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Cited by 1,119

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Identifying and Mapping Community Vulnerability

Morrow, Betty Hearn·1999·Disasters

Abstract

Disaster vulnerability is socially constructed, i.e., it arises out of the social and economic circumstances of everyday living. Most often discussed from the perspective of developing nations, this article extends the argument using American demographic trends. Examples from recent disasters, Hurricane Andrew in particular, illustrate how certain categories of people, such as the poor, the elderly, women‐headed households and recent residents, are at greater risk throughout the disaster response process. Knowledge of where these groups are concentrated within communities and the general nature of their circumstances is an important step towards effective emergency management. Emergency planners, policy‐makers and responding organisations are encouraged to identify and locate high‐risk sectors on Community Vulnerability Maps, integrating this information into GIS systems where feasible. Effective disaster management calls for aggressively involving these neighbourhoods and groups at all levels of planning and response, as well as mitigation efforts that address the root causes of vulnerability.

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Crossref · 10.1111/1467-7717.00102

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Metadata

Authors
Morrow, B. H.
Venue
Disasters
Publisher
Wiley
Published
1999-03
Volume
23
Issue
1
Pages
1-18
ISSN
  • 0361-3666 (ISSN-L)
  • 1467-7717
OpenAlex
W2054924919
Cited by
1,119
References
40
FWCI
19.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Identifying and Mapping Community Vulnerability. https://app.cochat.ai/papers/10.1111/1467-7717.00102
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Morrow, B. H. (1999). Identifying and Mapping Community Vulnerability. Disasters. https://doi.org/10.1111/1467-7717.00102

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.