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Modelled Population Backseries

Greater London Authority

@ukgov.modelled_population_backseries_1

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About this Dataset

Modelled Population Backseries

These modelled annual population estimates were created for use the GLA's population projections. They are intended to provide a consistent series of annual population and components of change between census years with all change accounted for by the standard components of change (births, deaths, and migration). The official mid-year population estimates published by ONS are available here. The original detailed internal migration data published by ONS is available here. An overview of the general approach used to create these estimates is described in this presentation delivered at the 2022 BSPS conference. \* 17 April 2023 code for producing the modelled 2021 detailed internal migration flows is now available on Github
Publisher name: Greater London Authority
Last updated: 2025-01-27T04:01:25Z

Tables

Gla Modelled Estimates Series LAD EW 2001–2022 (2021 Geog) Wide

@ukgov.modelled_population_backseries_1.gla_modelled_estimates_series_lad_ew_2001_2022_2021_geog_wide
  • 12.38 MB
  • 60424 rows
  • 195 columns
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CREATE TABLE gla_modelled_estimates_series_lad_ew_2001_2022_2021_geog_wide (
  "gss_code" VARCHAR,
  "gss_name" VARCHAR,
  "geography" VARCHAR,
  "sex" VARCHAR,
  "age" BIGINT,
  "births_2002" VARCHAR,
  "births_2003" VARCHAR,
  "births_2004" VARCHAR,
  "births_2005" VARCHAR,
  "births_2006" VARCHAR,
  "births_2007" VARCHAR,
  "births_2008" VARCHAR,
  "births_2009" VARCHAR,
  "births_2010" VARCHAR,
  "births_2011" VARCHAR,
  "births_2012" VARCHAR,
  "births_2013" VARCHAR,
  "births_2014" VARCHAR,
  "births_2015" VARCHAR,
  "births_2016" VARCHAR,
  "births_2017" VARCHAR,
  "births_2018" VARCHAR,
  "births_2019" VARCHAR,
  "births_2020" VARCHAR,
  "births_2021" VARCHAR,
  "births_2022" VARCHAR,
  "deaths_2002" VARCHAR,
  "deaths_2003" VARCHAR,
  "deaths_2004" VARCHAR,
  "deaths_2005" VARCHAR,
  "deaths_2006" VARCHAR,
  "deaths_2007" VARCHAR,
  "deaths_2008" VARCHAR,
  "deaths_2009" VARCHAR,
  "deaths_2010" VARCHAR,
  "deaths_2011" VARCHAR,
  "deaths_2012" VARCHAR,
  "deaths_2013" VARCHAR,
  "deaths_2014" VARCHAR,
  "deaths_2015" VARCHAR,
  "deaths_2016" VARCHAR,
  "deaths_2017" VARCHAR,
  "deaths_2018" VARCHAR,
  "deaths_2019" VARCHAR,
  "deaths_2020" VARCHAR,
  "deaths_2021" VARCHAR,
  "deaths_2022" VARCHAR,
  "internal_in_2002" VARCHAR,
  "internal_in_2003" VARCHAR,
  "internal_in_2004" VARCHAR,
  "internal_in_2005" VARCHAR,
  "internal_in_2006" VARCHAR,
  "internal_in_2007" VARCHAR,
  "internal_in_2008" VARCHAR,
  "internal_in_2009" VARCHAR,
  "internal_in_2010" VARCHAR,
  "internal_in_2011" VARCHAR,
  "internal_in_2012" VARCHAR,
  "internal_in_2013" VARCHAR,
  "internal_in_2014" VARCHAR,
  "internal_in_2015" VARCHAR,
  "internal_in_2016" VARCHAR,
  "internal_in_2017" VARCHAR,
  "internal_in_2018" VARCHAR,
  "internal_in_2019" VARCHAR,
  "internal_in_2020" VARCHAR,
  "internal_in_2021" VARCHAR,
  "internal_in_2022" VARCHAR,
  "internal_net_2002" VARCHAR,
  "internal_net_2003" VARCHAR,
  "internal_net_2004" VARCHAR,
  "internal_net_2005" VARCHAR,
  "internal_net_2006" VARCHAR,
  "internal_net_2007" VARCHAR,
  "internal_net_2008" VARCHAR,
  "internal_net_2009" VARCHAR,
  "internal_net_2010" VARCHAR,
  "internal_net_2011" VARCHAR,
  "internal_net_2012" VARCHAR,
  "internal_net_2013" VARCHAR,
  "internal_net_2014" VARCHAR,
  "internal_net_2015" VARCHAR,
  "internal_net_2016" VARCHAR,
  "internal_net_2017" VARCHAR,
  "internal_net_2018" VARCHAR,
  "internal_net_2019" VARCHAR,
  "internal_net_2020" VARCHAR,
  "internal_net_2021" VARCHAR,
  "internal_net_2022" VARCHAR,
  "internal_out_2002" VARCHAR,
  "internal_out_2003" VARCHAR,
  "internal_out_2004" VARCHAR,
  "internal_out_2005" VARCHAR,
  "internal_out_2006" VARCHAR,
  "internal_out_2007" VARCHAR,
  "internal_out_2008" VARCHAR,
  "internal_out_2009" VARCHAR,
  "internal_out_2010" VARCHAR,
  "internal_out_2011" VARCHAR,
  "internal_out_2012" VARCHAR
);

Gla Modelled Estimates Series LAD EW 2001–2023 (2023 Geog) Wide

@ukgov.modelled_population_backseries_1.gla_modelled_estimates_series_lad_ew_2001_2023_2023_geog_wide
  • 12.62 MB
  • 58058 rows
  • 204 columns
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CREATE TABLE gla_modelled_estimates_series_lad_ew_2001_2023_2023_geog_wide (
  "gss_code" VARCHAR,
  "gss_name" VARCHAR,
  "geography" VARCHAR,
  "sex" VARCHAR,
  "age" BIGINT,
  "births_2002" VARCHAR,
  "births_2003" VARCHAR,
  "births_2004" VARCHAR,
  "births_2005" VARCHAR,
  "births_2006" VARCHAR,
  "births_2007" VARCHAR,
  "births_2008" VARCHAR,
  "births_2009" VARCHAR,
  "births_2010" VARCHAR,
  "births_2011" VARCHAR,
  "births_2012" VARCHAR,
  "births_2013" VARCHAR,
  "births_2014" VARCHAR,
  "births_2015" VARCHAR,
  "births_2016" VARCHAR,
  "births_2017" VARCHAR,
  "births_2018" VARCHAR,
  "births_2019" VARCHAR,
  "births_2020" VARCHAR,
  "births_2021" VARCHAR,
  "births_2022" VARCHAR,
  "births_2023" VARCHAR,
  "deaths_2002" VARCHAR,
  "deaths_2003" VARCHAR,
  "deaths_2004" VARCHAR,
  "deaths_2005" VARCHAR,
  "deaths_2006" VARCHAR,
  "deaths_2007" VARCHAR,
  "deaths_2008" VARCHAR,
  "deaths_2009" VARCHAR,
  "deaths_2010" VARCHAR,
  "deaths_2011" VARCHAR,
  "deaths_2012" VARCHAR,
  "deaths_2013" VARCHAR,
  "deaths_2014" VARCHAR,
  "deaths_2015" VARCHAR,
  "deaths_2016" VARCHAR,
  "deaths_2017" VARCHAR,
  "deaths_2018" VARCHAR,
  "deaths_2019" VARCHAR,
  "deaths_2020" VARCHAR,
  "deaths_2021" VARCHAR,
  "deaths_2022" VARCHAR,
  "deaths_2023" VARCHAR,
  "internal_in_2002" VARCHAR,
  "internal_in_2003" VARCHAR,
  "internal_in_2004" VARCHAR,
  "internal_in_2005" VARCHAR,
  "internal_in_2006" VARCHAR,
  "internal_in_2007" VARCHAR,
  "internal_in_2008" VARCHAR,
  "internal_in_2009" VARCHAR,
  "internal_in_2010" VARCHAR,
  "internal_in_2011" VARCHAR,
  "internal_in_2012" VARCHAR,
  "internal_in_2013" VARCHAR,
  "internal_in_2014" VARCHAR,
  "internal_in_2015" VARCHAR,
  "internal_in_2016" VARCHAR,
  "internal_in_2017" VARCHAR,
  "internal_in_2018" VARCHAR,
  "internal_in_2019" VARCHAR,
  "internal_in_2020" VARCHAR,
  "internal_in_2021" VARCHAR,
  "internal_in_2022" VARCHAR,
  "internal_in_2023" VARCHAR,
  "internal_net_2002" VARCHAR,
  "internal_net_2003" VARCHAR,
  "internal_net_2004" VARCHAR,
  "internal_net_2005" VARCHAR,
  "internal_net_2006" VARCHAR,
  "internal_net_2007" VARCHAR,
  "internal_net_2008" VARCHAR,
  "internal_net_2009" VARCHAR,
  "internal_net_2010" VARCHAR,
  "internal_net_2011" VARCHAR,
  "internal_net_2012" VARCHAR,
  "internal_net_2013" VARCHAR,
  "internal_net_2014" VARCHAR,
  "internal_net_2015" VARCHAR,
  "internal_net_2016" VARCHAR,
  "internal_net_2017" VARCHAR,
  "internal_net_2018" VARCHAR,
  "internal_net_2019" VARCHAR,
  "internal_net_2020" VARCHAR,
  "internal_net_2021" VARCHAR,
  "internal_net_2022" VARCHAR,
  "internal_net_2023" VARCHAR,
  "internal_out_2002" VARCHAR,
  "internal_out_2003" VARCHAR,
  "internal_out_2004" VARCHAR,
  "internal_out_2005" VARCHAR,
  "internal_out_2006" VARCHAR,
  "internal_out_2007" VARCHAR,
  "internal_out_2008" VARCHAR
);

Gla Modelled Estimates Series WD22 London 2011–2022 Wide

@ukgov.modelled_population_backseries_1.gla_modelled_estimates_series_wd22_london_2011_2022_wide
  • 40.04 MB
  • 123760 rows
  • 73 columns
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CREATE TABLE gla_modelled_estimates_series_wd22_london_2011_2022_wide (
  "gss_code" VARCHAR,
  "gss_name" VARCHAR,
  "wd22cd" VARCHAR,
  "wd22nm" VARCHAR,
  "sex" VARCHAR,
  "age" BIGINT,
  "births_2012" DOUBLE,
  "births_2013" DOUBLE,
  "births_2014" DOUBLE,
  "births_2015" DOUBLE,
  "births_2016" DOUBLE,
  "births_2017" DOUBLE,
  "births_2018" DOUBLE,
  "births_2019" DOUBLE,
  "births_2020" DOUBLE,
  "births_2021" DOUBLE,
  "births_2022" BIGINT,
  "deaths_2012" DOUBLE,
  "deaths_2013" DOUBLE,
  "deaths_2014" DOUBLE,
  "deaths_2015" DOUBLE,
  "deaths_2016" DOUBLE,
  "deaths_2017" DOUBLE,
  "deaths_2018" DOUBLE,
  "deaths_2019" DOUBLE,
  "deaths_2020" DOUBLE,
  "deaths_2021" DOUBLE,
  "deaths_2022" DOUBLE,
  "inflow_2012" DOUBLE,
  "inflow_2013" DOUBLE,
  "inflow_2014" DOUBLE,
  "inflow_2015" DOUBLE,
  "inflow_2016" DOUBLE,
  "inflow_2017" DOUBLE,
  "inflow_2018" DOUBLE,
  "inflow_2019" DOUBLE,
  "inflow_2020" DOUBLE,
  "inflow_2021" DOUBLE,
  "inflow_2022" DOUBLE,
  "netflow_2012" DOUBLE,
  "netflow_2013" DOUBLE,
  "netflow_2014" DOUBLE,
  "netflow_2015" DOUBLE,
  "netflow_2016" DOUBLE,
  "netflow_2017" DOUBLE,
  "netflow_2018" DOUBLE,
  "netflow_2019" DOUBLE,
  "netflow_2020" DOUBLE,
  "netflow_2021" DOUBLE,
  "netflow_2022" DOUBLE,
  "outflow_2012" DOUBLE,
  "outflow_2013" DOUBLE,
  "outflow_2014" DOUBLE,
  "outflow_2015" DOUBLE,
  "outflow_2016" DOUBLE,
  "outflow_2017" DOUBLE,
  "outflow_2018" DOUBLE,
  "outflow_2019" DOUBLE,
  "outflow_2020" DOUBLE,
  "outflow_2021" DOUBLE,
  "outflow_2022" DOUBLE,
  "population_2011" DOUBLE,
  "population_2012" DOUBLE,
  "population_2013" DOUBLE,
  "population_2014" DOUBLE,
  "population_2015" DOUBLE,
  "population_2016" DOUBLE,
  "population_2017" DOUBLE,
  "population_2018" DOUBLE,
  "population_2019" DOUBLE,
  "population_2020" DOUBLE,
  "population_2021" DOUBLE,
  "population_2022" BIGINT
);

Modelled Backseries

@ukgov.modelled_population_backseries_1.modelled_backseries
  • 10.16 MB
  • 93093 rows
  • 96 columns
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CREATE TABLE modelled_backseries (
  "gss_code" VARCHAR,
  "gss_name" VARCHAR,
  "geography" VARCHAR,
  "sex" VARCHAR,
  "age" BIGINT,
  "births_2012" BIGINT,
  "births_2013" BIGINT,
  "births_2014" BIGINT,
  "births_2015" BIGINT,
  "births_2016" BIGINT,
  "births_2017" BIGINT,
  "births_2018" BIGINT,
  "births_2019" BIGINT,
  "births_2020" BIGINT,
  "births_2021" BIGINT,
  "deaths_2012" BIGINT,
  "deaths_2013" BIGINT,
  "deaths_2014" BIGINT,
  "deaths_2015" BIGINT,
  "deaths_2016" BIGINT,
  "deaths_2017" BIGINT,
  "deaths_2018" BIGINT,
  "deaths_2019" BIGINT,
  "deaths_2020" BIGINT,
  "deaths_2021" BIGINT,
  "internal_in_2012" DOUBLE,
  "internal_in_2013" DOUBLE,
  "internal_in_2014" DOUBLE,
  "internal_in_2015" DOUBLE,
  "internal_in_2016" DOUBLE,
  "internal_in_2017" DOUBLE,
  "internal_in_2018" DOUBLE,
  "internal_in_2019" DOUBLE,
  "internal_in_2020" DOUBLE,
  "internal_in_2021" DOUBLE,
  "internal_net_2012" BIGINT,
  "internal_net_2013" BIGINT,
  "internal_net_2014" BIGINT,
  "internal_net_2015" BIGINT,
  "internal_net_2016" BIGINT,
  "internal_net_2017" BIGINT,
  "internal_net_2018" BIGINT,
  "internal_net_2019" BIGINT,
  "internal_net_2020" BIGINT,
  "internal_net_2021" BIGINT,
  "internal_out_2012" DOUBLE,
  "internal_out_2013" DOUBLE,
  "internal_out_2014" DOUBLE,
  "internal_out_2015" DOUBLE,
  "internal_out_2016" DOUBLE,
  "internal_out_2017" DOUBLE,
  "internal_out_2018" DOUBLE,
  "internal_out_2019" DOUBLE,
  "internal_out_2020" DOUBLE,
  "internal_out_2021" DOUBLE,
  "international_in_2012" BIGINT,
  "international_in_2013" BIGINT,
  "international_in_2014" BIGINT,
  "international_in_2015" BIGINT,
  "international_in_2016" BIGINT,
  "international_in_2017" BIGINT,
  "international_in_2018" BIGINT,
  "international_in_2019" BIGINT,
  "international_in_2020" BIGINT,
  "international_in_2021" BIGINT,
  "international_net_2012" BIGINT,
  "international_net_2013" BIGINT,
  "international_net_2014" BIGINT,
  "international_net_2015" BIGINT,
  "international_net_2016" BIGINT,
  "international_net_2017" BIGINT,
  "international_net_2018" BIGINT,
  "international_net_2019" BIGINT,
  "international_net_2020" BIGINT,
  "international_net_2021" BIGINT,
  "international_out_2012" BIGINT,
  "international_out_2013" BIGINT,
  "international_out_2014" BIGINT,
  "international_out_2015" BIGINT,
  "international_out_2016" BIGINT,
  "international_out_2017" BIGINT,
  "international_out_2018" BIGINT,
  "international_out_2019" BIGINT,
  "international_out_2020" BIGINT,
  "international_out_2021" BIGINT,
  "population_2011" BIGINT,
  "population_2012" BIGINT,
  "population_2013" BIGINT,
  "population_2014" BIGINT,
  "population_2015" BIGINT,
  "population_2016" BIGINT,
  "population_2017" BIGINT,
  "population_2018" BIGINT,
  "population_2019" BIGINT,
  "population_2020" BIGINT,
  "population_2021" BIGINT
);

Modelled Backseries (updated 14-07-2023)

@ukgov.modelled_population_backseries_1.modelled_backseries_updated_14_07_2023
  • 24.96 MB
  • 93093 rows
  • 96 columns
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CREATE TABLE modelled_backseries_updated_14_07_2023 (
  "gss_code" VARCHAR,
  "gss_name" VARCHAR,
  "geography" VARCHAR,
  "sex" VARCHAR,
  "age" BIGINT,
  "births_2012" BIGINT,
  "births_2013" BIGINT,
  "births_2014" BIGINT,
  "births_2015" BIGINT,
  "births_2016" BIGINT,
  "births_2017" BIGINT,
  "births_2018" BIGINT,
  "births_2019" BIGINT,
  "births_2020" BIGINT,
  "births_2021" BIGINT,
  "deaths_2012" BIGINT,
  "deaths_2013" BIGINT,
  "deaths_2014" BIGINT,
  "deaths_2015" BIGINT,
  "deaths_2016" BIGINT,
  "deaths_2017" BIGINT,
  "deaths_2018" BIGINT,
  "deaths_2019" BIGINT,
  "deaths_2020" BIGINT,
  "deaths_2021" BIGINT,
  "internal_in_2012" DOUBLE,
  "internal_in_2013" DOUBLE,
  "internal_in_2014" DOUBLE,
  "internal_in_2015" DOUBLE,
  "internal_in_2016" DOUBLE,
  "internal_in_2017" DOUBLE,
  "internal_in_2018" DOUBLE,
  "internal_in_2019" DOUBLE,
  "internal_in_2020" DOUBLE,
  "internal_in_2021" DOUBLE,
  "internal_net_2012" BIGINT,
  "internal_net_2013" BIGINT,
  "internal_net_2014" BIGINT,
  "internal_net_2015" BIGINT,
  "internal_net_2016" BIGINT,
  "internal_net_2017" BIGINT,
  "internal_net_2018" BIGINT,
  "internal_net_2019" BIGINT,
  "internal_net_2020" BIGINT,
  "internal_net_2021" BIGINT,
  "internal_out_2012" DOUBLE,
  "internal_out_2013" DOUBLE,
  "internal_out_2014" DOUBLE,
  "internal_out_2015" DOUBLE,
  "internal_out_2016" DOUBLE,
  "internal_out_2017" DOUBLE,
  "internal_out_2018" DOUBLE,
  "internal_out_2019" DOUBLE,
  "internal_out_2020" DOUBLE,
  "internal_out_2021" DOUBLE,
  "international_in_2012" DOUBLE,
  "international_in_2013" DOUBLE,
  "international_in_2014" DOUBLE,
  "international_in_2015" DOUBLE,
  "international_in_2016" DOUBLE,
  "international_in_2017" DOUBLE,
  "international_in_2018" DOUBLE,
  "international_in_2019" DOUBLE,
  "international_in_2020" DOUBLE,
  "international_in_2021" DOUBLE,
  "international_net_2012" DOUBLE,
  "international_net_2013" DOUBLE,
  "international_net_2014" DOUBLE,
  "international_net_2015" DOUBLE,
  "international_net_2016" DOUBLE,
  "international_net_2017" DOUBLE,
  "international_net_2018" DOUBLE,
  "international_net_2019" DOUBLE,
  "international_net_2020" DOUBLE,
  "international_net_2021" DOUBLE,
  "international_out_2012" DOUBLE,
  "international_out_2013" DOUBLE,
  "international_out_2014" DOUBLE,
  "international_out_2015" DOUBLE,
  "international_out_2016" DOUBLE,
  "international_out_2017" DOUBLE,
  "international_out_2018" DOUBLE,
  "international_out_2019" DOUBLE,
  "international_out_2020" DOUBLE,
  "international_out_2021" DOUBLE,
  "population_2011" BIGINT,
  "population_2012" BIGINT,
  "population_2013" BIGINT,
  "population_2014" BIGINT,
  "population_2015" BIGINT,
  "population_2016" BIGINT,
  "population_2017" BIGINT,
  "population_2018" BIGINT,
  "population_2019" BIGINT,
  "population_2020" BIGINT,
  "population_2021" BIGINT
);

Origin Destination Lad 2012–2021

@ukgov.modelled_population_backseries_1.origin_destination_lad_2012_2021
  • 91.43 MB
  • 1764505 rows
  • 95 columns
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CREATE TABLE origin_destination_lad_2012_2021 (
  "gss_out" VARCHAR,
  "gss_in" VARCHAR,
  "year" BIGINT,
  "sex" VARCHAR,
  "age_0" VARCHAR,
  "age_1" VARCHAR,
  "age_3" VARCHAR,
  "age_4" VARCHAR,
  "age_5" VARCHAR,
  "age_8" VARCHAR,
  "age_10" VARCHAR,
  "age_12" VARCHAR,
  "age_17" VARCHAR,
  "age_18" VARCHAR,
  "age_19" VARCHAR,
  "age_20" VARCHAR,
  "age_21" VARCHAR,
  "age_22" VARCHAR,
  "age_25" VARCHAR,
  "age_26" VARCHAR,
  "age_27" VARCHAR,
  "age_28" VARCHAR,
  "age_29" VARCHAR,
  "age_30" VARCHAR,
  "age_32" VARCHAR,
  "age_33" VARCHAR,
  "age_34" VARCHAR,
  "age_39" VARCHAR,
  "age_40" VARCHAR,
  "age_41" VARCHAR,
  "age_42" VARCHAR,
  "age_43" VARCHAR,
  "age_44" VARCHAR,
  "age_49" VARCHAR,
  "age_55" VARCHAR,
  "age_57" VARCHAR,
  "age_58" VARCHAR,
  "age_2" VARCHAR,
  "age_7" VARCHAR,
  "age_9" VARCHAR,
  "age_11" VARCHAR,
  "age_13" VARCHAR,
  "age_23" VARCHAR,
  "age_24" VARCHAR,
  "age_31" VARCHAR,
  "age_36" VARCHAR,
  "age_45" VARCHAR,
  "age_46" VARCHAR,
  "age_47" VARCHAR,
  "age_52" VARCHAR,
  "age_61" VARCHAR,
  "age_15" VARCHAR,
  "age_50" VARCHAR,
  "age_56" VARCHAR,
  "age_60" VARCHAR,
  "age_83" VARCHAR,
  "age_37" VARCHAR,
  "age_54" VARCHAR,
  "age_73" VARCHAR,
  "age_35" VARCHAR,
  "age_51" VARCHAR,
  "age_53" VARCHAR,
  "age_63" VARCHAR,
  "age_64" VARCHAR,
  "age_6" VARCHAR,
  "age_16" VARCHAR,
  "age_48" VARCHAR,
  "age_66" VARCHAR,
  "age_38" VARCHAR,
  "age_88" VARCHAR,
  "age_62" VARCHAR,
  "age_70" VARCHAR,
  "age_65" VARCHAR,
  "age_67" VARCHAR,
  "age_86" VARCHAR,
  "age_59" VARCHAR,
  "age_14" VARCHAR,
  "age_77" VARCHAR,
  "age_78" VARCHAR,
  "age_90" VARCHAR,
  "age_71" VARCHAR,
  "age_82" VARCHAR,
  "age_68" VARCHAR,
  "age_79" VARCHAR,
  "age_69" VARCHAR,
  "age_84" VARCHAR,
  "age_76" VARCHAR,
  "age_80" VARCHAR,
  "age_85" VARCHAR,
  "age_87" VARCHAR,
  "age_81" VARCHAR,
  "age_74" VARCHAR,
  "age_75" VARCHAR,
  "age_72" VARCHAR,
  "age_89" VARCHAR
);

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