Spatial Scope of the Regional Hazard Mitigation Plan

Yeom, Jaeweon; Kang, Sangmok; Jung, P; Jung, Juchul

doi:10.9798/kosham.2020.20.1.61

Cited by 1 publication

(2 citation statements)

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“…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 Resultssupporting

confidence: 54%

“…However, because Moran's I index displays relationships across study sites as a single value, it cannot explain the local structure of spatial relationships for each target area analyzed when the target area is large [37]. Local spatial autocorrelation can be confirmed by LISA analysis, a technique used to explore spatial clustering patterns based on the numerical similarity of attributed values between adjacent regions [38]. Four clusters have been derived.…”

Section: Lisa Analysismentioning

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 Resultssupporting

confidence: 54%

Section: Lisa Analysismentioning

confidence: 99%