A Guidebook on Mapping Poverty through Data Integration and Artificial Intelligence
This guidebook identifies tools and resources that can help generate poverty statistics using satellite imagery, geospatial data, and machine-learning algorithms to augment conventional data collection and sample survey techniques.
- US$47.00 (paperback)
The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators to be estimated for different segments of a country’s population. The guidebook was based on a feasibility study by ADB, in collaboration with the Philippine Statistics Authority, the National Statistical Office of Thailand, and the World Data Lab, that aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics. It also serves as an accompanying guide to the Key Indicators for Asia and the Pacific 2020 special supplement focusing on mapping poverty estimates.
- Hardware and Software Requirements and Setup
- Data Preparation
- Training of Convolutional Neural Network
- Convolutional Neural Network Model Feature Extraction
- Ridge Regression
- Rescaling of Poverty Estimates and Visualization