scholarly journals Mapping the Spatial Distribution of Poverty Using Satellite Imagery in Thailand

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Survey Methods ◽  
Asian Development Bank ◽  
Data Sources ◽  
Development Bank ◽  
High Quality ◽  
2030 Agenda ◽  
Algorithmic Techniques ◽  
National Trends ◽  
National Statistical Office

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.

scholarly journals Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines

2021 ◽  
Keyword(s):  
Satellite Imagery ◽  
The Philippines ◽  
Asian Development Bank ◽  
Machine Learning Algorithms ◽  
Sample Survey ◽  
Development Bank ◽  
High Quality ◽  
2030 Agenda ◽  
The World ◽  
National Trends

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 data collection and sample survey techniques.conventional

scholarly journals Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines

2021 ◽  
Keyword(s):  
Satellite Imagery ◽  
The Philippines ◽  
Asian Development Bank ◽  
Machine Learning Algorithms ◽  
Sample Survey ◽  
Development Bank ◽  
Survey Techniques ◽  
2030 Agenda ◽  
The World ◽  
National Trends

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.

scholarly journals Practical Guidebook on Data Disaggregation for the Sustainable Development Goals

2021 ◽  
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Asian Development Bank ◽  
Development Bank ◽  
The Sustainable Development ◽  
2030 Agenda ◽  
Data Disaggregation ◽  
Development Goals ◽  
Disaggregated Data ◽  
Nations Department

The “leave no one behind” principle espoused by the 2030 Agenda for Sustainable Development requires measures of progress for different segments of the population. This entails detailed disaggregated data to identify subgroups that might be falling behind, to ensure progress toward achieving the Sustainable Development Goals (SDGs). The Asian Development Bank and the Statistics Division of the United Nations Department of Economic and Social Affairs developed this practical guidebook with tools to collect, compile, analyze, and disseminate disaggregated data. It also provides materials on issues and experiences of countries regarding data disaggregation for the SDGs. This guidebook is for statisticians and analysts from planning and sector ministries involved in the production, analysis, and communication of disaggregated data.

scholarly journals A Guidebook on Mapping Poverty through Data Integration and Artificial Intelligence

2021 ◽  
Keyword(s):  
The Philippines ◽  
Machine Learning Algorithms ◽  
Sample Survey ◽  
Development Bank ◽  
Survey Techniques ◽  
2030 Agenda ◽  
The Pacific ◽  
Special Supplement ◽  
Key Indicators ◽  
National Statistical Office

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 Asian Development Bank, in collaboration with the Philippine Statistics Authority, the National Statistical Office of Thailand, and the World Data Lab, conducted a feasibility study that aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines and Thailand. This accompanying guide to the Key Indicators for Asia and the Pacific 2020 special supplement is based on the study, capitalizing on satellite imagery, geospatial data, and powerful machine-learning algorithms to augment conventional data collection and sample survey techniques.

scholarly journals Prioritising recommendations following analyses of adverse events in healthcare: a systematic review

2020 ◽  
Vol 9 (4) ◽  
pp. e000843
Author(s):  
Kelly Bos ◽  
Maarten J van der Laan ◽  
Dave A Dongelmans
Keyword(s):  
Systematic Review ◽  
Adverse Events ◽  
Data Extraction ◽  
Data Sources ◽  
Cochrane Library ◽  
High Quality ◽  
Data Synthesis ◽  
Study Selection ◽  
Personal Opinion ◽  
User Friendly

PurposeThe purpose of this systematic review was to identify an appropriate method—a user-friendly and validated method—that prioritises recommendations following analyses of adverse events (AEs) based on objective features.Data sourcesThe electronic databases PubMed/MEDLINE, Embase (Ovid), Cochrane Library, PsycINFO (Ovid) and ERIC (Ovid) were searched.Study selectionStudies were considered eligible when reporting on methods to prioritise recommendations.Data extractionTwo teams of reviewers performed the data extraction which was defined prior to this phase.Results of data synthesisEleven methods were identified that are designed to prioritise recommendations. After completing the data extraction, none of the methods met all the predefined criteria. Nine methods were considered user-friendly. One study validated the developed method. Five methods prioritised recommendations based on objective features, not affected by personal opinion or knowledge and expected to be reproducible by different users.ConclusionThere are several methods available to prioritise recommendations following analyses of AEs. All these methods can be used to discuss and select recommendations for implementation. None of the methods is a user-friendly and validated method that prioritises recommendations based on objective features. Although there are possibilities to further improve their features, the ‘Typology of safety functions’ by de Dianous and Fiévez, and the ‘Hierarchy of hazard controls’ by McCaughan have the most potential to select high-quality recommendations as they have only a few clearly defined categories in a well-arranged ordinal sequence.

scholarly journals Caring for Older Patients With Cancer: Estimates of the Prevalence, Burden, and Unmet Needs of Caregivers in the United States

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 690-690
Author(s):  
Erin Kent
Keyword(s):  
Family Caregivers ◽  
Older Patients ◽  
Unmet Needs ◽  
The United States ◽  
Population Based ◽  
Data Sources ◽  
Behavioral Risk ◽  
Ripple Effect ◽  
The Us ◽  
National Trends

Abstract In 2020, ~1.8 million Americans are expected to be newly diagnosed with cancer, with approximately 70% of cases diagnosed over the age of 65. Cancer can have a ripple effect, impacting not just patients themselves, but their family caregivers. This presentation will provide an overview of the estimates of the number of family caregivers caring for individuals with cancer in the US, focusing on older patients, from several population-based data sources: Caregiving in the US 2020, the Health Information National Trends Survey (HINTS, 2017-2019), the Behavioral Risk Factors Surveillance System (BRFSS, 2015-2019), and the National Health and Aging Trends (NHATS) Survey. The presentation will compare features of the data sources to give a comprehensive picture of the state of cancer caregiving. In addition, the presentation will highlight what is known about the experiences of cancer caregivers, including caregiving characteristics, burden, unmet needs, and ideas for improving support for family caregivers.

scholarly journals A dataset on affiliation of venture capitalists in China between 2000 and 2016

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma
Keyword(s):  
Data Sources ◽  
Venture Capitalists ◽  
High Quality ◽  
Public Agency ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Multi Stage ◽  
The World ◽  
Prior Literature ◽  
Innovation And Entrepreneurship

AbstractPrior literature emphasizes the distinct roles of differently affiliated venture capitalists (VCs) in nurturing innovation and entrepreneurship. Although China has become the second largest VC market in the world, the unavailability of high-quality datasets on VC affiliation in China’s market hinders such research efforts. To fill up this important gap, we compiled a new panel dataset of VC affiliation in China’s market from multiple data sources. Specifically, we drew on a list of 6,553 VCs that have invested in China between 2000 and 2016 from CVSource database, collected VC’s shareholder information from public sources, and developed a multi-stage procedure to label each VC as the following types: GVC (public agency-affiliated, state-owned enterprise-affiliated), CVC (corporate VC), IVC (independent VC), BVC (bank-affiliated VC), FVC (financial/non-bank-affiliated VC), UVC (university endowment/spin-out unit), and PenVC (pension-affiliated VC). We also denoted whether a VC has foreign background. This dataset helps researchers conduct more nuanced investigations into the investment behaviors of different VCs and their distinct impacts on innovation and entrepreneurship in China’s context.

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