Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
- Publication Date
- February 17, 2015
- Journal
- PLOS ONE
- Authors
- Forrest R. Stevens, Andrea E. Gaughan, Catherine Linard & Andrew J. Tatem
- Volume
- 10
- Issue
- 2
- Pages
- e0107042
- DOI
- https://dx.plos.org/10.1371/journal.pone.0107042
- Publisher URL
- http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0107042
- PubMed
- http://www.ncbi.nlm.nih.gov/pubmed/25689585
- PubMed Central
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331277
- Europe PMC
- http://europepmc.org/abstract/MED/25689585
- Web of Science
- 000350322700001
- Scopus
- 84923279600
- Mendeley
- http://www.mendeley.com/research/disaggregating-census-data-population-mapping-using-random-forests-remotelysensed-ancillary-data-1
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Mendeley | Further Information
{"title"=>"Disaggregating census data for population mapping using Random forests with remotely-sensed and ancillary data", "type"=>"journal", "authors"=>[{"first_name"=>"Forrest R.", "last_name"=>"Stevens", "scopus_author_id"=>"41762789100"}, {"first_name"=>"Andrea E.", "last_name"=>"Gaughan", "scopus_author_id"=>"23491815700"}, {"first_name"=>"Catherine", "last_name"=>"Linard", "scopus_author_id"=>"16307405100"}, {"first_name"=>"Andrew J.", "last_name"=>"Tatem", "scopus_author_id"=>"6603035928"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0107042", "sgr"=>"84923279600", "issn"=>"19326203", "pui"=>"602339344", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"25689585", "scopus"=>"2-s2.0-84923279600"}, "id"=>"e6d43e1f-01c2-3807-bfc2-8a9b4654172d", "abstract"=>"High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.", "link"=>"http://www.mendeley.com/research/disaggregating-census-data-population-mapping-using-random-forests-remotelysensed-ancillary-data-1", "reader_count"=>143, "reader_count_by_academic_status"=>{"Unspecified"=>8, "Professor > Associate Professor"=>3, "Researcher"=>33, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>4, "Student > Master"=>24, "Other"=>7, "Student > Bachelor"=>11, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>8, "Professor > Associate Professor"=>3, "Researcher"=>33, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>4, "Student > Master"=>24, "Other"=>7, "Student > Bachelor"=>11, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>13, "Agricultural and Biological Sciences"=>17, "Arts and Humanities"=>2, "Computer Science"=>15, "Earth and Planetary Sciences"=>21, "Economics, Econometrics and Finance"=>4, "Energy"=>1, "Engineering"=>15, "Environmental Science"=>30, "Mathematics"=>2, "Medicine and Dentistry"=>6, "Design"=>3, "Social Sciences"=>14}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>14}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>13}, "Environmental Science"=>{"Environmental Science"=>30}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Design"=>{"Design"=>3}, "Engineering"=>{"Engineering"=>15}, "Energy"=>{"Energy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>21}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>15}}, "reader_count_by_country"=>{"Sweden"=>1, "Czech Republic"=>1, "United States"=>6, "Sri Lanka"=>1, "Japan"=>3, "Brazil"=>2, "United Kingdom"=>4, "Italy"=>1, "Mexico"=>1}, "group_count"=>13}CrossRef
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Scopus | Further Information
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- {"id"=>"568817767652012032", "text"=>"New WorldPop paper in #PLOSONE: Disaggregating Census Data for Population Mapping Using Random Forests ... http://t.co/uzkZRkUVEf", "created_at"=>"2015-02-20T17:01:38Z", "user"=>"CatherineLinard", "user_name"=>"Catherine Linard", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2150197647/afripop3_normal.jpg"}
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PMC Usage Stats | Further Information
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Relative Metric
{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}Loading … 

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