Reckien Diana
1Faculty of Geo-Information Science and Earth Observation (ITC), Department of Urban and Regional Planning and Geo-Information Management, University of Twente, Hengelosestraat 99, P.O. Box 217, Enschede, The Netherlands.
2Earth Institute, Center for Research on Environmental Decisions, Columbia University in the City of New York, New York, NY USA.
Reg Environ Change. 2018;18(5):1439-1451. doi: 10.1007/s10113-017-1273-7. Epub 2018 Jan 18.
Mapping social vulnerability is a prominent way to identify regions in which the lack of capacity to cope with the impacts of weather extremes is nested in the social setting, aiding climate change adaptation for vulnerable residents, neighborhoods, or localities. Calculating social vulnerability usually involves the construction of a composite index, for which several construction methods have been suggested. However, thorough investigation of results across methods or applied weighting of vulnerability factors is largely missing. This study investigates the outcome of the variable addition-both with and without weighting of single vulnerability factors-and the variable reduction approach/model on social vulnerability indices calculated for New York City. Weighting is based on scientific assessment reports on climate change impacts in New York City. Additionally, the study calculates the outcome on social vulnerability when using either area-based (person/km) or population-based (%) input data. The study reveals remarkable differences between indices particularly when using different methods but also when using different metrics as input data. The variable addition model has deductive advantages, whereas the variable reduction model is useful when the strength of factors of social vulnerability is unknown. The use of area-based data seems preferable to population-based data when differences are taken as a measure of credibility and quality. Results are important for all forms of vulnerability mapping using index construction techniques.
绘制社会脆弱性地图是一种重要方法，用于识别那些应对极端天气影响能力不足的地区，这些地区存在于社会环境之中，有助于弱势群体、社区或地区适应气候变化。计算社会脆弱性通常需要构建一个综合指数，目前已经提出了几种构建方法。然而，对于不同方法的结果进行全面调查或对脆弱性因素进行加权应用，在很大程度上还存在欠缺。本研究调查了在为纽约市计算社会脆弱性指数时，采用单一脆弱性因素加权和不加权的变量加法以及变量约简方法/模型的结果。加权是基于纽约市气候变化影响的科学评估报告。此外，本研究还计算了使用基于面积（人/平方公里）或基于人口（%）的输入数据时社会脆弱性的结果。研究发现，不同指数之间存在显著差异，尤其是在使用不同方法时，以及在使用不同指标作为输入数据时。变量加法模型具有演绎优势，而当社会脆弱性因素的强度未知时，变量约简模型则很有用。当将差异作为可信度和质量的衡量标准时，使用基于面积的数据似乎比基于人口的数据更可取。研究结果对于所有使用指数构建技术的脆弱性地图绘制形式都很重要。
