Mapping The Spatial Distribution Of Poverty Using Satellite Imagery In Thailand
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Mapping The Spatial Distribution Of Poverty Using Satellite Imagery In Thailand
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Author : Asian Development Bank
language : en
Publisher: Asian Development Bank
Release Date : 2021-04-01
Mapping The Spatial Distribution Of Poverty Using Satellite Imagery In Thailand written by Asian Development Bank and has been published by Asian Development Bank this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-01 with Business & Economics categories.
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.
Mapping The Spatial Distribution Of Poverty Using Satellite Imagery In The Philippines
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Author : Asian Development Bank
language : en
Publisher: Asian Development Bank
Release Date : 2021-03-01
Mapping The Spatial Distribution Of Poverty Using Satellite Imagery In The Philippines written by Asian Development Bank and has been published by Asian Development Bank this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-01 with Business & Economics categories.
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.
Mapping Poverty Through Data Integration And Artificial Intelligence
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Author : Asian Development Bank
language : en
Publisher:
Release Date : 2020-09
Mapping Poverty Through Data Integration And Artificial Intelligence written by Asian Development Bank and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09 with categories.
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.
Help
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Author : Fleur Johns
language : en
Publisher: Oxford University Press
Release Date : 2023-02-14
Help written by Fleur Johns and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-14 with categories.
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.
A Guidebook On Mapping Poverty Through Data Integration And Artificial Intelligence
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Author : Asian Development Bank
language : en
Publisher:
Release Date : 2021-05-03
A Guidebook On Mapping Poverty Through Data Integration And Artificial Intelligence written by Asian Development Bank and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-03 with categories.
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. 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.
Where Are The Poor
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Author : Norbert Henninger
language : en
Publisher:
Release Date : 2002
Where Are The Poor written by Norbert Henninger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Poverty categories.
Publ. in association with UNEP/GRID-Arendal, Norway.
Combining Census And Survey Data To Study Spatial Dimensions Of Poverty A Case Study Of Ecuador
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Author : Peter F. Lanjouw
language : en
Publisher:
Release Date : 2016
Combining Census And Survey Data To Study Spatial Dimensions Of Poverty A Case Study Of Ecuador written by Peter F. Lanjouw and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Combining sample survey data and census data can yield predicted poverty rates for all households covered by the census. This offers a means to construct detailed poverty maps. But standard errors on the estimated poverty rates are not negligible.Poverty maps, providing information on the spatial distribution of living standards, are an important tool for policymaking and economic research. Policymakers can use such maps to allocate transfers and inform policy design. The maps can also be used to investigate the relationship between growth and distribution inside a country, thereby complementing research using cross-country regressions. The development of detailed poverty maps is difficult because of data constraints. Household surveys contain data on income or consumption but are typically small. Census data cover a large sample but do not generally contain the right information. Poverty maps based on census data but constructed in an ad-hoc manner can be unreliable.Hentschel, Lanjouw, Lanjouw, and Poggi demonstrate how sample survey data and census data can be combined to yield predicted poverty rates for all households covered by the census. This represents an improvement over ad hoc poverty maps. However, standard errors on the estimated poverty rates are not negligible, so additional efforts to cross-check results are warranted.This paper - a joint product of the Development Research Group and the Poverty Reduction and Economic Management Network, Poverty Division - is part of a larger effort in the Bank to study the spatial distribution and determinants of poverty. Jesko Hentschel may be contacted at [email protected].
Locating The Poor Spatially Disaggregated Poverty Maps For Sri Lanka
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Author : Amarasinghe, Upali, Samad, Madar, Anputhas, Markandu
language : en
Publisher: IWMI
Release Date : 2005
Locating The Poor Spatially Disaggregated Poverty Maps For Sri Lanka written by Amarasinghe, Upali, Samad, Madar, Anputhas, Markandu and has been published by IWMI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Poor categories.
This report presents the results of subnational poverty estimation using aggregate poverty statistics and how they can help policy interventions. In particular, they estimate the poverty map across the DS division level in Sri Lanka. The poverty map depicts the proportion of households below the poverty line, which is based on household expenditure for food for obtaining the minimum calorie requirement.
The Application Of A Spatial Regression Model To The Analysis And Mapping Of Poverty
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Author : Alessandra Petrucci
language : en
Publisher: FAO
Release Date : 2003
The Application Of A Spatial Regression Model To The Analysis And Mapping Of Poverty written by Alessandra Petrucci and has been published by FAO this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Ecuador categories.
Poverty mapping in developing countries is used to identify ways to improve living standards and, until now, methods have been generally based on econometric models which do not take into account the spatial dependence that may exist in human societies, with regard to income distribution. This report uses spatial regression techniques to model more accurately the distribution of poverty across regions in Ecuador.
The Spatial Distribution Of Poverty In Vietnam And The Potential For Targeting
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Author : Nicholas Minot
language : en
Publisher: World Bank Publications
Release Date : 2002
The Spatial Distribution Of Poverty In Vietnam And The Potential For Targeting written by Nicholas Minot and has been published by World Bank Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Anti-Poverty categories.
Abstract: Minot and Baulch combine household survey and census data to construct a provincial poverty map of Vietnam and evaluate the accuracy of geographically targeted antipoverty programs. First, they estimate per capita expenditure as a function of selected household and geographic characteristics using the 1998 Vietnam Living Standards Survey. Next, they combine the results with data on the same household characteristics from the 1999 census to estimate the incidence of poverty in each province. The results show that rural poverty is concentrated in 10 provinces in the Northern Uplands, 2 provinces in the Central Highlands, and 2 provinces in the Central Coast. The authors use Receiver Operating Characteristics curves to evaluate the effectiveness of geographic targeting. The results show that the existing poor communes system excludes large numbers of poor people, but there is potential for sharpening poverty targeting using a small number of easy-to-measure household characteristics. This paper is a joint product of Macroeconomics and Growth, Development Research Group, and the International Food Policy Research Institute. The authors may be contacted at n.minot@@cgiar.org or b.baulch@@lds.ac.uk.