Abstract
Environmental characteristics, urban morphologies, and socio-economic factors co-evolve as urban areas grow. Investigating the interactions between these components is important as they directly impact community health and city sustainability. This research examines correlations among building density, temperature distribution, and socio-economic composition within an urban setting. The research is conducted as a case study of the Los Angeles Metropolitan Statistical Area (LAMSA). The results demonstrate that varying degrees of correlations exist among the LAMSA’s built urban environment, natural environment, and demographic composition. Further, the incorporation of community health data underscores marginalized groups that may potentially experience disproportionate effects of evolving climactic events. Finally, as the general framework of analysis can be applied to other cities, this research provides a set of steps in understanding the connections among these domains to shift towards more sustainable urban futures.
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References
DATAUSA. Profile of Los Angeles, Long Beach and Anaheim. https://datausa.io/profile/geo/los-angeles-long-beach-anaheim-ca. Accessed 23 May 2022
U.S. Census Bureau. U.S Tract 2020 data (2020). https://www.census.gov/geographies/mapping-files.html. Accessed 15 June 2022
U.S. Census Bureau. U.S. Census Metropolitan Statistical Area map. 2020 State-based Metropolitan and Micropolitan Statistical Areas Maps (2020). https://www.census.gov/geographies/reference-maps/2020/demo/state-maps.html. Accessed 18 June 2022
New, J., Bass, B., Adams, M., Berres, A., Luo., X.: Los Angeles County Archetypes in Weather Research and Forecasting (WRF) Region from ORNL’s AutoBEM (2021). https://doi.org/10.5281/zenodo.4726136
Hengl, T., Parente, L.: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2020 at 1-km (v1.1) (2021). https://doi.org/10.5281/zenodo.4527052
Multi-Resolution Land Characteristics (MRLC) Consortium. The National Land Cover Database (NLCD) (2019). https://www.mrlc.gov/. Accessed 24 May 2022
Multi-Resolution Land Characteristics (MRLC) Consortium. National Land Cover Database (NLCD) Class Legend and Description (2022). https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description. Accessed 24 May 2022
County of Los Angeles Enterprise GIS Data Gallery. https://egis-lacounty.hub.arcgis.com/. Accessed 23 June 2022
McClave, J., Benson, G., Sincich, T.: Statistics for Business and Economics, 13th edn. Pearson, New York (2017)
USGS. LP DAAC2Disk Download Manager (HTTP/FTP Download Tool) User Guide (2014). https://lpdaac.usgs.gov/tools/daac2diskscripts/#documentation. Accessed 18 June 2022
Getis, A., Ord, J.K.: The analysis of spatial association by use of distance statistics. Geogr. Anal. 24(3), 189–206 (1992)
Statista. Health insurance status distribution of the total population of California in 2020 (2022). https://www.statista.com/statistics/238714/health-insurance-status-of-the-total-population-of-california/
Office of Disease Prevention and Health Promotion (ODPHP). Access to Health Care (2020). https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-health/interventions-resources/access-to-health#11
U.S. Environmental Protection Agency (EPA). Heat Island Impacts (2022). https://www.epa.gov/heatislands/heat-island-impacts. Accessed 23 June 2022
Southern California Association of Governments (SCAG) (2020). Extreme Heat & Public Health Report, https://scag.ca.gov/sites/main/files/file-attachments/extremeheatpublichealthreportfinal_09302020.pdf?1634674354
Cardona, O.D., et al.: Determinants of risk: exposure and vulnerability. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Field, C.B., et al., (eds.) A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 65–108 (2012)
Gencer, E., et al.: Disasters and risk in cities. In: Rosenzweig, C., Solecki, W., Romero-Lankao, P., Mehrotra, S., Dhakal, S., Ali Ibrahim, S. (eds.) Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network, pp. 61–98. Cambridge University Press, New York (2018)
Wan, Z.: Collection-6 MODIS land surface temperature products users’ guide. ICESS, University of California, Santa Barbara (2007). https://icess.eri.ucsb.edu/modis/LstUsrGuide/usrguide_8dtil.html#alg
Hu, L., Brunsell, N.A., Monaghan, A.J., Barlage, M., Wilhelmi, O.V.: How can we use MODIS land surface temperature to validate long-term urban model simulations? J. Geophys. Res.: Atmos. 119(6), 3185–3201 (2014). https://doi.org/10.1002/2013jd021101
Jonsson, P., Eklundh, L.: TIMESAT-a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833–845 (2004)
“Hot Spot Analysis (Getis-Ord Gi*) (Spatial Statistics).” Hot Spot Analysis (Getis-Ord Gi*) (Spatial Statistics)-ArcGIS Pro, https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/hot-spot-analysis.htm
Singh, R., Dumas, M.A.: Esxploring the spatial relationship between demographic indicators and the built environment of a city. In: Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, vol 1512. Springer, Cham (2022) https://doi.org/10.1007/978-3-030-96498-6_27
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Distribution by race (1) Asian, (2) Hispanic, (3) Black, (4) White; The coefficients r to the dependent variable of Model 1 and 2 is presented in each Figure.
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Kim, E. (2022). Investigating Relationships in Environmental and Community Health: Correlations of Environment, Urban Morphology, and Socio-economic Factors in the Los Angeles Metropolitan Statistical Area. In: Doug, K., Al, G., Pophale, S., Liu, H., Parete-Koon, S. (eds) Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation. SMC 2022. Communications in Computer and Information Science, vol 1690. Springer, Cham. https://doi.org/10.1007/978-3-031-23606-8_23
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