Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
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{"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}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1908593"], "description"=>"<p>* The variable names are used in Random Forest model output and throughout the text as reference to the specific data they were derived from. The first three letters are derived from the data type (e.g. “<i>lan</i>” indicates land cover) and the last three letters, if present, indicates what type of data each variable represents (e.g. “_<i>cls</i>” is a binary classification and “_<i>dst</i>” is a calculated Euclidean distance-to variable.</p><p><sup>†</sup> The default data for populated places is merged from several VMAP0 data sources. There are three VMAP0 data sets used: The point data <i>pop/builtupp</i> and <i>pop/mispopp</i> are buffered to 100 m and merged with the <i>pop/builtupa</i> polygons creating avector-based built layer. This layer is then converted to binary class and distance-to rasters for use in modeling.</p><p>Country-specific data sources and variable names used for population density estimation used for dasymetric weights.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310720, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.t001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_data_sources_and_variable_names_used_for_population_density_estimation_used_for_dasymetric_weights_/1310720", "title"=>"Country-specific data sources and variable names used for population density estimation used for dasymetric weights.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908621", "https://ndownloader.figshare.com/files/1908622", "https://ndownloader.figshare.com/files/1908623"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310732, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0107042.s001", "https://dx.doi.org/10.1371/journal.pone.0107042.s002", "https://dx.doi.org/10.1371/journal.pone.0107042.s003"], "stats"=>{"downloads"=>2, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Disaggregating_Census_Data_for_Population_Mapping_Using_Random_Forests_with_Remotely_Sensed_and_Ancillary_Data_/1310732", "title"=>"Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908581"], "description"=>"<p>The orange boxes represent items that are specific to the research presented here and not part of end-user map data product generation. The green boxes represent data pre-processing stages. Items in blue represent Random Forest model estimation, per-pixel prediction and dasymetric redistribution of census counts.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310708, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g001", "stats"=>{"downloads"=>1, "page_views"=>98, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_This_figure_represents_the_general_structure_of_the_data_processing_and_map_production_procedure_used_to_compare_the_methodology_outlined_in_this_paper_to_the_AfriPop_AsiaPop_GRUMP_and_GPW_methodologies_/1310708", "title"=>"This figure represents the general structure of the data processing and map production procedure used to compare the methodology outlined in this paper to the AfriPop/AsiaPop, GRUMP, and GPW methodologies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908590"], "description"=>"<p>We present prediction errors (observed minus predicted) as percentage values of the observed Level 5 census counts. The RF approach results in far fewer census units with extreme over predictions (negative percent residuals in yellows and oranges) and under predictions (positive percent residuals in dark blues).</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310717, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g006", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_For_Kenya_we_compared_the_summed_predictions_of_population_maps_estimated_using_coarse_census_data_Level_4_8220_Division_8221_level_to_population_counts_at_Level_5_finer_scale_units_/1310717", "title"=>"For Kenya we compared the summed predictions of population maps estimated using coarse census data (Level 4, “Division” level) to population counts at Level 5 (finer scale) units.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908589"], "description"=>"<p>The difference illustrates the finer gradations of RF model predictions for the density weighting layer when there are larger ranges of observed population densities present in training data (N = 505 for Level 4, N = 6622 for Level 5).</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310716, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g005", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_visual_comparison_of_Kenyan_population_maps_for_census_data_in_1999_produced_at_coarser_administrative_unit_Level_4_and_finer_scale_administrative_unit_Level_5_/1310716", "title"=>"A visual comparison of Kenyan population maps for census data in 1999 produced at coarser administrative unit (Level 4) and finer-scale administrative unit (Level 5).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908586"], "description"=>"<p>Dotted lines in each figure represent the 1:1 line and prediction error is stated for each country by the RMSE, %RMSE and MAE values.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310713, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_census_counts_plotted_from_the_finer_census_administrative_units_versus_the_summed_grid_cell_values_from_the_population_map_estimated_using_coarser_administrative_units_for_a_Cambodia_b_Vietnam_and_c_Kenya_/1310713", "title"=>"Observed census counts plotted from the finer census administrative units versus the summed grid cell values from the population map estimated using coarser administrative units for a) Cambodia, b) Vietnam, and c) Kenya.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908585"], "description"=>"<p>The model including variables here was used to produce the density weighting layer for the dasymetrically distributed population map in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107042#pone.0107042.g002\" target=\"_blank\">Fig. 2</a>. Variable names are defined and described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107042#pone.0107042.t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310712, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g003", "stats"=>{"downloads"=>1, "page_views"=>60, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variable_importance_for_Random_Forest_regression_presented_as_the_mean_decrease_in_residual_sum_of_squares_when_the_variable_is_included_in_a_tree_split_/1310712", "title"=>"Variable importance for Random Forest regression, presented as the mean decrease in residual sum of squares when the variable is included in a tree split.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908583"], "description"=>"<p>Both census counts and the Random Forest predicted weighting layer for dasymetric redistribution were based on the finest level administrative units (Level 4) where a complete, country-wide coverage was available.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310710, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g002", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_final_redistributed_population_map_for_the_Kenyan_1999_census_data_/1310710", "title"=>"The final redistributed population map for the Kenyan 1999 census data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908594"], "description"=>"<p>Two different error assessment methods are presented: root mean square error (RMSE), also expressed as a percentage of the mean population size of the administrative level (% RMSE); and the mean absolute error (MAE).</p><p>Accuracy assessment results for the RF, Afri/AsiaPop, GRUMP and GPW modeling methods for Cambodia, Vietnam and Kenya.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310721, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.t002", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_assessment_results_for_the_RF_Afri_AsiaPop_GRUMP_and_GPW_modeling_methods_for_Cambodia_Vietnam_and_Kenya_/1310721", "title"=>"Accuracy assessment results for the RF, Afri/AsiaPop, GRUMP and GPW modeling methods for Cambodia, Vietnam and Kenya.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-02-17 02:51:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1908592"], "description"=>"<p>Though this region northwest of Nairobi, Kenya is not a highly populated region this figure shows the results of the more detailed RF weighting layer versus the use of just urban areas (GRUMP) and land cover plus urban areas (AfriPop). The distinct edges in estimated people per pixel between census units are almost eliminated by the RF approach and it achieves greater consistency in predicted population density after census count redistribution.</p>", "links"=>[], "tags"=>["population data sets", "Ancillary Data High resolution", "case study", "population distributions", "Latin America", "census counts", "population density", "Disaggregating Census Data", "Geospatial Data", "Random Forest model", "disaggregate census data", "prediction layer", "Random Forests", "population densities", "dasymetric weights", "policy development", "Population Mapping", "weighting surface", "Many methods", "dasymetric redistribution", "Population growth", "population data production methodologies", "country level"], "article_id"=>1310719, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Forrest R. Stevens", "Andrea E. Gaughan", "Catherine Linard", "Andrew J. Tatem"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0107042.g007", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visual_comparisons_of_GRUMP_AfriPop_and_the_RF_based_population_map_from_this_study_/1310719", "title"=>"Visual comparisons of GRUMP, AfriPop and the RF-based population map from this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-17 02:51:37"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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