Výsledky vyhledávání - "Jennifer Joan Unangst"

A Comparison of Geosampling and Random Walk Methods for Household Sample Selection in Uttar Pradesh, India

Autor: John David Bunker, Stephanie Anne Eckman, Justine Allpress, Pramod Bhatt, Jennifer Joan Unangst, Kasey Jones, Kelsey Starr, Jamie Cajka, Jonathan Evans, Charles Q. Lau, Lisa Thalji, Martha McRoy

Publikováno v: International Journal of Public Opinion Research. 33:1062-1073

Our research evaluates an innovative sampling technique for household surveys called “geosampling” which leverages recent advances in geographic information systems, computer vision algorithms, and satellite imagery. We compare geosampling to the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d5bd26b77df1e04219663a23007aa4ac
https://doi.org/10.1093/ijpor/edab020

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A Process for Decomposing Total Survey Error in Probability and Nonprobability Surveys: A Case Study Comparing Health Statistics in US Internet Panels

Autor: Sarita Lynn Karon, Jill Dever, Abigail Rose Ferrell, Jennifer Joan Unangst, Jennifer Howard, Ashley Amaya, Herschel Lisette Pang Sy Sanders

Publikováno v: Journal of Survey Statistics and Methodology. 8:62-88

As survey methods evolve, researchers require a comprehensive understanding of the error sources in their data. Comparative studies, which assess differences between the estimates from emerging survey methods and those from traditional surveys, are a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4b420cc62acee9479a563e7ed88bb6f2
https://doi.org/10.1093/jssam/smz040

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Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery

Autor: Safaa Amer, Rob Chew, Justine Allpress, Jennifer Joan Unangst, James Cajka, Kasey Jones, Mark Bruhn

Publikováno v: International Journal of Health Geographics
International Journal of Health Geographics, Vol 17, Iss 1, Pp 1-17 (2018)

Background Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07f67ce79514569e8256c92802b43a12
https://doi.org/10.1186/s12942-018-0132-1

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Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning

Autor: Justine Allpress, Jennifer Joan Unangst, Kasey Jones, Karol P. Krotki, Rob Chew, Safaa Amer, James Cajka

Publikováno v: ISPRS International Journal of Geo-Information, Vol 7, Iss 11, p 448 (2018)
ISPRS International Journal of Geo-Information
Volume 7
Issue 11

While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba96015fbe07d166e8e79c761cd649c1
https://doi.org/10.3390/ijgi7110448

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