Perceived neighborhood: Preferences versus actualities

Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number101824
JournalInternational Journal of Disaster Risk Reduction
Volume51
DOIs
StatePublished - Dec 2020

Keywords

  • Anchors of social network awareness index
  • Deep learning
  • Disaster recovery
  • Feed-forward neural network
  • Perceived neighborhood

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