The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank (ADB), in collaboration with the National Statistical Office of Thailand and the Word Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand. This report documents the results of the study, providing insights on data collection requirements, advanced algorithmic techniques, and validation of poverty estimates using artificial intelligence to complement traditional data sources and conventional survey methods.

The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank, in collaboration with the Philippine Statistics Authority and the World Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines. This report documents the results of the study, which capitalized on satellite imagery, geospatial data, and powerful machine learning algorithms to augment conventional data collection and sample survey techniques.

Like many other areas of life, humanitarian practice and thinking are being transformed by information and communications technology. Despite this, the growing digitization of humanitarianism has been a relatively unnoticed dimension of global order. Based on more than seven years of data collection and interdisciplinary research, #Help presents a ground-breaking study of digital humanitarianism and its ramifications for international law and politics. Global problems and policies are being reconfigured, regulated, and addressed through digital interfaces developed for humanitarian ends. #Help analyses how populations, maps, and emergencies take shape on the global plane when given digital form and explores the reorientation of nation states' priorities and practices of governing around digital data collection imperatives. This book also illuminates how the growing prominence of digital interfaces in international humanitarian work is sustained and shaped by law and policy. #Help reveals new vectors of global inequality and new forms of global relation taking effect in the here and now. To understand how major digital platforms are seeking to extend their serviceable lives, and to see how global order might take shape in the future, it is essential to grasp the perils and possibilities of digital humanitarianism. #Help will transform thinking about what is at stake in the use of digital interfaces in the humanitarian field and about how, where, and for whom we are making the global order of tomorrow.

This special supplement to the Key Indicators for Asia and the Pacific 2020 discusses how poverty estimates can be enhanced by integrating household surveys and censuses with data extracted from satellite imagery. As part of a special ADB knowledge initiative, computer vision techniques and machine-learning algorithms were applied on datasets from the Philippines and Thailand to demonstrate increased granularity of poverty estimation using artificial intelligence. The report identifies practical considerations and technical requirements for this novel approach to mapping the spatial distribution of poverty. It also outlines the investments required by national statistics offices to fully capitalize on the benefits of incorporating innovative data sources into conventional work programs.

.."in many developing countries, there are large differences in economic conditions and the standard of living between regions, and even between communities within the same region. In many countries, poverty has a clear geographic dimension, since the poor are often concentrated in pockets of poverty. Therefore, the design of poverty alleviation policies must also have a signficant spatial component." Although development projects are carefully designed and meticulously evaluated for cost effectiveness and benefits, too many of them are not sufficiently targeted geographically. The growing availability and use of spatial data, organized in a computer system such as a geographical information system (GIS), makes it more feasible to analyze the impact of projects in specific locales and to achieve more effective targeting. 'Geographical Targeting for Poverty Alleviation' introduces the basic concepts of a GIS. It also demonstrates how to organize geographic and nongeographic data. In addition, it presents different methods for using the data of the Household Income and Expenditure Survey, together with other surveys and the population census, to provide estimates for the standard of living and the incidence of poverty incidence in different geographical areas of a country. Ultimately, these estimates should be used to establish guidelines for targeting poverty alleviation projects. This publication illustrates different GIS applications for identifying the project's target population, determining the project's spatial 'sphere of influence' or deciding where to locate public facilities. This publication is of interest to task managers, economists, development researchers, and geographers.

Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others. This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan that apply spatial analysis including autocorrelation, fuzzy, voronoi, cellular automata, analytic hierarchy process, artificial neural network, spatial metrics, spatial statistics, regression, and remote sensing mapping techniques are compiled comprehensively. The core value of this book is a wide variety of results with state of the art discussion including empirical case studies. It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues. We are pleased to announce that this book has been presented with the 2011 publishing award from the GIS Association of Japan. We would like to congratulate the authors!

Satellite images acquired at night provide a visually arresting perspective of the Earth and the human activities that light up the otherwise mostly dark Earth. These night-time light satellite images can be compiled into a geospatial time series that represent an invaluable source of information for both the natural and social sciences. Night-time light remote sensing has been shown to be particularly useful for a range of natural science and social science applications, including studies relating to urban development, demography, sociology, fishing activity, light pollution and the consequences of civil war. Key sensors for these time-series include the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite Day/Night Band (Suomi NPP/VIIRS DNB). An increasing number of alternative sources are also available, including high spatial resolution and multispectral sensors. This book captures key methodological issues associated with pre-processing night-time light data, documents state of the art analysis methods, and explores a wide range of applications. Major sections focus on NPP/VIIRS DNB processing; inter-calibration between NPP/VIIRS and DMPS/OLS; applications associated with socio-economic activities, applications in monitoring urbanization; and fishing activity monitoring. The chapters in this book were originally published as a special issue of the International Journal of Remote Sensing.

This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.

This atlas portrays Thailand's spatial structures and presents the country's social and economic development in a territorial context. The Kingdom of Thailand has undergone many changes throughout its long history, and most recently during its vigorous growth from the middle of the 1980s. The maps and text give a comprehensive interpretation of Thailand's internal dynamics as well as its regional and global integration. This is the first atlas of its kind for Thailand. It includes a wide range of spatial information and maps using various computer-assisted techniques. Seventy plates of maps, accompanied with commentary, cover significant topics such as: Thailand's relation to the world-system, its place in Eastern Asia, and its population, infrastructure, urban network, production, income, education, intra-regional dynamics. The volume brings together experts in a variety of fields and methods. It will be a valuable tool for teachers and students, planners and entrepreneurs -- indeed, for anyone eager to understand recent changes and prepare future diagnoses. Book jacket.