Anchors of Social Network Awareness Index: A Key to Modeling Postdisaster Housing Recovery
Publication: Journal of Infrastructure Systems
Volume 25, Issue 2
Abstract
Reestablishment of housing is a crucial component of the recovery process and has a domino effect on the overall timing of recovery. Anchors of social networks, such as schools and churches, on the other hand, are perceived to be influential in housing recovery decisions. This study provides a model for indexing households’ anchors of social network awareness based on publicly available data. This model uses individual-level data to develop a county-level index of anchors of social network awareness. This allows devising recovery strategies that are tailored to the needs of residents within a given county. Data were collected through an internet survey targeting New York and Louisiana, which were highly impacted by Hurricanes Sandy and Katrina. The survey asked participants to draw a polygon around their perceived neighborhood area in Google Maps. Then, follow-up questions were asked to identify key anchors driving this perception. Latent class analysis (LCA) and regression revealed the existence of multiple latent classes, each corresponding to a certain demographic and socioeconomic group. Finally, a county-level index of anchors of social network awareness was developed using individual-level latent classes. This index can be used by policyholders as a decision support tool for prioritizing anchors that are deemed to be important in a given county for receiving recovery assistance, which can then lead to a more enhanced recovery.
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Acknowledgments
This research was supported by the National Science Foundation award #1454650 and the Texas Tech University Office of the Vice President for Research, for which the author expresses his appreciation. Publication of this paper does not necessarily indicate acceptance by the funding entities of its contents, either inferred or especially expressed herein. The authors thank Mehdi Jamali, who helped with the review of literature for this paper.
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Received: Dec 14, 2017
Accepted: Aug 22, 2018
Published online: Jan 23, 2019
Published in print: Jun 1, 2019
Discussion open until: Jun 23, 2019
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