Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru
Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focu...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2014
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/149856 |
| _version_ | 1855526107281883136 |
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| author | Anderson, Weston Guikema, Seth Zaitchik, Ben Pan, William |
| author_browse | Anderson, Weston Guikema, Seth Pan, William Zaitchik, Ben |
| author_facet | Anderson, Weston Guikema, Seth Zaitchik, Ben Pan, William |
| author_sort | Anderson, Weston |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies. |
| format | Journal Article |
| id | CGSpace149856 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1498562025-01-24T08:54:17Z Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru Anderson, Weston Guikema, Seth Zaitchik, Ben Pan, William rivers roads transport population density land use censuses bayesian theory Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies. 2014 2024-08-01T02:50:05Z 2024-08-01T02:50:05Z Journal Article https://hdl.handle.net/10568/149856 en Open Access Public Library of Science Anderson, Weston; Guikema, Seth; Zaitchik, Ben; and Pan, William. 2014. Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru. PLoS ONE 9(7): e100037. https://doi.org/10.1371/journal.pone.0100037 |
| spellingShingle | rivers roads transport population density land use censuses bayesian theory Anderson, Weston Guikema, Seth Zaitchik, Ben Pan, William Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title | Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title_full | Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title_fullStr | Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title_full_unstemmed | Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title_short | Methods for estimating population density in data-limited areas: Evaluating regression and tree-based models in Peru |
| title_sort | methods for estimating population density in data limited areas evaluating regression and tree based models in peru |
| topic | rivers roads transport population density land use censuses bayesian theory |
| url | https://hdl.handle.net/10568/149856 |
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