded.