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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
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)
Automatic Processing
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
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
Study Area
• East Africa
• Cluster map
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
Map
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
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
“The goal is to turn data into
information, and information into insight”
By Carly Fiorina

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

  • 1. 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
  • 2. 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
  • 3. 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
  • 4. Introduction • Cost • Time Socioeconomic Data Free Data Open source software
  • 5. 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
  • 6. 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)
  • 8. 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
  • 9. DHS Data Download Data download [www.dhsprogram.com] How the data is saved Inside country folders
  • 10. Data Extraction Main data extracted • Household economic activity • Country specific livestock variable
  • 11. Study Area • East Africa • Cluster map
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. Map
  • 17. 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
  • 18. 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
  • 19. 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 “The goal is to turn data into information, and information into insight” By Carly Fiorina