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/km²) 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.

中文翻译：

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索引中有什么？构建方法、数据度量和加权方案决定了纽约市综合社会脆弱性指数的结果。


绘制社会脆弱性图是识别社会环境中缺乏应对极端天气影响能力的地区的一种重要方法，有助于脆弱居民、社区或地区适应气候变化。计算社会脆弱性通常涉及构建综合指数，为此提出了几种构建方法。然而，对跨方法结果的彻底调查或对脆弱性因素的应用权重很大程度上缺乏。本研究调查了变量添加的结果（有或没有对单一脆弱性因素进行加权）以及为纽约市计算的社会脆弱性指数的变量减少方法/模型。加权是基于纽约市气候变化影响的科学评估报告。此外，该研究还计算了使用基于区域（人/平方公里² ）或基于人口（%）的输入数据时的社会脆弱性结果。该研究揭示了指数之间的显着差异，特别是在使用不同方法时以及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，而变量减少模型在社会脆弱性因素的强度未知时有用。当将差异视为可信度和质量的衡量标准时，使用基于区域的数据似乎比基于人口的数据更可取。结果对于使用索引构建技术的所有形式的漏洞映射都很重要。