Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines
(eBook)
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Language
English
ISBN
9789292621322
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Citations
APA Citation, 7th Edition (style guide)
Various Authors., & Various Authors|AUTHOR. (2021). Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines . Asian Development Bank.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Various Authors and Various Authors|AUTHOR. 2021. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines. Asian Development Bank.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Various Authors and Various Authors|AUTHOR. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines Asian Development Bank, 2021.
MLA Citation, 9th Edition (style guide)Various Authors, and Various Authors|AUTHOR. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines Asian Development Bank, 2021.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouping Information
| Grouped Work ID | 1e972b68-ab70-5bdf-f5e7-0042c90c649a-eng |
|---|---|
| Full title | mapping the spatial distribution of poverty using satellite imagery in the philippines |
| Author | authors various |
| Grouping Category | book |
| Last Update | 2023-08-03 21:00:44PM |
| Last Indexed | 2024-04-17 02:23:02AM |
Book Cover Information
| Image Source | hoopla |
|---|---|
| First Loaded | Jul 22, 2023 |
| Last Used | Jan 26, 2024 |
Hoopla Extract Information
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[synopsis] => 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.
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