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| Titel |
A spatial meta-indicator for identifying and evaluating hotspots of climate-related drivers for conflict and migration in the Sahel |
| VerfasserIn |
Michael Hagenlocher, Stefan Lang, Daniel Hölbling |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250051910
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| Zusammenfassung |
| The countries of the Sahel are among the countries currently most affected by global climate
and/or environmental change. The combination of multiple stresses, such as endemic poverty,
population pressure due to high population growth rates, prevailing dominance of the primary
sector, complex governance, political instability, population displacement due to natural or
man-made disasters, highly degraded environments, etc. and the resulting weak adaptive
capacity make the Sahel one of the most vulnerable regions to the projected effects of climate
change. Against this background, based on a request from UNEP-PCDMB (Disasters and
Conflicts), a comprehensive study focusing on the implications of climate change on
conflict dynamics and migration patterns in the nine Sahelian countries which are
forming the Permanent Inter-State Committee for Drought Control in the Sahel
(CILSS) was conducted by the Centre for Geoinformatics (Z_GIS) at Salzburg
University.
Within this context a mapping task was carried out highlighting observed changes for the
past 20 to 30 years (depending on data availability) concerning a set of four climate-change
induced drivers as well as migration and conflict patterns for the nine CILSS member
states and their neighbouring countries based on time-series of freely available
global datasets. The four climate-related drivers were: (1) erratic temperature and
rainfall patterns, (2) drought occurrences, (3) major flood events, and (4) sea level
rise.
In addition to solely using these singular indicators to deliver information on specific
components of the complex and manifold nexus of climate change, as well as migration and
conflict, a spatial meta-indicator was developed for highlighting and assessing hotspots of the
climate-related drivers, and the migration and conflict patterns in the target area. The
developed meta-indicator was composed by integrating and weighting the singular indicators
in a multi-dimensional feature space, making use of normalization (for this study an
8 bit value range had been applied as new value range to each of the integrated
drivers) and regionalization techniques (Kienberger et al., 2009). The resulting
conceptual spatial entities, instances of geons (Lang et al., 2008) are homogenous in
terms of their response to the climate-related spatial phenomena under concern. The
applied concept is a method for delineating units where similar spatial conditions
apply with respect to a set of defined spatial indicators. Consequently, for each
delineated unit a hotspot intensity (HI) value was calculated considering the six
integrated layers (v1, v2, -¦, v6) in a six dimensional feature space trough the vector
product:
|HI| = (v12 + v22 + v32+ v42+ v52+ v62)0.5
In order to ease the subsequent interpretation of the results, values were standardized
within a new range from zero to one [0, -¦, 1], where zero reflects a very low and one a very
high hotspot intensity. Building on that, all units with significant hotspot intensity (>
0.75) were defined as the actual hotspots. Next to the location and estimated size
of the delineated hotspots, the specific composition of the hotpots was visualized
by means of a pie chart for each of them, showing the relative proportion of the
contributing drivers or indicators. Consequently, not only the approximated size and
location, but also the quality of the respective hotspot was determined and mapped,
indicating to policy makers or stakeholders where further fine-scaled studies could be
conducted and at the same time highlighting which domains should be addressed in
particular.
LANG, S., ZEIL, P., KIENBERGER, S. and TIEDE, D. (2008): Geons – policy-relevant
geo-objects for monitoring high-level indicators. In: Car, A., Griesebner, G. and Strobl, J.
(eds.): Geospatial Crossroads @ GI_Forum ’08. Proceedings of the Geoinformatics Forum
Salzburg. Wichmann: Heidelberg, pp. 180-185.
KIENBERGER, S., LANG, S. and ZEIL, P. (2009): Spatial vulnerability units
– expert-based spatial modelling of socio-economic vulnerability in the Salzach
catchmet, Austria. In: Natural Hazards and Earth System Sciences, 9, pp. 767-778. |
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