Estimating the level of income in individual buildings using data from household interview surveys and satellite imagery: Case study in Myanmar and Nicaragua

Abstract

Leaving no one behind is a worldwide goal, but it is difficult to make policy to address this issue because we do not have a thorough knowledge of where poverty exists and in what forms due to lack of data, particularly in developing countries. Household interview surveys are the common way to collect such information, but conducting large-scale surveys frequently is difficult from the perspective of cost and time. Here, we show a novel method for estimating income levels of individual building in urban and peri-urban rural areas. The combination of high-resolution satellite imagery and household interview survey data obtained by visiting households on the ground makes it possible to estimate income levels at a detailed scale for the first time. These data are often handled in different academic disciplines and are rarely used in combination. Using the results, we can determine the number and location of poor people at the local scale. We can also identify areas with particularly high concentrations of poor people. This information enables planning and policy making for more effective poverty reduction and disaster prevention measures tailored to local conditions. Thus, the results of this study will help developing countries to achieve sustainable development.

Keywords

• Poverty
• deep learning
• satellite imagery
• household interview survey