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Erschienen in: Population and Environment 2/2016

11.06.2016 | Perspectives

Climate, migration, and the local food security context: introducing Terra Populus

verfasst von: Raphael J. Nawrotzki, Allison M. Schlak, Tracy A. Kugler

Erschienen in: Population and Environment | Ausgabe 2/2016

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Abstract

Studies investigating the connection between environmental factors and migration are difficult to execute because they require the integration of microdata and spatial information. In this article, we introduce the novel, publically available data extraction system Terra Populus (TerraPop), which was designed to facilitate population–environment studies. We showcase the use of TerraPop by exploring variations in the climate–migration association in Burkina Faso and Senegal based on differences in the local food security context. Food security was approximated using anthropometric indicators of child stunting and wasting derived from Demographic and Health Surveys and linked to the TerraPop extract of climate and migration information. We find that an increase in heat waves was associated with a decrease in international migration from Burkina Faso, while excessive precipitation increased international moves from Senegal. Significant interactions reveal that the adverse effects of heat waves and droughts are strongly amplified in highly food insecure Senegalese departments.

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Fußnoten
1
Geographic unit boundaries are the key to TerraPop’s location-based integration, and the system provides several types of boundaries to serve the needs of various users. For use with microdata, geographic units are regionalized to ensure that the population of each unit is >20,000 people to maintain confidentiality. TerraPop also includes both harmonized and year-specific boundaries. In the harmonized boundaries, units that have changed over time are combined to provide consistent footprints facilitating the analysis of change over time (Kugler et al. 2015). For most countries, TerraPop provides first and second administrative level boundaries.
 
2
For Burkina Faso, the DHS survey year (2003) falls within the 5-year window (2001–2006) prior to the census round in 2006. However, for Senegal the DHS survey was conducted in year 2005, three years after the census round in 2002. Although another full DHS survey was conducted in Senegal in 1997, this earlier wave did not include the relevant anthropometric indicators. We use the Senegal DHS for 2005 based on the common assumption that food security within a population is relatively static (Saha et al. 2009).
 
3
For confidentiality purposes, DHS randomly displaces rural cluster centroids between 0 and 5 km and an additional random selection of 10 % of the cluster points between 0 and 10 km, resulting in a relatively small average displacement distance of 2.45 km (Burgert et al. 2013). The random displacement algorithm ensures that centroids fall within the correct first-level administrative unit (Burgert et al. 2013). Although we aggregate points to the second-level administrative unit, the introduced uncertainty is likely minimal due to the large size of provinces/departments (most clusters are more than 5 km away from the borders). In addition, the random nature of the displacement ensures that the resulting estimates are not systematically biased.
 
4
We use the years 1961–1990 as the standard “climate normal” period recommended by the World Meteorological Organization to be used as reference period for studies of climate change and climate variability (Arguez and Vose 2011).
 
5
The wealth index combined three measures of the quality of the housing unit (material of floor, wall, roof), three measures of the type and quality of services available at the residence (type of cooking fuel, toilet type, access to electricity), and three measures to capture the possession of appliances (car, refrigerator, TV).
 
6
The models were fitted using the package lme4 (Bates 2010; Bates et al. 2014) within the R statistical environment (RCoreTeam 2015). For improved speed and more robust convergence properties, we adjusted the model settings (integer scalar setting nAGQ = 0) so that the random and fixed effects were optimized (optimizer=“bobyqa”) in the penalized iteratively reweighted least squares step (Bates et al. 2014).
 
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Metadaten
Titel
Climate, migration, and the local food security context: introducing Terra Populus
verfasst von
Raphael J. Nawrotzki
Allison M. Schlak
Tracy A. Kugler
Publikationsdatum
11.06.2016
Verlag
Springer Netherlands
Erschienen in
Population and Environment / Ausgabe 2/2016
Print ISSN: 0199-0039
Elektronische ISSN: 1573-7810
DOI
https://doi.org/10.1007/s11111-016-0260-0

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