지역 특성을 고려한 무더위쉼터의 입지특성 분석 및 평가 모델 개발
Development of a Model for Analylzing and Evaluating the Suitability of Locations for Cooling Center Considering Local Characteristics
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초록
기후변화로 인한 폭염은 취약계층의 건강 피해를 급격히 증가시키고 있으며, 이를 예방하기 위하여 국가, 광역, 기초지자체는 기후위기 적응대책을 수립하고 있다. 폭염 피해를 줄이기 위한 대표적인 기후위기 적응대책은 무더위쉼터 개소 수 확대이다. 단기간에 효과가 높아 전라북도를 제외한 대부분의 광역지자체에서는 해당 사업을 적응대책으로 포함하고 있다. 하지만 예산 및 비예산 등에 따라 무더위쉼터로서 선정 기준이 달라 무더위쉼터의 이용률 및 효과가 모두 다르다. 따라서 본 연구에서는 지자체에서 적응대책 이행을 위해 무더위쉼터 확장 시 가능성이 높은 지역을 예측 및 평가할 수 있는 로지스틱 회귀분석 모델을 개발하였다. 원도심과 신도시의 공존 등으로 다양한 폭염 취약 환경으로 구성된 인천광역시를 대상으로 사회경제적환경적 차이를 고려하여 강화옹진군과 이외의 지역으로 구분하여 무더위쉼터 가능 지역을 예측하는 로지스틱 모델을 개발하였다. 연구 결과, 강화옹진군 지역의 통계 모델에서는 지표면 온도가 높을수록, 65세 이상 고령자수가 많을수록 무더위쉼터 가능성이 높은 것으로 나타났으며, 약 80.93%의 예측 정확도를 나타냈다. 강화옹진군 이외의 지역에 대해서는 지표면 온도가 높을수록, 65세 이상 고령자 수가 많을수록, 30년 이상인 노후 주택으로부터의 거리가 가까울수록, 공공시설로부터의 거리가 가까울수록 무더위쉼터 가능성이 높은 것으로 나타났으며, 약 89.08%의 예측 정확도로 나타났다. 개발된 로지스틱 회귀모형은 지역의 특성을 고려하여 무더위쉼터로서 가능성이 높은 지역을 예측 및 평가할 수 있으며, 추후 무더위쉼터 추가 지정 시 우선순위 선정 및 관리에 활용할 수 있을 것으로 기대한다.
Abstract
Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into GanghwaOngjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the GanghwaOgjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.
참고문헌 (32)
- Bae M-K, "Analysis on the spatial relationship between the residential area of the vulnerable groups and the hazardous area during the heat wave", Environmental Policy, 28(3), pp.243~280 (2020)
- Brett L., "Machine Learning with R-Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data" (2024), Acor publishing Co., Ltd
- Chae SM, "The effectiveness of connunity public health interventions in alleviating the health impacts of high temperatures", Health and Social Welfare Review, 36(3), pp.179~203 (2016)
- Spatial downscaling of population vulnerable to climate change in Incheon Metropolitan City and Policy Utilization Plan, "Cho KD", The Incheon Institute, 7~99 (2022)
- Hong SH, "History of Education and Science", pp.1~141 (2005)
- The 3rd Incheon Metropolitan City Climate Change Adaptation Measures Detailed Implementation Plan (2022-2026), "Incheon Metropolitan City", ~ (2021)
- Japanese Ministry of the Environment(JME), "Health manual of heat stroke", 6~7 (2011)
- Kang JY, "A study on Improvement of heat wave adaptation through questionnaire survey on the heat wave congnition in Busan", National Land Research, 107, pp.79~92 (2020)
- Kim D, "The policy measures to reduce heat-wave3 damage of vulnerable groups in Korea", Environmental Policy, 28(2), pp.211~260 (2020)
- Kim DW, "Characteristics of Heat wave Mortality in Korea", Atmosphere, 24(2), pp.225~234 (2014), Korean Meteorological Society
- Kim HR, "Identification of Vulnerability factors for urban floods : Considering social vulnerability of vulnerable groups in Seoul", Journal of Climate Change Research, 12(2), pp.187~202 (2021)
- There is a need for heat wave measures such as expansion of heat shelters and tropical night shelters considering regional balance, "Kim KW", Busan edvelopment institute, ~ (2019)
- Kim, J, "Analysis on the Characteristics of Heat Wave Vulnerable Areas Using Landsat 8 Data and Vulnerability Assessment Analysis", Journal of the Korean Geographic Information Society, 23(1), pp.1~14 (2020)
- Lee H, "Landslide Risk assessment in Inje using logistic Regresssion model", Journal of the Korean Society of Surveying and Surveying, 30(3), pp.313~321 (2012)
- Lee J-D, "Relationship analysis between topographic factors and land surface temperature from Landsat 7 ETM+ imagery", Journal of the Korea Contents Association, 12(11), pp.482~491 (2012)
- Lee JH, "Green city policy direction considering vulnerable groups", Korea Reseach Institute for Human Settlements, 427, pp.1~6 (2013)
- Lee J-S, "Distribution analysis of lan surface temperature about seoul using landsat 8 satellite images and AWS data", Journal of the Korea Academia-industrial, Cooperation Society, 20(1), pp.434~439 (2019)
- Lee K, "Impact of climate change to the summer season hedging cost in electricity sector : Using a weather derivatives simulation method", Environmental Policy, 12, pp.125~159 (2019)
- Lee K, "Study on Heat Vulnerability Assessment Using Biotope Map : A Case Study of Suwon Korea", Journal of Climate Change Research, 11(6), pp.629~641 (2020)
- Lee K, "Analysis of the Surface Urban Heat Island Changes according to Urbanization in Sejong City Using Landsat Imagery", Journal of the Korean Society of Remote Sensing, 38(3), pp.225~236 (2022)
- Lee KI, "Analysis of the effect of heat island on the administrative district unit in Seoul using Landsat image", Korean Journal of Remote Sensing, 33(5-3), pp.821~834 (2017)
- Lee M., "Urban heat wave occurrence status and damage reduction direction", Planning and Policy Brief, 466, pp.15~20 (2020)
- Lee SY, "Direction of Improvement of Indicators in Heat Wace Sector of The Urban Climate Change Disaster Vulnerability Analysis System", Living Environment, 20(3), pp.1~16 (2022)
- Martinez GS, "Local Heat Stroke Prevention Plans in Japan : Characteristics and Elements for Public Health Adaptation to Climate Change", International Journal of Environmental Research and Public Health, 8, pp.4563~4581 (2011)
- Park JE, "A Study on Human Damage due to Heat Wave by Region", Journal of the Korean Society of Hazard Mitigation, 16(1), pp.103~109 (2016)
- Park J-K, "A study on the characteristics of heat wave disaster vulnerability using landsat satellite imagery", Korea Society of Environmental Administration, 26(1), pp.33~38 (2020)
- Park M-H, "Effects of heat vulnerability on the distribution of young single-person households in the Seoul Metropolitan area", Journal of Korea Urban Design Society Urban Design, 22(3), pp.41~53 (2021)
- Rhim H, "Optimal Locatino selection for the cooling fog system using Landsat-8 Satellite Images", Journal of the Korean Society of Regional Geography, 29(1), pp.107~116 (2023)
- Ryu J, "Incheon metropolitan city climate adaptation priority selection and utilization plan in planning", Journal of Climate Change Research, 14(4), pp.405~423 (2023)
- Seo JW, "Logistic regression analysis considering soil loss and GIS-based urban ground subsidence vulnerability assessment", Tunnel & Underground Space, 30(2), pp.149~163 (2020)
- Song K-W, "Emergency measures for people vulnerable to heat waves", BDI Focus (2013), Busan Development Institute
- "Another death from heat-related illness occurred during the longest tropical night in history"[URL]
