Skip to contents

Burkina Faso Enumeration Areas

Source: R/datasets.R
bfa_eas.Rd

A derived enumeration area (EA) frame for household budget and living-standards surveys. Each row corresponds to one WorldPop/GRID3 preEA polygon, and ea_id preserves the source identifier for spatial joins. The frame covers 13 regions, 45 provinces, and 348 communes of Burkina Faso.

Usage

bfa_eas

Format

A tibble with 44,570 rows and 13 columns:

ea_id

Integer. Unique preEA identifier from the WorldPop source

region

Factor. Region name (13 regions)

province

Factor. Province name within region (45 provinces)

commune

Factor. Commune name within province (348 communes)

urban_rural

Factor. Modeled urban/rural classification based on commune population density

population

Integer. WorldPop/GRID3 preEA population estimate, informed by the 2019 population census

households

Integer. Modeled household count, derived from population and aggregated EHCVM 2021-2022 household-size parameters

area_km2

Numeric. EA area in square kilometers

pop_density

Numeric. Population per square kilometer, derived from population and source polygon area

longitude

Numeric. Longitude of the preEA point-on-surface in WGS 84

latitude

Numeric. Latitude of the preEA point-on-surface in WGS 84

remoteness

Factor. Modeled operational class (Low, Medium, High) for sampling examples; not an official geographic classification

fieldwork_cost

Integer. Synthetic relative fieldwork cost index; not a monetary estimate

Source

  • Qader et al. (2022), National automatic pre-Enumeration Areas (preEAs) in Burkina Faso (2019), version 1.0, WorldPop, University of Southampton, doi:10.5258/SOTON/WP00731 . Data licensed CC BY 4.0.

  • Institut National de la Statistique et de la Demographie, Enquete Harmonisee sur les Conditions de Vie des Menages 2021-2022, reference BFA_2021_EHCVM-2_v01_M, https://microdata.worldbank.org/catalog/6277. Used only to derive aggregated household-size parameters.

  • OCHA Common Operational Dataset for Burkina Faso administrative boundaries, https://data.humdata.org/dataset/cod-ab-bfa.

Details

This dataset is designed for demonstrating:

  • Stratified multi-stage cluster sampling

  • PPS (probability proportional to size) sampling using household counts

  • Urban/rural stratification

  • Neyman and optimal allocation using auxiliary variables

  • Balanced and spatially balanced selection using frame auxiliaries

  • Operational planning with modeled remoteness and relative costs

The data structure follows typical household survey sampling frames where enumeration areas serve as primary sampling units, selected with probability proportional to the number of households. Survey outcomes such as expenditure, poverty, and food security are deliberately not included in the frame: they are observed after selection. remoteness and fieldwork_cost are synthetic planning variables supplied only for examples.

See also

bfa_eas_variance for Neyman allocation, bfa_eas_cost for optimal allocation

Examples

# Explore the data
head(bfa_eas)
#> # A tibble: 6 × 13
#>   ea_id region       province commune urban_rural population households area_km2
#>   <int> <fct>        <fct>    <fct>   <fct>            <int>      <int>    <dbl>
#> 1 11759 Boucle du M… Bale     Bagassi Rural               56          7     9.21
#> 2 11760 Boucle du M… Bale     Bagassi Rural              204         25     8.75
#> 3 11761 Boucle du M… Bale     Bagassi Rural               63          8     8.54
#> 4 11762 Boucle du M… Bale     Bagassi Rural              257         31     8.92
#> 5 11763 Boucle du M… Bale     Bagassi Rural               48          6     4.89
#> 6 11764 Boucle du M… Bale     Bagassi Rural              139         17     8.51
#> # ℹ 5 more variables: pop_density <dbl>, longitude <dbl>, latitude <dbl>,
#> #   remoteness <fct>, fieldwork_cost <int>
table(bfa_eas$region)
#> 
#> Boucle du Mouhoun          Cascades            Centre        Centre-Est 
#>              5009              2508              3888              2941 
#>       Centre-Nord      Centre-Ouest        Centre-Sud               Est 
#>              3402              3723              1612              5505 
#>     Hauts-Bassins              Nord   Plateau-Central             Sahel 
#>              4839              2930              1662              4144 
#>         Sud-Ouest 
#>              2407 
table(bfa_eas$urban_rural)
#> 
#> Rural Urban 
#> 37687  6883 

# Stratified PPS sample
sampling_design() |>
  stratify_by(region, urban_rural) |>
  draw(n = 3, method = "pps_brewer", mos = households) |>
  execute(bfa_eas, seed = 3)
#> # A tbl_sample: 69 × 19
#> # Sampling:     1 stage | 69/44,570 units
#> # Weights:      678.94 [1.54, 3040.29]
#>    ea_id region      province commune urban_rural population households area_km2
#>  * <int> <fct>       <fct>    <fct>   <fct>            <int>      <int>    <dbl>
#>  1  6282 Boucle du … Banwa    Kouka   Rural              364         42     0.53
#>  2 11674 Boucle du … Nayala   Yaba    Rural             1353        178     1.3 
#>  3 12785 Boucle du … Kossi    Djibas… Rural              711        101    10.6 
#>  4  9044 Boucle du … Bale     Poura   Urban              751        142     0.77
#>  5  9046 Boucle du … Bale     Poura   Urban             4132        784     3.25
#>  6  9045 Boucle du … Bale     Poura   Urban              536        102     0.17
#>  7 20683 Cascades    Comoe    Banfora Rural              926        106     0.22
#>  8 14826 Cascades    Comoe    Niango… Rural              234         28     8.06
#>  9 10239 Cascades    Comoe    Sidéra… Rural             1532        245     1.22
#> 10 13956 Centre      Kadiogo  Koubri  Rural              407         76     7.92
#> # ℹ 59 more rows
#> # ℹ 11 more variables: pop_density <dbl>, longitude <dbl>, latitude <dbl>,
#> #   remoteness <fct>, fieldwork_cost <int>, .weight <dbl>, .sample_id <int>,
#> #   .stage <int>, .weight_1 <dbl>, .fpc_1 <int>, .certainty_1 <lgl>