Sooyeon Kang
April 8, 2012
DHP 207: GIS for International Applications
Assignment 8: Project Proposal and Data Sources
1. Project Proposal: Vulnerability Mapping of North Korea
a. Which areas in North Korea are most susceptible and vulnerable to starvation?
i. Considering natural disasters such as flooding and famine (crop failure)
ii. Structural factors such as road networks and access to public facilities
b. Where have NGOs worked inside North Korea to deliver food-related humanitarian
assistance over the years? (Possible question if data is acquired in time).
2. References:
a. Forte, F., Strobl, R. O., & Pennetta, L. (2006). A methodology using GIS, aerial photos and
remote sensing for loss estimation and flood vulnerability analysis in the SupersanoRuffano-Nociglia Graben, southern Italy. Environmental Geology, 50, 581–594.
i. Although this article presents a good methodology of loss estimation and flood
vulnerability analysis, it is not very useful to the case of North Korea because of
the lack of access to aerial photos and remote sensing methods. However, this
article was helpful in showing the level of in-depth analysis possible using direct
mapping and indirect mapping techniques, in combination with other data
sources.
b. Jenelius, E. and L.-G. Mattsson (2006). Developing a Methodology for Road Network
Vulnerability Analysis, Molde University College. Retrieved on: 2 April 2012.
<http://home.abe.kth.se/~jenelius/vulnerability/Paper_Molde_2006.pdf>.
i. This paper presents a methodology to analyze and quantify transportation
network vulnerability. I could use similar methodology to identify access to
alternatives from vulnerable regions – considering the travel time, land cover,
and road networks.
c. O’Brien, K., Leichenko, R., Kelkar, U., Venema, H., Aandahl, G., Tompkins, H., et al.
(2004). Mapping vulnerability to multiple stressors: climate change and globalization in
India. Global Environmental Change, 14, 303-313.
i. I found this article helpful because it takes into account confounding variables
to vulnerability. It tries to present a systematic methodology to study climate
change vulnerability in the context of multiple stressors, and I would like to do
something similar with my vulnerability to starvation analysis. It would benefit
the analysis to measure the adaptive capacity as a composite of multiple
indicators – weighting them according to their significant provided by research.
d. Tran, P., Shaw, R., Chantry, G. and Norton, J. (2009). GIS and local knowledge in disaster
management: a case study of flood risk mapping in Viet Nam. Disasters, 33: 152–169.
i. This paper used a base map of the overlay of transportation system, river and
steam network, commune boundary, and land cover to map flood risk in Viet
Nam. It identified 6 main physical factors that contributed to household flood
risk: house types; 1999 flood level; 2004 flood level; household proximity to
river; household proximity to safe shelter; and household proximity to main
roads. Although I do not have accessible flood level data, I found the study
helpful in terms of giving relative weight to the factors that contributed to
household flood risk and I found it insightful that the flood risk map was created
considering the interactions between natural hazards, exposures and vulnerable
conditions.
e. Zhang, Z. and K. Virrantaus, K. (2010) Analysis of Vulnerability of Road Networks on the
Basis of Graph Topology and Related Attribute Information, 13th AGILE International
Conference on Geographic Information Science 2010 (presentation). Retrieved on: 6
April 2012 <http://agile2010.dsi.uminho.pt/pen/PosterAbstracts_PDF%5C76_DOC.pdf>.
i. This presentation uses the multi-attribute value theory as a tool of decision
analysis. The effect of different attributes are calculated and combined to
evaluate the preparedness for crisis management.
3. Methods:
a. Georeferencing – maps from CIA reports regarding floods in North Korea1
b. Proximity analysis – types of facility near areas of high flooding
c. Overlay analysis – using the newly acquired data on North Korea (M-drive) and land
cover data, analyze different types of points and polygons that fall within areas more
vulnerable to starvation
d. Density analysis – If I am able to gather enough NGO data, I would like to run a density
analysis to learn more about the spread of NGO work over the region
e. Suitability map using raster calculator – using individual factor maps, overlay them to
create a vulnerability to starvation map.
4. Data Layers:
Description of Layer
1.
2.
3.
4.
5.
6.
1
Land Cover
Administrative boundaries
Transportation network (streets
and railroad lines)
Population
Elevation
North Korean Provinces Receiving
Heavy Rain in 2010
Source of Layer
Basemap
M:\datasets\Country\NorthKorea
M:\datasets\Country\NorthKorea
DPRK 2008 Population Census – National Report
Global Digital Elevation Model (SRTM)
“North Korea: Assessing the Impact of Flooding on
Agricultural Output,” CIA Open Source Works, 15
December, 2010. (10). 6 April 2012.
<http://www.fas.org/irp/cia/product/nk-flood.pdf>.
“North Korea: Assessing the Impact of Flooding on Agricultural Output,” CIA Open Source Works, 15 December,
2010. (10). 6 April 2012. <http://www.fas.org/irp/cia/product/nk-flood.pdf>.