An Analysis on the Spatial Patterns of Heat Wave Vulnerable Areas and Adaptive Capacity Vulnerable Areas in Seoul

Choi, Ye Seul; Kim, Jae Won; Lim, Up

doi:10.17208/jkpa.2018.12.53.7.87

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…According to a previous study confirming spatial autocorrelation, it was judged that there was spatial autocorrelation when Moran's I coefficient was 0.267 [49]. judged that a coefficient value of 0.2857 showed a significant level of positive spatial autocorrelation [21]. Yeom et al (2020) confirmed exponential values of 0.398, 0.607, and 0.483 for the three indicators and found that they appeared to have high spatial autocorrelation [38].…”

Section: Lisa Resultsmentioning

confidence: 93%

“…Moran's I coefficient, which usually confirms this, has a positive spatial autocorrelation range of +1 and a negative spatial autocorrelation of −1. It has been seen to show a positive spatial autocorrelation with similar values [21]. It is closer to −1 because the adjacent regions are different, and it appears closer to 0 because autocorrelation is not present.…”

Section: Lisa Analysismentioning

confidence: 87%

“…In discussing the relationship between urban characteristics and micro-dust, most studies observing social welfare characteristics have aimed to measure the welfare of humans within cities. Regarding social welfare characteristics, variables such as number of hospital beds, number of doctors, and ratio of health and social welfare enterprises have been utilized [21]. This is related to the adaptive capacity of cities.…”

Section: Influencing Factors Of Particulate Matter Distributionmentioning

confidence: 99%

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Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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In 2019, South Korea’s Framework Act on The Management of Disasters and Safety was revised to include respirable particulate matter as a social disaster. Urban air pollution, especially particulate matter pollution, has been a serious threat to socioeconomic development and public health. In order to address this problem, strong climate crisis response strategies and policies to improve urban air quality are necessary. Therefore, it is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of this study is to develop consistent methodologies for the construction of an index system and for assessing the influencing factors of urban particulate matter pollution based on population, social welfare, land use, environmental, transportation, and economic governance considerations. We applied the local indicators of spatial association and geographical detector methods, and 35 influencing factors were selected to assess their influence on urban air pollution occurrence in 229 cities and counties in South Korea. The results indicated the spatial pattern of the particulate matter concentration in these locations showed strong spatial correlation, and it was confirmed that there was a difference in distribution according to the season. As a result of the analysis of influencing factors, it was found that environment and land use characteristics were the main influencing factors for PM10 and PM2.5. The explanatory power between the two influencing factors of particulate matter was greater than that of a single influencing factor. In addition, most influencing factors resulted in both positive and negative effects on urban fine particulate matter pollution. The interaction relationship of all factors showed a strong action effect in the case of both PM10 and PM2.5, so it was confirmed that all influencing factors were interdependent. In particular, the findings proved that combining the two factors would have a more pronounced effect on particulate matter than when they were independent. We confirmed the significant results for the factors affecting particulate matter. This study offers suggestions on reducing urban air pollution occurrence that can be used to provide a basis and reference for the government to form policies on urban air pollution control in cities and counties.

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Section: Lisa Resultsmentioning

confidence: 93%

Section: Lisa Analysismentioning

confidence: 87%

Section: Influencing Factors Of Particulate Matter Distributionmentioning

confidence: 99%

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Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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“…Lee et al, 2020;Hwang et al, 2021). Wolf and McGregor (2013) (GRI, 2009;KEI, 2012;KRIHS, 2012;Wolf and McGregor, 2013;Koo et al, 2015;Macnee and Tokai, 2016;Lee, 2017;Choi et al, 2018;Nayak et al, 2018;Lee and Cha, 2019; (Kim et al, 2019). Lee et al, 2020).…”

Section: 서 론mentioning

confidence: 99%

Assessment of Heatwave Vulnerability in Korea Considering Socio-economic Indices

Kim¹,

Kim²,

Jang³

et al. 2021

J. Korean Soc. Hazard Mitig

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The rising heatwave occurrences in recent times due to climate change have resulted in increased mortalities and socio-economic damage. Consequently, several studies have been conducted to examine heatwave vulnerability in Korea. However, most of these studies used the IPCC vulnerability framework and weighting techniques, such as the equal weight and AHP methods, which lacked objectivity in the process of calculating vulnerability. This study employed socio-economic data to measure the heatwave vulnerability index for individual local governments in Korea using the principal component analysis and entropy weighting methods. These techniques ensure that the aggregation of proxies and the weighting process remain objective, unlike previous studies. According to the obtained results, rural areas such as Jeollanam-do, Jeollabuk-do, Gyeongsangnam-do, and Gyeongsangbuk-do, and relatively decrepit urban areas demonstrated high vulnerability scores. In addition, a positive correlation was found between the calculated vulnerability index and mortalities from the recent heatwaves. The heatwave vulnerability index developed in this study can therefore be used to form effective heatwave response policies suited to the conditions of each local government.

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“…As the spatial resolution of the index is limited to cities, counties, and districts, identifying the detailed characteristics of regions where patients with heat-related illnesses actually occur, and directly reflecting them in metrics recognized by local governments, remains challenging. For example, Choi et al [15] constructed statistical data on the degree of exposure, sensitivity, and adaptive capacity to climate change of administrative neighborhoods in Seoul, while analyzing the spatial distribution of heatwave vulnerability. The authors found that the vulnerability index of each administrative neighborhood can serve as important data for establishing heatwave adaptation policies.…”

Section: Introductionmentioning

confidence: 99%

Spatial Relationship between Heat Illness Incidence and Heat Vulnerability in Gurye and Sunchang, South Korea, 2018

Kwon

2023

IJERPH

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Heatwaves, along with their affiliated illnesses and mortalities, are increasing in frequency and severity under climate change. Spatial analyses at the level of census output areas can produce detailed maps of heatwave risk factors and potential correlated damages, thus contributing to practical policies to reduce the risk of heatwave illnesses. This study analyzed the 2018 summer heatwave in Gurye and Sunchang counties in South Korea. To compare damages and analyze the detailed causes of heatwave vulnerability, spatial autocorrelation analyses were conducted, incorporating weather, environmental, personal, and disease factors. Gurye and Sunchang, although similar in demographics and region, exhibited large differences in heatwave damage specifically in the number of heat-related illness cases. In addition, exposure data were constructed at the census output area level by calculating the shadow pattern, sky view factor, and mean radiant temperature, revealing a higher risk in Sunchang. Spatial autocorrelation analyses revealed that the factors most highly correlated with heatwave damage were hazard factors, in the case of Gurye, and vulnerability factors, in the case of Sunchang. Accordingly, it was concluded that regional vulnerability factors were better distinguished at the finer scale of the census output area and when detailed and diversified weather factors were incorporated.

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An Analysis on the Spatial Patterns of Heat Wave Vulnerable Areas and Adaptive Capacity Vulnerable Areas in Seoul

Cited by 14 publications

References 38 publications

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Assessment of Heatwave Vulnerability in Korea Considering Socio-economic Indices

Spatial Relationship between Heat Illness Incidence and Heat Vulnerability in Gurye and Sunchang, South Korea, 2018

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