Essentially, “social vulnerability”
describes how susceptible people (as individuals or groups) are to natural and
man-made disasters. This particular model used sociological and economic inputs
like literacy level, child population, and urban workers to score the relative
levels of vulnerability.
The source for all of the data was
the National Statistical System of Burkina Faso website; instead of shapefiles,
census tables were available through PDFs. Other challenges included
translating the data from French to English; adapting SoVI categories to
culturally relevant Burkinabé categories; and converting vectors to rasters in
order to use the Weighted Overlay tool in ArcMap. After the data was
normalized, four classes of social vulnerability were calculated, from 1 being
the least vulnerable to 4 being the most.
The audience of this project was
primarily Burkinabé government agencies; this information will help prepare official
decision makers for potential disasters. This model could very well be used in
the future for other governments and disaster organizations. In sum, the use of
GIS has helped make socioeconomic data more accessible to disaster-prevention administrators
in Burkina Faso, one of the poorest nations in the world.
- Anne Bransford
This article shows a great use of composite statistics that are usually used in economics applied in order to highlight socioeconomic issues to inform policy makers. Many times people use these indices to revel patterns. This is much like what we were doing in class with lab 1, but instead of looking at only 1 variable of demographic data you take multiple variables. Basically you have to make a formula that adds together the multiple variables to one index. A famous Index is the Human Development Index (HDI). Here is a link to an article that I co-authored about HDI. This can give you more ideas on indices. A great project would be to take the SVI and apply it to the 2010 census compare it to the 2000 census and see how Williamson county has changed with 10 years of development.
ReplyDeletehttp://www.ambiente.sp.gov.br/prestacaodecontas/08-jan-a-21-mai-2011-book.pdf#page=33
This article is a great example of how GIS can help save the poor nations of the world, without requiring too much man power. There are not enough volunteers and workers to find ways to support the poor nations of Africa, and with the information received through GIS the administrators can create specific work groups to tackle the different disaster preventions.
ReplyDeleteThis is a very interesting intersection between environmental disasters and demographic data. It seems like using a model like this at a very local level could help us understand the interactions of climate change impact on local communities. Factors to calculate could relate to the availability to respond to elements such as rising sea level, or to mosquito vector diseases brought on by rainier climate or more humidity. However, I wonder how well GIS can capture harder factors to measure such as communities having to change hunting behaviors in response to climate change (or natural/man-made disasters). Behavioral changes may impact community identity and thus vulnerability in a similar way than would frequent floods.
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