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Regular PaperNo Access

Landscape-Based Extreme Heat Vulnerability Assessment

    https://doi.org/10.1142/S2345737618500185Cited by:16
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    Abstract

    Extreme heat is becoming an increasingly dangerous threat to urban residents. However, unlike hazards such as storm surges which have been well studied by agencies such as the Federal Emergency Management Commission in the United States, communities lack basic knowledge of where extreme heat threats are likely to have the most impact, and who is likely to be most affected. Here, we apply a mapping approach to identify areas of New York City where people are likely to be particularly vulnerable to extreme heat-related health effects based on both exposure to biophysical elements that exacerbate heat, and sensitivity to heat-related health impacts. Unlike most studies that develop indicators of heat vulnerability at Census-based aggregations, we disaggregate population data to a fine scale, in order to more precisely identify vulnerable communities. Using a landscape-based indicator that links exposure to properties of the urban built and natural landscape, we develop an approach for informing land-based strategies for mitigating micro-urban heat islands. Our findings indicate that African Americans and households living below the poverty line are disproportionately exposed to high surface temperatures. This study illustrates an approach for identifying multiple dimensions of vulnerability to extreme heat with improved location precision, in a way that informs spatially strategic extreme heat mitigation efforts.

    References

    • Adger, WN [2006] Vulnerability. Global Environmental Change, 16 (3): 268–281. http://doi.org/10.1016/j.gloenvcha.2006.02.006. Crossref, Google Scholar
    • Bastian, O, Grunewald K, Syrbe R-U, Walz U and Wende W [2014] Landscape services: The concept and its practical relevance. Landscape Ecology, 29 (9), 1463–1479. http://doi.org/10.1007/s10980-014-0064-5. Crossref, Google Scholar
    • Bradford, K, Abrahams L, Hegglin M and Klima K [2015] A heat vulnerability index and adaptation solutions for Pittsburgh, Pennsylvania. Environmental Science and Technology (49): 11303–11311. http://doi.org/10.1021/acs.est.5b03127. Crossref, Google Scholar
    • CDCP (2013). Heat Illness and Death — New York City, 2000–2011 (Morbidity and Mortality Weekly Report Vol. 62, No. 31). U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Google Scholar
    • Chow, WTL, Chuang W and Gober P [2012] Vulnerability to extreme heat in metropolitan phoenix: Spatial, temporal and demographic dimensions. The Professional Geographer, 64 (September 2010), 286–302. Crossref, Google Scholar
    • City of New York (2017). Cool Neighborhoods NYC. New York City. Google Scholar
    • Cutter, SL (1996). Vulnerability to environmental hazards. Progress in Human Geography. http://doi.org/10.1177/030913259602000407. Google Scholar
    • Ellis, F, Nelson F and Pincus L [1975] Mortality during heat waves in New York City July, 1972 and August and September, 1973. Environmental Research, 10 (1): 1–13. Crossref, Google Scholar
    • Fry, J, Xian G, Jin S, Dewitz J, Homer C, Yang L and Wickham J [2006] Completion of the 2006 national land cover database for the coterminous United States. Photogrammetric Engineering and Remote Sensing, 77 (9): 858–864. Google Scholar
    • Hamstead, ZA, Kremer P, Larondelle N, McPhearson T and Haase D [2016] Classification of the heterogeneous structure of urban landscapes (STURLA) as an indicator of landscape function applied to surface temperature in New York City. Ecological Indicators, 70: 574–585. http://doi.org/10.1016/j.ecolind.2015.10.014. Crossref, Google Scholar
    • Hattis, D, Ogneva-Himmelberger Y and Ratick S [2012] The spatial variability of heat-related mortality in Massachusetts. Applied Geography, 33 (1): 45–52. http://doi.org/10.1016/j.apgeog.2011.07.008. Crossref, Google Scholar
    • Horton, R, Bader D, Kushnir Y, Little C, Blake R and Rosenzweig C [2015] New York city panel on climate change 2015 report chapter 1: Climate observations and projections. Annals of the New York Academy of Sciences, 1336 (1): 18–35. http://doi.org/10.1111/nyas.12586. Crossref, Google Scholar
    • Inostroza, L, Hamstead Z, Spyra M and Qhreshi S [2019] Beyond urban–rural dichotomies: Measuring urbanisation degrees in central European landscapes using the technomass as an explicit indicator. Ecological Indicators, 96 (September 2018): 466–476. http://doi.org/10.1016/j.ecolind.2018.09.028. Crossref, Google Scholar
    • Inostroza, L, Palme M and Barrera F De [2016] A heat vulnerability index: Spatial patterns of exposure, sensitivity and adaptive capacity for santiago de chile. PLos ONE, 11 (9): 1–26. http://doi.org/10.1371/journal.pone.0162464. Crossref, Google Scholar
    • Intergovernmental Panel on Climate Change (IPCC) (2012). Glossary of Terms. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC)), 555–564. http://doi.org/10.1016/B978-0-12-396951-4.00019-0. Google Scholar
    • Johnson, DP, Stanforth A, Lulla V and Luber G [2012] Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data. Applied Geography, 35: 23–31. http://doi.org/10.1016/j.apgeog.2012.04.006. Crossref, Google Scholar
    • Klinenberg, E [2002] Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago: University of Chicago Press. Crossref, Google Scholar
    • Knowlton, K, Lynn B, Goldberg RA, Rosenzweig C, Hogrefe C, Rosenthal JK and Kinney PL [2007] Projecting heat-related mortality impacts under a changing climate in the New York City region. American Journal of Public Health, 97 (11): 2028–2034. http://doi.org/10.2105/AJPH.2006.102947. Crossref, Google Scholar
    • Kraus, MW, Rucker JM and Richeson JA [2017] Americans misperceive racial economic equality. Proceedings of the National Academy of Sciences, 114 (39): 201707719. http://doi.org/10.1073/pnas.1707719114. Crossref, Google Scholar
    • Krellenberg, K and Welz J [2017] Assessing urban vulnerability in the context of flood and heat hazard: Pathways and challenges for indicator-based analysis. Social Indicator Research, 132: 709–731. http://doi.org/10.1007/s11205-016-1324-3. Crossref, Google Scholar
    • Lo, CP, Quattrochi DA and Luvall JC [1997] Application of high-resolution GIS to assess the urban heat thermal infrared island effect remote sensing. International Journal of Remote Sensing, 18 (2). Crossref, Google Scholar
    • Lofvenhaft, K, Bjorn C and Ihse M [2002] Biotope patterns in urban areas: A conceptual model integrating biodiversity issues in spatial planning. Landscape and Urban Planning, 58: 223–240. Crossref, Google Scholar
    • Madrigano, J, Ito K, Johnson S, Kinney PL and Matte T [2015] A case-only study of vulnerability to heat wave–related mortality in New York City (2000–2011). Environmental Health Perspectives, 123 (7): 672–678. http://doi.org/10.1289/ehp.1408178. Crossref, Google Scholar
    • Manley, G [1958] On the frequency of snowfall in metropolitan England. Quarterly Journal of the Royal Meteorological Society, 84 (359): 70–72. Crossref, Google Scholar
    • Mennis, J [2002] Using geographic information systems to create and analyze statistical surfaces of population and risk for environmental justice analysis. Social Science Quarterly, 83 (1): 281–297. http://doi.org/10.1111/1540-6237.00083. Crossref, Google Scholar
    • Mennis, J [2003] Generating surface models of population using dasymetric mapping. The Professional Geographer, 55 (1): 31–42. http://doi.org/10.1111/0033-0124.10042. Google Scholar
    • NYC (2006). Deaths Associated with Heat Waves in 2006 (Special Report). New York City, Department of Health and Mental Hygiene. Google Scholar
    • NYC (2014). Heat-Related Deaths in New York City, 2013 (Epi Data Brief No. 47). New York City Department of Health and Mental Hygiene. Google Scholar
    • NYC Department of City Planning (2011). MapPLUTOTM. Retrieved September 1, 2015. http://www.nyc.gov/html/dcp/html/bytes/applbyte.shtml#pluto. Google Scholar
    • Openshaw, S and Taylor P [1979] A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In: Wrigley N (ed.) Statistical Applications in the Spatial Sciences, pp. 127–144. London: Pion. Google Scholar
    • Oppenheimer, M, Campos M, Warren R, Birkmann J, Luber G, O’Neill B and Takahashi K [2014] Emergent risks and key vulnerabilities. In: Field CBBarros VRDokken DJMach KJMastrandrea MDBilir TEWhite LL (eds.) Climate Change 2014: Impacts, Adaptationand Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK: Cambridge University Press, pp. 1039–1099. Google Scholar
    • Reid, CE, O’Neill MS, Gronlund CJ, Brines SJ, Brown DG, Diez-Roux AV and Schwartz J [2009] Mapping community determinants of heat vulnerability. Environmental Health Perspectives, 117 (11): 1730–1736. http://doi.org/10.1289/ehp.0900683. Crossref, Google Scholar
    • Rinner, C, Rinner C, Patychuk D, Bassil K, Nasr S and Gower S [2010] Vulnerability assessment for the city of Toronto. Cartography and Geographic Information Science, 37 (1): 31–44. Crossref, Google Scholar
    • Rosenthal, JK, Kinney PL and Metzger KB [2014] Intra-urban vulnerability to heat-related mortality in New York City, 1997–2006. Health and Place, 30: 45–60. http://doi.org/10.1016/j.healthplace.2014.07.014. Crossref, Google Scholar
    • Schlosberg, D [2007] Defining Enviornmental Justice: Theories, Movements and Nature. Oxford, UK: Oxford University Press. Crossref, Google Scholar
    • Schuman, SH [1972] Patterns for of urban prevention: Data during deaths New York and implications St. Louis. Environmental Research, 5: 59–75. Crossref, Google Scholar
    • Shepard, CC, Agostini VN, Gilmer B, Allen T, Stone J, Brooks W and Beck MW [2012] Assessing future risk: Quantifying the effects of sea level rise on storm surge risk for the southern shores of Long Island, New York. Natural Hazards, 60 (2): 727–745. http://doi.org/10.1007/s11069-011-0046-8. Crossref, Google Scholar
    • Sleeter, R and Gould M (2007). Geographic information systems software to remodel population data using dasymetric mapping methods. Techniques and Methods 11-C2. U.S. Department of the Interior; U.S. Geological Survey. https://pubs.usgs.gov/tm/tm11c2/. Google Scholar
    • Small, C [2009] The color of cities: An overview of urban spectral diversity. In: Gamba PHerold M (eds.) Global Mapping of Human Settlement, Boca Raton, Florida: Taylor and Francis, pp. 59–105. Crossref, Google Scholar
    • Smargiassi, A, Goldberg MS, Plante C, Fournier M, Baudouin Y and Kosatsky T [2009] Variation of daily warm season mortality as a function of micro-urban heat islands. Journal of Epidemiology and Community Health, 63 (8): 659–664. http://doi.org/10.1136/jech.2008.078147. Crossref, Google Scholar
    • Stone, B Jr. [2012] The City and the Coming Climate: Climate Change in the Places We Live. New York, NY: Cambridge University Press. Crossref, Google Scholar
    • Turner, BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L and Schiller A [2003] A framework for vulnerability analysis in sustainability science. PNAS, 100 (14): 8074–8079. http://doi.org/10.1073/pnas.1231335100. Crossref, Google Scholar
    • Uejio, CK, Wilhelmi OV, Golden JS, Mills DM, Gulino SP and Samenow JP [2011] Intra-urban societal vulnerability to extreme heat: The role of heat exposure and the built environment, socioeconomics and neighborhood stability. Health and Place, 17 (2): 498–507. http://doi.org/10.1016/j.healthplace.2010.12.005. Crossref, Google Scholar
    • van Oudenhoven, APE, Petz K, Alkemade R, Hein L and de Groot RS [2012] Framework for systematic indicator selection to assess effects of land management on ecosystem services. Ecological Indicators, 21: 110–122. http://doi.org/10.1016/j.ecolind.2012.01.012. Crossref, Google Scholar
    • Walker, G [2009] Beyond distribution and proximity: Exploring the multiple spatialities of environmental justice. Antipode, 41 (4): 614–636. http://doi.org/10.1111/j.1467-8330.2009.00691.x. Crossref, Google Scholar
    • Wilhelmi, OV and Hayden MH [2010] Connecting people and place: A new framework for reducing urban vulnerability to extreme heat. Environmental Research Letters, 5 (1): 14021. http://doi.org/10.1088/1748-9326/5/1/014021. Crossref, Google Scholar
    • Wolf, T and McGregor G [2013] The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes, 1 (August): 59–68. http://doi.org/10.1016/j.wace.2013.07.004. Crossref, Google Scholar