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Table 2 Overview of the nine selected peer-reviewed articles

From: Geographic information analysis and web-based geoportals to explore malnutrition in Sub-Saharan Africa: a systematic review of approaches

Authors (year) Study objectives Spatial analysis method(s) Geodata Scale of geodata/level of analysis Retro- or prospective (time span for analysis) Geographic region
Balk et al.[23] Capture the effects of geographic and environmental variables on child hunger. Looking for causal relationships using micro-level data on a continental scale. - Spatial Statistics: Simple ordinary least squares (OLS) regression analysis - Agriculture Local and regional data/Micro- and meso-level analysis Retrospective (1995–2004) African, Asian and Latin American countries
- Climate
- Health (+DHS*)
- Infrastructure
- Physiography
- Politics
- Population
Grace et al.[19] Evaluate the relationship between climate variables and child malnutrition using a food security framework - Spatial Statistics: Multi-level linear regression model - Climate Household and local data/Micro-level analysis Retrospective and Prospective (1990–2039) Kenya
- DHS*
- Livelihood
- Zones
- Population
- Spatial Interpolation: Geostatistical interpolation using a moving window regression
Jankowska et al.[20] Examine and project climate and health trends in the African Sahel through the spatial coupling of climate data and health data in Mali. - Spatial Statistics: Multivariate linear regression analysis - Climate Local and regional data/Meso-level analysis Prospective (1960–2039) Mali
- DHS*
- Livelihood
- Zones
- Physiography
- Population
- Spatial Interpolation: Geostatistical interpolation using a moving window regression
Kandala et al.[25] Investigate the geographical and socioeconomic determinants of childhood undernutrition. Explore regional patterns of undernutrition. - Spatial Statistics: Bayesian geo-additive regression model based on Markov priors - DHS* Local data/Meso-level analysis Retrospective (1992) Malawi, Tanzania and Zambia
- Socioeconomics
Liu et al.[21] Spatially explicit assessment of current and future hotspots of food insecurity in SSA. Analyzing the impact of climate change on crop production. - Spatial Modeling: Simulate dynamics of agricultural production - Climate Local and regional data/Meso-level analysis Prospective (1990–2030) Sub-Saharan Africa
- DHS*
- Economic
- Population
- Spatial Analysis: Hotspot analysis
Margai[22] Discuss the multi-dimensional causes of food insecurity conditions, analyze the relation- ships between food insufficiency and nutritional health outcomes among children, and identify the demographic, socio-economic and environmental correlates of these conditions. - Spatial Analysis: Road network distance analysis - Agriculture Household and regional data/Meso-level analysis Retrospective (1999) Burkina Faso
- Spatial Interpolation: Kriging algorithm - DHS*
- Spatial Statistics/Statistical Methods: Chi-square test, Logistic regression analysis - Infrastructure
Pawloski et al.[26] Examine geographic relationships of nutritional status, including underweight, overweight and obesity among Kenyan mothers and children. - Spatial Statistics: Getis–Ord General G Statistics, Gi* Statistic - DHS* Local data/Meso-level analysis Retrospective (2003/2006) Kenya
Rowhani et al.[18] Present the influence of the climate-induced changes of ecosystem resources on malnutrition and armed conflict. - Spatial Statistics: Logistic regression models - Agriculture Local and regional data/Micro- and meso-level analysis Retrospective (1946–2006) Sudan, Ethiopia and Somalia
- Economics
- Health
- Infrastructure
- Politics
Sherbinin[24] Determine if, when controlling for income and the health conditions, biophysical and geographical variables help to explain variation in the rates of child malnutrition. - Spatial Statistics: OLS Regression, Spatial Autocorrelation, Spatial Error (SE) model - Agriculture Regional data/Meso-level analysis Retrospective Africa
    - Climate    
    - Economics    
    - Health (+DHS*)    
    - Infrastructure    
    - Physiography    
    - Population    
  1. *The Demographic and Health Survey Program.