DOI QR코드

DOI QR Code

폭염 취약지역과 건강 피해 발생의 공간적 일치성에 따른 지역 유형 분석

Analysis of regional type according to spatial correspondence between heat wave vulnerable areas and health damage occurrence

  • 황희수 (부산대학교 도시공학과) ;
  • 최지윤 (부산대학교 도시공학과) ;
  • 강정은 (부산대학교 도시공학과)
  • Hee-Soo HWANG (Dept. of Urban Planning and Engineering, Pusan National University) ;
  • Ji Yoon CHOI (Dept. of Urban Planning and Engineering, Pusan National University) ;
  • Jung Eun KANG (Dept. of Urban Planning and Engineering, Pusan National University)
  • 투고 : 2023.02.14
  • 심사 : 2023.03.08
  • 발행 : 2023.03.31

초록

본 연구는 폭염 취약지역을 도출하고, 폭염 피해와의 공간적 일치성 분석을 통해 공간 유형화 및 정책적 방향성에 대해 논의하고자 한다. 연구 방법은 IPCC의 기후변화 취약성 평가와 공간통계 비교분석을 활용하였으며, 폭염이 가장 극심했던 2018년을 포함하는 5개년(2015~2019)의 전국 시군구를 대상으로 하였다. 폭염 취약성은 다양한 요소 중 폭염 영향을 나타내는 폭염일수(노출)가 가장 큰 영향을 미치고 있었으며, 폭염에 대한 민감도와 적응 능력은 지역의 특성에 따라 경향성이 나타나는 것으로 확인되었다. 폭염 취약성과 피해의 관계는 공간적 일치성을 통해 4개 유형으로 구분하였으며, 취약성과 피해가 정의 관계를 가지는 Hot to Hot, Cold to Cold 유형과 역의 관계를 가지는 Hot to Cold, Cold to Hot 유형을 도출하였다. 이는 유형별로 지역의 특성과 현황이 상이하므로 유형에 따라 개선을 위한 정책과 연구의 방향성을 달리 설정해야 한다는 시사점을 남긴다. 해당 연구는 폭염 취약성과 피해를 함께 고려하여 지역을 유형화하고, 유형별 대응 방향성에 대해 살펴본 점에서 추후 폭염 관련 정책 수립에 기초자료로 활용되기를 기대한다.

This study aimed to identify heat wave vulnerable areas and discuss spatial typology and policy directions through spatial coincidence analysis of heat wave damage. By utilizing the climate change vulnerability assessment of the Intergovernmental Panel on Climate Change (IPCC) and Spatial Statistics Comparison Analysis, this study examined cities, counties, and districts in South Korea for five years (2015-2019), including 2018, when the heat wave was most extreme. It was determined that the number of heat wave days (exposure) was the most impactful among various factors for heat wave vulnerability. Sensitivity and adaptive capacity to heat waves were found to vary according to regional characteristics. The relationship between heat wave vulnerability and damage was categorized into four types through spatial coherence. Hot to Hot and Cold to Cold types have a positive relationship between vulnerability and damage, while Hot to Cold and Cold to Hot types have a negative relationship. The findings suggest that since different types of regions have distinct characteristics and conditions, policies and research for improvement should be directed to address each region separately. This study may be used as basic data for establishing heat-related policies in the future, as it categorizes regions by considering both heat vulnerability and damage and examines the direction of response by type.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원의 환경보건디지털 조사기반 구축기술개발사업의 지원을 받아 연구되었습니다.(2021003330002)

참고문헌

  1. Anselin, L. 1995. Local Indicators of Spatial Association-LISA, Geographical Analysis 27(2):93-115  https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  2. Bae, H.J., D.W. Jung, S.Y. Gan, J.Y. Park, and Y.H. Lim. 2017. Climate Change Response Strategies Based on Assessment and Prediction of Health Impacts from Abnormal Temperatures. Climate and Environmental Policy 2017:1-137. 
  3. Bae, S.H. 2018. Spatial Clustering Analysis of Fire in Gangwon-Do. Journal of Korean Association of Geographic Information Studies 21(3):93-103. 
  4. Bae, W.K. and S.H. Park. 2021. Deriving heat island vulnerable areas according to changes in heat island distribution characteristics and strength cluster analysis. Seoul Studies 22(4):43-63.  https://doi.org/10.23129/SEOULS.22.4.202112.43
  5. Barreca, A.I. 2012. Climate change, humidity, and mortality in the United States. Journal of Environmental Economics and Management 63(1):19-34.  https://doi.org/10.1016/j.jeem.2011.07.004
  6. Basu, R. and J.M. Samet. 2002. Relation between Elevated Ambient Temperature And Mortality: A Review of the Epidemiologic Evidence. Epidemoilogic Reviews 24(2):190-202.  https://doi.org/10.1093/epirev/mxf007
  7. Chae, S.M. 2020. Adaptattion to the Health Effects of Heat Waves in Sensitive Groups. Korea Institute for Health and Social Affairs 
  8. Chae, Y.R. 2019. Analysis of the Impact of Heat Wave on Social and Economic Conditions through Civic Participation. Korea Environment Institute. 
  9. Cho, H.M., J.H. Ha, and S.G. Lee. 2019. Exploring Physical Environments, Demographic and Socioeconomic Characteristics of Urban Heat Island Effect Areas in Seoul, Korea. Journal of the Korean Regional Science Association 35(4):61-73. 
  10. Cho, S.A. and G.H. Lee. 2017. Spatial Mismatch between the Elderly Populated Areas and Elderly Demanding Facilities Using Spatial Statistics. Journal of the Korean Urban Geographical Society 20 (2):99-112.  https://doi.org/10.21189/JKUGS.20.2.8
  11. Choi, H.R. and W.S. Han. 2021. Heat Wave Disaster Vulnerability Assessment and Identification of Vulnerable Area Characteristics Considering Medical Vulnerability. The Korea Spatial Planning Review 110:63-79. 
  12. Choi, Y.S., J.W. Kim, and U. Lim. 2018. An Analysis on the Spatial Patterns of Heat Wave Vulnerable Areas and Adaptive Capacity Vulnerable Areas in Seoul. Journal of Korea Planning Association 53(7):87-107.  https://doi.org/10.17208/jkpa.2018.12.53.7.87
  13. Choi, Y.W., T.K. Yang, and H.S. Choi. 2017. Operation and management of climate change support programs for vulnerable groups. Korea Adaptation Center for Climate Change, Korea Environment Institute. 
  14. Eum, J.H. 2016. Vulnerability Assessment to Urban Thermal Environment for Spatial Planning- A Case Study of Seoul, Korea-. Journal of the Korean Institute of Landscape Architecture 44(4):109-120.  https://doi.org/10.9715/KILA.2016.44.4.109
  15. Fussel, H.M. and R.J. Klein. 2006. Climate change vulnerability assessments: an evolution of conceptual thinking. Climatic change 75(3):301-329.  https://doi.org/10.1007/s10584-006-0329-3
  16. Garrsen, J., C. Harmsen, and J.D. Beer. 2005. The Effect of the Summer 2003 Heat Wave on Mortality in the Netherlands. Eurosurveillance, 10(7-9):165-167.  https://doi.org/10.2807/esm.10.07.00557-en
  17. Hagen, A. 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science 17(3):235-249.  https://doi.org/10.1080/13658810210157822
  18. Hagen-Zanker, A. 2009. An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science 23(1):61-73.  https://doi.org/10.1080/13658810802570317
  19. Hong, Y.C. 2018. Estimation of acute.chronic disease and mortality in extreme weather (heat.cold) in Korea. Korea Disease Control and Prevention Agency. 
  20. IPCC. 2007. Climate Change 2007: Impact, adaptation and vulnerability, Parry, M. et al,. Eds., Cambridge University Press, Cambridge, UK. 
  21. Johnson, H., R.S. Kovats, G. McGregor, J. Stedman, M. Gibbs, H. Walton, and E. Black. 2005. The Impact of the 2003 Heat Wave on Mortality and Hospital Adissions in England. Health Statistics Quarterly, 25:6-11. 
  22. Jung, M.R. 2022. Analysis for Deriving Priority Management Areas Vulnerable to Heatwaves in Incheon Using Landsat-8 and Sentinel-2A Satellite Images. Local Informatization 132(0):14-19. 
  23. Kang, J.E. and M.J. Lee. 2012. Assessment of Flood Vulnerability to Climate Change Using Fuzzy Model and GIS in Seoul. Joornal of the Korean Association of Geographic Information Studies 15(3):119-136.  https://doi.org/10.11108/kagis.2012.15.3.119
  24. Khamis, N., T.C. Sin and G.C. Hock. 2018. Segmentation of residential customer load profile in peninsular Malaysia using jenks natural breaks. 2018 IEEE 7th international Conference on Power and Energy(PECon). Kuala Lumpur, Malaysia, Malaysia, Dec, 3-4, 2018. pp. 128-131 
  25. Kim, B.E., M.H. Lee, D.G. Lee, and J.Y. Kim. 2020. Study on the Characteristics of Spatial Relationship between Heat Concentration and Heat-deepening Factors Using MODIS Based Heat Distribution Map. Korean Journal of Remote Sensing 36(5-4):1153-1166.  https://doi.org/10.7780/KJRS.2020.36.5.4.2
  26. Kim, D.W., J.H. Chung, J.S. Lee, and J.S. Lee. 2014. Characteristics of Heat wave Mortatlity in Korea. Atmosphere. Korean Meteorological Society 24(2):225-234.  https://doi.org/10.14191/Atmos.2014.24.2.225
  27. Kim, D.W., J.E. Kim, C.R. Jang, and M.Y. Jang. 2021. Assessment of Heatwave Vulnerability in Korea Considering Socio -economic Indices. Journal of The Korean Society of Hazard Mitigation 21 (5):39-47.  https://doi.org/10.9798/KOSHAM.2021.21.5.39
  28. Kim, G.H., J.H. Ryu, M.J. Hong, and N.R. Im. 2022. A study on the Types of Heat Wave Vulnerabilities and Factors of Heat Wave Patients by Region. Journal of Environmental Policy and Administration 30(3):147-175.  https://doi.org/10.15301/jepa.2022.30.3.147
  29. Kim, J.S. and H.Y. Kim. 2020. Analysis on the Characteristics of Heat Wave Vulnerable Areas Using Landsat 8 Data and Vulnerability Assessment Analysis. Journal of the Korean Association of Geographic Information Studies 23(1):1-14. 
  30. Kim, J.S., D.K. Lee, S.Y. Sung, S.G. Jeong, and J.H. Park. 2015. Study of vulnerable district characteristics on urban heat island according to land use using normalized index. Journal of Korea Planning Association 50(5):59-72.  https://doi.org/10.17208/jkpa.2015.08.50.5.59
  31. Kim, J.Y., D.G. Lee, and K. Jan. 2009. Characteristics of Heat Acclimatization for Major Korean Cities. Atmosphere 19(4):309-318. 
  32. Kim, K. and J.H. Eum. 2018. Policies for Improving Thermal Environment Using Vulnerability Assessment-A Case Study of Daegu, Korea-. Journal of the Korean Association of Geographic Information Studies 21(2):1-23. 
  33. Kim, S.M and N.E. Kim. 2012. A Study on Vulnerability Assessment to Climate Change: Focused on 22 Municipalities of JeollaNam-Do in South Korea. Korean Governance Review 19(2):99-123.  https://doi.org/10.17089/kgr.2012.19.2.005
  34. Klinenberg, E. 2003. Review of Heat Wave: Social Autopsy of Disaster in Chicago. New England Journal of Medeicine 348(7):666-667.  https://doi.org/10.1056/NEJM200302133480721
  35. Koh, J.K. 2011. A Study on Climate Change Vulnerability Types and Adaptation in Local Government; With Cases from Gyeonggi-Do. Korean society and public administration 22(2):93-118. 
  36. Koh, J.K. and H.S. Kim. 2010. A study on Local Vulnerability Assessment to Climate Change: the Case of Municipalities of Gyeonggi-Do. Journal of Environment policy and Administration 18(2):79-105. 
  37. KOICD. https://www.koicd.kr/mobile/kcd/list.do. (Accessed February 02, 2023) 
  38. Koo, Y.S., J.E. Kim, J.S. Kim and S.H. Lee. 2015. Study on the Improvement of Adaptation Ability by Vulnerability Analysis of Heat Wave - the Case of Busan Metropolitan City. Journal of the Korean Regional Development Association 27(5):331-348. 
  39. Koppe, C., S. Kovats, G. Jendritzky and B. Menne. 2004. Heat-waves: risks and responses. World Health Organization. Regional Office for Europe. 
  40. Korea Disease Control and Prevention Agency. 2020. 2020 Annual Report on Notified Patients with Heat-related illness in Korea. 
  41. Lee, G.J. and J.W. Cha. 2019. A study on Identification of the Heat Vulnerability Area-Case Study in ChungCheongnamdo-. Journal Of the Korean Society Of Rural Planning 25(1):67-74.  https://doi.org/10.7851/ksrp.2019.25.1.067
  42. Lee, N.Y., Y.S. Cho and J.Y. Lim. 2014. Effect of Climate Change on Mortatlity Rate Analysis of Vulnerable Populations. Health and Social Welfate Review 34(1):456-484.  https://doi.org/10.15709/hswr.2014.34.1.456
  43. Lee, S.H. and J.E. Kang. 2018. Urban Flood Vulnerability and Risk Assessments for Applying to Urban Planning. Journal of Korea Planning Association 53(5):185-206.  https://doi.org/10.17208/jkpa.2018.10.53.5.185
  44. Lee, S.H., J.E. Kang and H.J. Bae. 2022. Spatial Exploration of Areas Affected by Heatwaves Using Big Data: The Impact on Urban Residents' Activities. Journal of the Korean Society of Hazard Mitigation 22(3):59-67.  https://doi.org/10.9798/KOSHAM.2022.22.3.59
  45. Lee, S.H., J.E. Kang, H.J. Bae and D.K. Yoon. 2015. Vulnerability Assessment of the Air Pollution Using Entropy Weights : Focused on Ozone. Journal of The Korean Association of Regional Geographers 21(4):751-763. 
  46. Lee, S.I. 2001. Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I. Journal of Geographical Systems 3:369-385  https://doi.org/10.1007/s101090100064
  47. Lee, S.M., I. Kweon and Y.J. Kim. 2019. A study on the Influence of Urban Environment on the Generation of Thermal Diseases. The Journal of the Korea Contents Association 19(12):84-92. 
  48. Lee, Y.M., W.J. Park and K.Y. Yu. 2014. A Study on Detection Methodology for Influential Areas in Social Network using Spatial Statistical Analysis Methods. Journal of the Korean Society for Geospatial Information System 22(4):21-30  https://doi.org/10.7319/kogsis.2014.22.4.021
  49. Lim, H.J., S.H. Kim and J. Heo. 2019. Outliers of the Official Land Price and Their Price Trend by Floating Population Data: A Case of Gangnam-Gu, Seoul. Journal of Korean Society for Geospatial Information Science 27(5):41-49.  https://doi.org/10.7319/kogsis.2019.27.5.041
  50. National Health Insurance Sharing Service (NHISS). Customized DB. https://nhiss.nhis.or.kr/bd/ab/bdabd003cv.do. (Accessed February 02, 2023). 
  51. National Institute of Meteorological Sciences. 2020. Climate Change Forecast on Korea 2020. 
  52. Nitschke, M., G.R. Tucker, A.L. Hansen, S. Williams, Y. Zhang and P. Bi. 2011. Impact of two recent extreme heat episodes on morbidity and mortality in Adelaide, South Australia: a case-series analysis. Environmental Health 10:42. 
  53. Park, D.S., B.Y. Park and E.H. Jung. 2017. Guidelines for the VESTAP-based Climate Change Vulnerability Assessment. Journal of Climate Change Research 8(4):339-346.  https://doi.org/10.15531/ksccr.2017.8.4.339
  54. Park, J.C. and Y.R. Chae. 2020. Analysis of heat-related illness and excess mortality by heat waves in South Korea in 2018. Journal of the KOrean Geographical Socitey 55(4):391-408. 
  55. Park, J.H. 2015. Healthcare Big Data Status and Utilization Prospects. Healthcare policy forum 13(4):56-62. 
  56. Park, W.J., S.W. Eo and K.Y. Yu. 2015. Analyzing Spatial Correlation between Location-Based Social Media Data and Real Eestates Price Index through Rasterization. Journal fo the Korean Society for Geospatial Information System 23(1):23-29  https://doi.org/10.7319/kogsis.2015.23.1.023
  57. Petitti, D.B., S.L. Harlan, G. Chowell-Puente and D. Ruddell. 2013. Occupation and Environmental Heat-Associated Deaths in Maricopa County, Arizona: A Case- Control Study. PLOS ONE 8(5):e62596. 
  58. PHE. 2015. Heatwave plan for England - Making the case: the impact of heat on health-now and in the future. NHS England.
  59. Seo, J.S. and W.S. Han. 2019. Exploring the Relationship Between Floods and Socially Vulnerable Groups: The Case of Jeju. Journal of the Korean Society of Hazard Mitigation 19(4):103-113  https://doi.org/10.9798/KOSHAM.2019.19.4.103
  60. Seong, J.H., K.R. Lee, Y.S. Kwon, Y.K. Han and W.H. Lee. 2020. A Study on Identification of the Heat Vulnerability Area Considering Spatial Autocorrelation - Case Study in Daegu. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 38(4):295-304. 
  61. Singh, A., P.K. Pathak, R.K. Chauhan and W. Pan. 2011. Infant and Child Mortality in India in the Last Two Decades: A Geospatial Analysis. PLOS ONE 6(11):e26856. 
  62. Song, W., C. Wang, W. Chen, X. Zhang, H. Li and J. Li. 2020. Unlocking the spatial heterogeneous relationship between Per Capita GDP and nearby air quality using bivariate local indicator of spatial association. Resource, Conservation & Recycling 160:104880. 
  63. Weibei, D., Y. Ren, Q. Wu, S. Ruan, Y. Chen, D. Bloyet and J.M. Constans. 2007. Fuzzy Kappa for the agreement measure of fuzzy classifications. Neurocomputing 70:726-734.  https://doi.org/10.1016/j.neucom.2006.10.007
  64. Yang, H.J. and H.Y. Yoon. 2019. Performance Efficiency of Urban Heat Wave Response Policy:Targeting the number of people suffering from heat-related diseases and medical expenditures in Seoul. Journal of the Korean Urban Management Association 32(1):31-45.  https://doi.org/10.36700/KRUMA.2019.03.32.1.31