Literature DB >> 32621296

Using Panel Data to Understand the Dynamics of Human Behavior in Response to Flooding.

Philip Bubeck1, Lisa Berghäuser1, Paul Hudson1, Annegret H Thieken1.   

Abstract

Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross-sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated-measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents' perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk-reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk-reducing behavior, and associated risk and protective factors.
© 2020 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.

Entities:  

Keywords:  Adaptation behavior; LCGA; floods; individual recovery; panel data

Year:  2020        PMID: 32621296     DOI: 10.1111/risa.13548

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  3 in total

1.  [Identification of potential hub genes of Alzheimer's disease by weighted gene co-expression network analysis].

Authors:  J Xue; J Liu; M Geng; J Yue; H He; J Fan
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2021-12-20

2.  [Identification and validation of hub genes in prostate cancer progression based on weighted gene co-expression network analysis].

Authors:  H Zhang; N Chen; X Wang; B Gao; M Ling; G Chen; Z Wu; Y Li; W Zhong; B Pan
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2021-11-20

3.  Contextualizing cross national patterns in household climate change adaptation.

Authors:  Brayton Noll; Tatiana Filatova; Ariana Need; Alessandro Taberna
Journal:  Nat Clim Chang       Date:  2021-12-02
  3 in total

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