contributor author | Ali Nejat; Saeed Moradi; Souparno Ghosh | |
date accessioned | 2019-03-10T12:15:09Z | |
date available | 2019-03-10T12:15:09Z | |
date issued | 2019 | |
identifier other | %28ASCE%29IS.1943-555X.0000471.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4255206 | |
description 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. | |
publisher | American Society of Civil Engineers | |
title | Anchors of Social Network Awareness Index: A Key to Modeling Postdisaster Housing Recovery | |
type | Journal Paper | |
journal volume | 25 | |
journal issue | 2 | |
journal title | Journal of Infrastructure Systems | |
identifier doi | 10.1061/(ASCE)IS.1943-555X.0000471 | |
page | 04019004 | |
tree | Journal of Infrastructure Systems:;2019:;Volume ( 025 ):;issue: 002 | |
contenttype | Fulltext | |