wow-inequalities/02-data/intermediate/wos_sample/686f026983b6b2a221854a9b67bc4ad2-apotsos-alex/info.yaml

abstract: 'Urban decision-makers in South Africa face growing challenges related to

  rapidly expanding populations and a changing climate. To help target

  limited resources, municipalities have begun to conduct climate change

  vulnerability assessments. Many of these assessments take a holistic

  approach that combines both physical hazard exposure and the underlying

  socio-economic conditions that predispose populations to harm (i.e.,

  social vulnerability). Given the increasing use of socio-economic

  conditions in climate change vulnerability analyses, this paper seeks to

  explore two key research questions: 1) can the spatial distribution of

  relative social vulnerability be estimated in six mostly urban South

  African municipalities, and if so, 2) how sensitive are the results to a

  range of subjective methodological choices often required when

  implementing this type of analysis. Here, social vulnerability is

  estimated using socio-economic and demographic data from the 2001 and

  2011 South African censuses. In all six municipalities, social

  vulnerability varies spatially, driven primarily by differences in

  income, assets, wealth, employment and education, and secondarily by

  differences in access to services and demographics. Even though social

  vulnerability is estimated from a wide array of population

  characteristics, the spatial distribution is surprising similar to that

  of the percent of working-age individuals making less than 800 rand per

  month. Areas with high percentages of previously disadvantaged, extended

  family, and informal households tend to display relatively higher levels

  of social vulnerability. In fact, demographics (e.g., race, language,

  age) are often highly correlated with other characteristics that have

  direct ties to social vulnerability (e.g., income, employment,

  education). The spatial patterns of relative social vulnerability are

  similar in 2001 and 2011. However, there is some evidence social

  vulnerability is relatively lower in 2011. While the choice of input

  data and aggregation method can affect the spatial distribution of

  social vulnerability, the general spatial patterns appear to be fairly

  robust across a number of subjective choices related to methodological

  and aggregation approach, spatial resolution, and input data.'
affiliation: 'Apotsos, A (Corresponding Author), Williams Coll, Geosci Dept, Clark
  Hall,947 Main St, Williamstown, MA 01267 USA.

  Apotsos, Alex, Williams Coll, Geosci Dept, Clark Hall,947 Main St, Williamstown,
  MA 01267 USA.'
author: Apotsos, Alex
author-email: aa13@williams.edu
author_list:
- family: Apotsos
  given: Alex
da: '2023-09-28'
doi: 10.1016/j.apgeog.2019.02.012
eissn: 1873-7730
files: []
issn: 0143-6228
journal: APPLIED GEOGRAPHY
keywords: Social vulnerability; South Africa; Urban municipalities; Mapping
keywords-plus: 'CLIMATE-CHANGE ADAPTATION; ADAPTIVE CAPACITY; NATURAL HAZARDS;

  ASSESSMENTS; VARIABILITY; INDICATORS; CHALLENGES; HOUSEHOLDS; DYNAMICS;

  LEVEL'
language: English
month: APR
number-of-cited-references: '69'
pages: 86-101
papis_id: 37854f606fb529dbbfcd4cbd40524e5a
ref: Apotsos2019mappingrelative
times-cited: '16'
title: Mapping relative social vulnerability in six mostly urban municipalities in
  South Africa
type: article
unique-id: WOS:000464479200008
usage-count-last-180-days: '3'
usage-count-since-2013: '20'
volume: '105'
web-of-science-categories: Geography
year: '2019'