{"title":"调查空间尺度对社会脆弱性指数的影响:结合遥感土地覆被数据的混合不确定性和敏感性分析方法。","authors":"Bowen He, Qun Guan","doi":"10.1111/risa.14342","DOIUrl":null,"url":null,"abstract":"<p><p>Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":"2723-2739"},"PeriodicalIF":3.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.\",\"authors\":\"Bowen He, Qun Guan\",\"doi\":\"10.1111/risa.14342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.</p>\",\"PeriodicalId\":21472,\"journal\":{\"name\":\"Risk Analysis\",\"volume\":\" \",\"pages\":\"2723-2739\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/risa.14342\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/risa.14342","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.
Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.
期刊介绍:
Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include:
• Human health and safety risks
• Microbial risks
• Engineering
• Mathematical modeling
• Risk characterization
• Risk communication
• Risk management and decision-making
• Risk perception, acceptability, and ethics
• Laws and regulatory policy
• Ecological risks.