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Author: Corral
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Corral
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Asian Development Bank
Publisher: Asian Development Bank
ISBN: 9292627694
Category : Business & Economics
Languages : en
Pages : 141
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Book Description
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.
Author: Asian Development Bank
Publisher:
ISBN: 9789292627850
Category :
Languages : en
Pages : 274
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Book Description
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.
Author:
Publisher:
ISBN: 9789292623142
Category : Artificial intelligence
Languages : en
Pages : 54
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Book Description
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.
Author: Asian Development Bank
Publisher:
ISBN: 9789292623135
Category :
Languages : en
Pages : 54
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Book Description
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.
Author: Peizan Sheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This paper develops a machine learning approach to estimate internationally-and-intertemporally comparable, decomposable, structural asset poverty measures. These measures are founded in theory, link directly to official poverty lines, and are amenable to ML-based prediction using Earth Observation data. Using household survey data from Tanzania, Uganda, and Malawi, we model the relationship between household consumption expenditures and productive assets, directly linking flow-based poverty measures with asset-based structural poverty measures. The poverty measures we construct can serve as new, improved dependent variables for ML poverty prediction. We also assess whether our poverty estimates vary from readily available poverty estimates and whether this difference in poverty measures matters.
Author: Tara Bedi
Publisher: World Bank Publications
ISBN: 0821369326
Category : Social Science
Languages : en
Pages : 308
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Book Description
The allocation of resources and the design of policies tailored to local-level conditions require highly disaggregated information. Data on poverty at the local level is typically not available because most household surveys are not representative past the regional level. This volume aims to promote the effective use of Small Area Estimation poverty maps in policy making. It presents the range of policies and interventions which have been informed by poverty maps, focusing on the political economy of poverty maps and the key elements to their effective use by policy makers. The volume also looks at the future of poverty maps in terms of new techniques and new areas of application.
Author: Monica Pratesi
Publisher: John Wiley & Sons
ISBN: 1118815017
Category : Mathematics
Languages : en
Pages : 485
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Book Description
A comprehensive guide to implementing SAE methods for poverty studies and poverty mapping There is an increasingly urgent demand for poverty and living conditions data, in relation to local areas and/or subpopulations. Policy makers and stakeholders need indicators and maps of poverty and living conditions in order to formulate and implement policies, (re)distribute resources, and measure the effect of local policy actions. Small Area Estimation (SAE) plays a crucial role in producing statistically sound estimates for poverty mapping. This book offers a comprehensive source of information regarding the use of SAE methods adapted to these distinctive features of poverty data derived from surveys and administrative archives. The book covers the definition of poverty indicators, data collection and integration methods, the impact of sampling design, weighting and variance estimation, the issue of SAE modelling and robustness, the spatio-temporal modelling of poverty, and the SAE of the distribution function of income and inequalities. Examples of data analyses and applications are provided, and the book is supported by a website describing scripts written in SAS or R software, which accompany the majority of the presented methods. Key features: Presents a comprehensive review of SAE methods for poverty mapping Demonstrates the applications of SAE methods using real-life case studies Offers guidance on the use of routines and choice of websites from which to download them Analysis of Poverty Data by Small Area Estimation offers an introduction to advanced techniques from both a practical and a methodological perspective, and will prove an invaluable resource for researchers actively engaged in organizing, managing and conducting studies on poverty.
Author: Felix Agyemang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
High resolution poverty mapping supports evidence-based policy and research, yet about half of countries lack the requisite survey data to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning techniques are increasingly used to create small-area estimates of poverty in low- and middle-income countries (LMICs). Convolutional Neural Networks (CNN) trained on satellite imagery are one of the most popular and effective approaches in this literature. However, the spatial resolution of poverty estimates has remained quite coarse, particularly in rural areas which are critical for governments to support. To resolve this, we use an ensemble transfer learning approach involving three CNN models to predict chronic poverty at a finer 1 km2 scale in rural Sindh, Pakistan. We train the model with spatially noisy georeferenced household survey containing poverty scores for 1.9 million anonymized households in Sindh Province using publicly available inputs, including daytime and nighttime satellite imagery and accessibility data. Results from rigorous cross-validation and ground truthing of predictions with an original survey suggest the model performs well in identifying the chronic poor in both arid and non-arid regions, outperforming previous studies in key accuracy metrics. Our inexpensive and scalable approach could be used to improve poverty targeting in low- and middle-income countries.
Author: UNESCO
Publisher: UNESCO Publishing
ISBN: 9231005170
Category : Political Science
Languages : en
Pages : 70
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Book Description
