TITLE: Methods Paper Using Household survey data to identify large-scale food security patterns across Uganda
AUTHORS: Jannike Wichern, Joost van Heerwaarden, Mark van Wijk

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><div class = "justify" style = "text-align:justify">To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. </div><style>
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							</style></div><div class = "text-block"><div class = "justify" style = "text-align:justify"><span>Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation </span><span style = "font-style:italic;">within</span><span> any given area often overrides the variability of livelihood activities between distant regions in the country.</span></div><style>
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[STEPS]
?. [Spatial interpolation of household survey data]
Step 1: Household level food availability analysisIn this step the household data are used to calculate food availability and the contributing activities on household level. To run the household level food availability analysis household survey data must be downloaded from the World Bank for 2010/11 for Uganda: http://microdata.worldbank.org/index.php/catalog/2166Run the household level food availability analysis (R-scripts attached, scripts 1.1. to 1.5). Make sure that the file sources are adjusted in the scripts LSMS_Tools.R and LSMS_201011_param.R to be able to run script 1.4_LSMS_201011_IA.R. Two csv-files attached provide parameters in LSMS_201011_param.R.
?. [Spatial interpolation of household survey data]
Step 2: Data preparation for regression analysisIn this step the household data and spatial data layers are prepared for the regression analysis.First, the LSMS household locations are randomly off-set (step 2.1.). Next, the soil carbon stock is calculated (2.2.). Then the spatial layers are sampled to the off-set household locations (step 2.3.).For step 2 the household output data from step 1 are needed as well as the following spatial data:Climate data: Hijmans et al. 2005, BioClim data version 1.4, http://www.worldclim.org/Elevation: Jarvis et al. 2008, http://www.cgiar-csi.org/data/Soil data: Hengl et al. 2017Population:Worldpop 2015, Worldpop.org.ukMarket access: Nelson 2008, http://forobs.jrc.ec.europa.eu/products/gam/download.phpLength of growing period: HarvestChoice 2015, Harvestchoice.orgDetails on the sources are in the paper Wichern et al. in Plos One.
?. [Spatial interpolation of household survey data]
Step 3: Regression analysisFor each variable of interest (see paper Wichern et al. in Plos One) regression models are built using the spatial variables from step 2 and household variables from step 1. Depending on the characteristics of the variable of interest, a multiple linear regression model (MLR, step 3.2.) or a multiple inflated  beta regression model (MIBR, step 3.1.) is used. Scripts here are examples for the variables banana contribution and food availability.
?. [Spatial interpolation of household survey data]
Step 4: Spatial interpolationOnce the regression model is built, the model is used to spatially interpolate the variable of interest. Depending on the regression model type, the spatial interpolation either is done one a multiple linear regression model (4.2.) or on a multiple inflated beta regression model (step 4.1.).