This study explores the relationship between Winter storm Grayson and human responses in the US by considering a multitude of periods, regions, socio-demographic variables and spatial autocorrelation for the number of tweets. This study suggests that the proportion of tweets is highly concentrated in the north-east region, which is the most damaged region in the winter storm week. Second, there is significant spatial autocorrelation for the number of tweets related to the winter storm. The lag coefficient values of the spatial lag model and the spatial error model are 0.362 and 0.437 at a 0.01 significance level, respectively. Third, younger people and people who have less than a high school degree show a positive coefficient value, whereas males, people with a high school degree, and those who have high income show a negative coefficient value for the number of tweets. This study shows that spatial regression models would be better models to understand human responses to natural disasters than the ordinary least squares model.

中文翻译：

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人类反应与冬季风暴格雷森之间的空间自相关

本研究通过考虑多个时期、地区、社会人口变量和推文数量的空间自相关，探讨冬季风暴格雷森与美国人类反应之间的关系。这项研究表明，推文的比例高度集中在东北地区，这是冬季风暴周受损最严重的地区。其次，与冬季风暴相关的推文数量存在显着的空间自相关。空间滞后模型和空间误差模型的滞后系数值在 0.01 显着性水平下分别为 0.362 和 0.437。第三，年轻人和高中以下学历的人表现出正的系数值，而男性，高中学历的人，高收入者的推文数量系数值为负。这项研究表明，空间回归模型比普通的最小二乘模型更能理解人类对自然灾害的反应。