Mapping livestock keepers and their herds across Africa based on household surveys

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This document summarizes a presentation about mapping livestock keepers and their herds across Africa using household survey data. The presentation: - Describes how Demographic and Health Survey (DHS) data from over 90 countries was extracted and prepared for analysis. - Demonstrates using DHS data from East Africa, random forest modeling was used to predict cattle ownership at the cluster level based on covariates like precipitation, land cover, and night light data. - Discusses next steps like adjusting the dataset to conform with census data, incorporating temporal aspects, and using the methods to map other socioeconomic variables beyond livestock ownership.

Mapping Livestock Keepers and Their Herds Across Africa Based
on Household Surveys
Stephen Oloo & Catherine Pfeifer, ILRI
FOSS4G 2018 Conference, Dar es Salaam
28 August – 3 September, 2018

Content
Background
•Introduction
•DHS data
•Automatic Processing
Demonstration
•DHS Data Download
•Data Extraction
•Study Area
•DHS Data Preparation
•Data modeling and prediction
Way Forward
•Data Adjustment
•Other Applications

Introduction
• Cost
• Time
Socioeconomic Data
Free Data Open source software

Demographic Health Survey (DHS) data
• DHS program was initiated in 1984 by USAID
• DHS program carries out nationally representative demographic and health
surveys in developing countries in collaboration with the statistical body of
the respective countries
• Main aim of DHS data is to support informed decision making in
developing countries
• So far, more than 300 nationally representative surveys have been carried
out in about 90 countries

Spatiality of DHS data
Cluster coordinates :
Cluster is a group of 20-40 households
For the participants to remain anonymous the GPS coordinates
for the cluster are randomly shifted (2km in urban, 5km in rural
area)

DHS Data Download
Data download
[www.dhsprogram.com]
How the data is saved
Inside country folders

Data Extraction
Main data extracted
• Household economic activity
• Country specific livestock variable

DHS Data Preparation
• DHS surveys are complex surveys that is;
• They are stratified
• They are clustered
• They are random
• Sampling error was corrected using sample weights, this was done using
srvyr package
• Data aggregated per cluster for household that have got at least one cattle

Data Modelling and Prediction
Random Forest
• It is a machine learning technique
that uses decision trees
• At each node there is split based on
how much the new partition reduces
the model error

Data Modelling and Prediction: Covariates
• Livestock production systems
• Precipitation
• DEM
• Population
• Land Cover
• Night Light
• NDVI_a0
• NDVI_a1
• NDVI_a2
• NDVI_a3
• Crop Cover
• Travel Time
• Slope

Data Modelling and Prediction
Full Model
• Mean of squared residuals:
0.03818643
• % Var explained: 54.84
Model after selection using VSURF
package
• Mean of squared residuals: 0.0374028
• % Var explained: 55.76

Way Forward
Data Adjustment
• Adjust the dataset to conform with the United Nations Population
Data
• Where there is recent census data, adjust the dataset to conform
with the census data
• Incorporate temporal aspect of the data
Other Application
• The dataset and code can be used to map other socioeconomic
such mobile phone ownership, access to information, nutrition

This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
better lives through livestock
ilri.org
ILRI thanks all donors and organizations which globally support its work through their contributions
to the CGIAR Trust Fund
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This document summarizes a presentation on mapping livestock keepers and their herds across Africa based on household survey data. It discusses extracting relevant data from Demographic and Health Surveys, preparing the data, using random forest modeling to predict livestock ownership based on geographic and environmental covariates, and creating maps of the predictions. It also addresses future work on adjusting the data and models and applying the approach to map other socioeconomic variables.

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