TY - JOUR
T1 - Perceived neighborhood
T2 - Preferences versus actualities
AU - Moradi, Saeed
AU - Nejat, Ali
AU - Hu, Da
AU - Ghosh, Souparno
N1 - Publisher Copyright:
© 2020
PY - 2020/12
Y1 - 2020/12
N2 - Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist within perceived neighborhood boundaries (actual anchors). The goal is to propose a model for classifying households’ perceived anchors from publicly available data on actual anchors. Data were collected on households' attributes, perceived neighborhood boundaries, and perceived community anchors through an online survey of New York and Louisiana residents. Actual anchors were mined from the OpenStreetMap database. Correlation analysis revealed several significant associations between actual and perceived anchors. A multilayer feed-forward neural network model was also developed to predict the classification of households’ perceived anchors from actual anchors. Sensitivity analysis of the model disclosed that individuals whose perceived neighborhood comprised more categories of actual anchors were more likely to prioritize infrastructure to other neighborhood assets, a preference that was more dominant in high-density areas.
AB - Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist within perceived neighborhood boundaries (actual anchors). The goal is to propose a model for classifying households’ perceived anchors from publicly available data on actual anchors. Data were collected on households' attributes, perceived neighborhood boundaries, and perceived community anchors through an online survey of New York and Louisiana residents. Actual anchors were mined from the OpenStreetMap database. Correlation analysis revealed several significant associations between actual and perceived anchors. A multilayer feed-forward neural network model was also developed to predict the classification of households’ perceived anchors from actual anchors. Sensitivity analysis of the model disclosed that individuals whose perceived neighborhood comprised more categories of actual anchors were more likely to prioritize infrastructure to other neighborhood assets, a preference that was more dominant in high-density areas.
KW - Anchors of social network awareness index
KW - Deep learning
KW - Disaster recovery
KW - Feed-forward neural network
KW - Perceived neighborhood
UR - http://www.scopus.com/inward/record.url?scp=85090118717&partnerID=8YFLogxK
U2 - 10.1016/j.ijdrr.2020.101824
DO - 10.1016/j.ijdrr.2020.101824
M3 - Article
AN - SCOPUS:85090118717
SN - 2212-4209
VL - 51
JO - International Journal of Disaster Risk Reduction
JF - International Journal of Disaster Risk Reduction
M1 - 101824
ER -