PRIOGRID embeds rich metadata about every variable and data source.
This tutorial walks through the three core metadata objects —
pgvariables, pgsources, and
pgchecksum — and the search and discovery tools built on
top of them.
Variables: pgvariables
pgvariables is a data frame listing every variable
PRIOGRID can calculate:
pgvariables$name
#> [1] "cru_tmp" "cru_pre"
#> [3] "cru_pet" "cshapes_cover_share"
#> [5] "cshapes_gwcode" "geoepr_reg_excluded"
#> [7] "bdist1" "bdist2"
#> [9] "bdist3" "ghsl_population_grid"
#> [11] "hilda_cropland" "hilda_forest"
#> [13] "hilda_grassland" "hilda_ocean"
#> [15] "hilda_pasture" "hilda_sparse"
#> [17] "hilda_urban" "hilda_water"
#> [19] "linight_mean" "naturalearth_cover"
#> [21] "naturalearth_cover_share" "ruggedterrain_elevation_mean"
#> [23] "traveltime_mean" "traveltime_min"
#> [25] "geopko_troops_count" "geopko_operations_count"
#> [27] "ne_disputed_area_share" "speibase6_mean"
#> [29] "ghs_wup_degurba_urban" "ucdp_ged"
#> [31] "shdi" "msch"
#> [33] "esch" "lifexp"
#> [35] "gnic"| Column | Description |
|---|---|
name |
Variable name used in load_pgvariable(),
calc_pg(), etc. |
static |
TRUE = no temporal dimension; FALSE =
time-varying |
source_ids |
Comma-separated UUIDs of the data sources feeding this variable |
Static and time-varying variables:
pgvariables[pgvariables$static == TRUE, "name"] # terrain, borders, etc.
#> [1] "naturalearth_cover" "naturalearth_cover_share"
#> [3] "ruggedterrain_elevation_mean" "traveltime_mean"
#> [5] "traveltime_min" "ne_disputed_area_share"
pgvariables[pgvariables$static == FALSE, "name"] # climate, conflict, etc.
#> [1] "cru_tmp" "cru_pre"
#> [3] "cru_pet" "cshapes_cover_share"
#> [5] "cshapes_gwcode" "geoepr_reg_excluded"
#> [7] "bdist1" "bdist2"
#> [9] "bdist3" "ghsl_population_grid"
#> [11] "hilda_cropland" "hilda_forest"
#> [13] "hilda_grassland" "hilda_ocean"
#> [15] "hilda_pasture" "hilda_sparse"
#> [17] "hilda_urban" "hilda_water"
#> [19] "linight_mean" "geopko_troops_count"
#> [21] "geopko_operations_count" "speibase6_mean"
#> [23] "ghs_wup_degurba_urban" "ucdp_ged"
#> [25] "shdi" "msch"
#> [27] "esch" "lifexp"
#> [29] "gnic"Data Sources: pgsources
pgsources describes every raw data source that PRIOGRID
draws on:
str(pgsources)
#> Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 50 obs. of 18 variables:
#> $ id : chr "04254b82-85f0-4c06-9f1b-86ed35e05403" "0a746ab8-cc8e-4b31-bb71-8479a9ac8fa3" "1604221b-e558-4e65-b7fe-d6b0a517ff5c" "190c7abc-b779-4462-97ff-00994cbd2431" ...
#> $ source_name : chr "EOG Annual VIIRS Nighttime Lights" "SEDAC Food Insecurity Hotspots" "Global Area Equipped for Irrigation Dataset 1900-2015" "GlobalDataLab Area Database" ...
#> $ source_version : chr "V2.2" "v1" "v4" "v.4.2" ...
#> $ license : chr "CC BY 4.0" "CC BY 4.0" "CC BY 4.0" "https://globaldatalab.org/termsofuse/" ...
#> $ citation_keys : chr "elvidgeAnnualTimeSeries2021" "centerforinternationalearthscienceinformationnetworkciesincolumbiauniversityFoodInsecurityHotspots2020" "mehtaHalfTwentyfirstCentury2024" "smitsGDLAreaDatabase2016; globaldatalabAreaDatabase2015" ...
#> $ aws_bucket : chr NA NA NA NA ...
#> $ aws_region : chr NA NA NA NA ...
#> $ download_url : chr NA "https://sedac.ciesin.columbia.edu/downloads/data/food/food-food-insecurity-hotspots/food-food-insecurity-hotspo"| __truncated__ NA NA ...
#> $ website_url : chr "https://eogdata.mines.edu/products/vnl/#annual_v2" "https://sedac.ciesin.columbia.edu/data/set/food-food-insecurity-hotspots" "https://zenodo.org/records/14219723" "https://globaldatalab.org/areadata/" ...
#> $ tags : chr "demographic, remote - sense, socio - economic" "demographic, food insecurity" "irrigation, land use" "infrastructure, sanitation" ...
#> $ spatial_extent : chr "World" "Several countries (spread)" "World" "World" ...
#> $ temporal_resolution: chr "Yearly" "Quarterly" "Less than yearly" "Yearly" ...
#> $ reference_keys : chr "elvidgeVIIRSNighttimeLights2017" "omayProjectedFutureChanges2024" NA NA ...
#> $ prio_mirror : chr NA NA NA NA ...
#> $ download_url_exists: logi NA FALSE NA NA FALSE FALSE ...
#> $ website_url_exists : logi NA TRUE NA NA TRUE TRUE ...
#> $ prio_mirror_exists : logi NA NA NA NA NA FALSE ...
#> $ created_at : POSIXct, format: "2024-12-04 08:35:28" "2024-12-03 14:00:14" ...
#> - attr(*, "spec")=List of 3
#> ..$ cols :List of 18
#> .. ..$ id : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ source_name : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ source_version : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ license : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ citation_keys : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ aws_bucket : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ aws_region : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ download_url : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ website_url : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ tags : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ spatial_extent : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ temporal_resolution: list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ reference_keys : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ prio_mirror : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ download_url_exists: list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_logical" "collector"
#> .. ..$ website_url_exists : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_logical" "collector"
#> .. ..$ prio_mirror_exists : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_logical" "collector"
#> .. ..$ created_at :List of 1
#> .. .. ..$ format: chr ""
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_datetime" "collector"
#> ..$ default: list()
#> .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
#> ..$ delim : chr "\t"
#> ..- attr(*, "class")= chr "col_spec"
#> - attr(*, "problems")=<pointer: (nil)>Key columns:
| Column | Description |
|---|---|
id |
UUID — links to pgvariables$source_ids
|
source_name |
Human-readable name (e.g., "CRU TS",
"UCDP GED") |
source_version |
Data version used |
license |
Data license (e.g., "CC BY 4.0") |
citation_keys |
Semicolon-separated BibTeX keys →
inst/REFERENCES.bib
|
tags |
Comma-separated tags (e.g., "climate",
"conflict") |
spatial_extent |
"World", "Multiple continents", etc. |
temporal_resolution |
"Yearly", "Monthly",
"Static", etc. |
download_url |
Primary download URL |
website_url |
Landing page URL |
prio_mirror |
PRIO-hosted mirror URL |
Browse sources by tag or license:
# All sources with CC-BY license
pgsources[grepl("CC BY", pgsources$license), c("source_name", "license")]
#> source_name license
#> 1 EOG Annual VIIRS Nighttime Lights CC BY 4.0
#> 2 SEDAC Food Insecurity Hotspots CC BY 4.0
#> 3 Global Area Equipped for Irrigation Dataset 1900-2015 CC BY 4.0
#> 6 Li Nighttime CC BY 4.0
#> 7 World Bank Global Subnational Poverty Atlas (GSAP) CC BY 4.0
#> 9 UCDP GED CC BY 4.0
#> 13 MCC-PIK DOSE – Subnational Economic Output CC BY 4.0
#> 16 WorldPop Migration Flows CC BY 4.0
#> 17 GHSL GHS-BUILT-C CC BY 4.0
#> 19 GHSL GHS-BUILT-H CC BY 4.0
#> 21 HILDA+ CC BY 4.0
#> 22 GISCO Geostat Census Grid CC BY 4.0
#> 23 GlobalDataLab Subnational Human Development (SHDI) CC BY-NC
#> 24 Global Multi-resolution Terrain Elevation Data CC BY 4.0
#> 27 Estimated Travel Time CC BY 4.0
#> 28 GHSL GHS-BUILT-V CC BY 4.0
#> 29 Global Irrigated Areas CC BY-NC 3.0
#> 30 ORNL Landscan Global CC BY 4.0
#> 32 GHSL GHS Population Grid CC BY 4.0
#> 34 UCDP Violent Political Protest Dataset (VPP) CC BY 4.0
#> 36 ReliefWeb Disasters List CC BY 4.0
#> 37 World Bank Subnational Poverty and Inequality Database CC BY 4.0
#> 38 GHSL GHS-BUILT-S CC BY 4.0
#> 39 ESA WorldCover 10m CC BY 4.0
#> 40 World Bank Subnational Doing Business Reports CC BY 4.0
#> 41 GHSL GHS-DUC CC BY 4.0
#> 42 ETH ICR cShapes CC BY-NC-SA 4.0
#> 43 GHSL GHS Settlement Model Grid CC BY 4.0
#> 44 geoBoundaries CC BY 4.0
#> 49 GHS-WUP-DEGURBA CC BY 4.0
#> 50 UCDP GED CC BY 4.0
# Yearly time-varying sources
pgsources[pgsources$temporal_resolution == "Yearly", c("source_name", "source_version")]
#> source_name source_version
#> 1 EOG Annual VIIRS Nighttime Lights V2.2
#> 4 GlobalDataLab Area Database v.4.2
#> 6 Li Nighttime v8
#> 7 World Bank Global Subnational Poverty Atlas (GSAP) Oct. 2024
#> 8 ETH ICR EPR Core 2023
#> 11 ETH ICR GeoEPR 2023
#> 12 WIDE Education Inequalities 9.23
#> 13 MCC-PIK DOSE – Subnational Economic Output v2.9
#> 15 World Bank Geocoded Research Release 1.4.2
#> 21 HILDA+ v1.0
#> 22 GISCO Geostat Census Grid 2021
#> 23 GlobalDataLab Subnational Human Development (SHDI) v.7.0
#> 29 Global Irrigated Areas 2018
#> 30 ORNL Landscan Global 2023
#> 31 SEDAC Global Gridded Relative Deprivation Index (GRDI) v1
#> 33 GlobalDataLab International Wealth Index (IWI) v.4.2
#> 34 UCDP Violent Political Protest Dataset (VPP) 20.1
#> 37 World Bank Subnational Poverty and Inequality Database Oct 2024
#> 40 World Bank Subnational Doing Business Reports 2022File Integrity: pgchecksum
pgchecksum stores MD5 checksums for downloaded files,
allowing you to verify that your local copies match those used to build
the official release:
pgchecksum
#> source_name source_version
#> 1 ETH ICR cShapes 2.0
#> 2 IHME GHDx Under-5 mortality 2019
#> 3 World Bank Geocoded Research Release 1.4.2
#> 4 WorldPop Migration Flows 2019
#> 5 GISCO Geostat Census Grid 2021
#> 6 SEDAC Food Insecurity Hotspots v1
#> 7 WIDE Education Inequalities 9.23
#> 8 SEDAC Global Gridded Relative Deprivation Index (GRDI) v1
#> 9 Geocoded Disasters (GDIS) Dataset v1
#> 10 SEDAC Global Subnational Infant Mortality Rates v2.01
#> 11 ReliefWeb Disasters List 2024
#> 12 ETH ICR GeoEPR 2021
#> 13 UCDP GED 24.1
#> 14 Natural Earth Breakaway and Disputed Areas 5.1.1
#> 15 Natural Earth Physical 10m Land 5.1.1
#> 16 FAO AQUASTAT Irrigation areas v5
#> 17 World Bank Global Subnational Poverty Atlas (GSAP) Oct. 2024
#> 18 MCC-PIK DOSE – Subnational Economic Output v2.9
#> 19 World Bank Subnational Doing Business Reports 2022
#> 20 World Bank Subnational Poverty and Inequality Database Oct 2024
#> 21 World Bank Subnational Poverty and Inequality Database Oct 2024
#> 22 World Bank Subnational Poverty and Inequality Database Oct 2024
#> 23 GHSL GHS-BUILT-S R2023
#> 24 GHSL GHS-BUILT-S R2023
#> 25 GHSL GHS-BUILT-S R2023
#> 26 GHSL GHS-BUILT-S R2023
#> 27 GHSL GHS-BUILT-S R2023
#> 28 GHSL GHS-BUILT-S R2023
#> 29 GHSL GHS-BUILT-S R2023
#> 30 GHSL GHS-BUILT-S R2023
#> 31 GHSL GHS-BUILT-S R2023
#> 32 GHSL GHS-BUILT-S R2023
#> 33 GHSL GHS-BUILT-S R2023
#> 34 GHSL GHS-BUILT-S R2023
#> 35 GHSL GHS-BUILT-V R2023
#> 36 GHSL GHS-BUILT-V R2023
#> 37 GHSL GHS-BUILT-V R2023
#> 38 GHSL GHS-BUILT-V R2023
#> 39 GHSL GHS-BUILT-V R2023
#> 40 GHSL GHS-BUILT-V R2023
#> 41 GHSL GHS-BUILT-V R2023
#> 42 GHSL GHS-BUILT-V R2023
#> 43 GHSL GHS-BUILT-V R2023
#> 44 GHSL GHS-BUILT-V R2023
#> 45 GHSL GHS-BUILT-V R2023
#> 46 GHSL GHS-BUILT-V R2023
#> 47 GHSL GHS Population Grid R2023
#> 48 GHSL GHS Population Grid R2023
#> 49 GHSL GHS Population Grid R2023
#> 50 GHSL GHS Population Grid R2023
#> 51 GHSL GHS Population Grid R2023
#> 52 GHSL GHS Population Grid R2023
#> 53 GHSL GHS Population Grid R2023
#> 54 GHSL GHS Population Grid R2023
#> 55 GHSL GHS Population Grid R2023
#> 56 GHSL GHS Population Grid R2023
#> 57 GHSL GHS Population Grid R2023
#> 58 GHSL GHS Population Grid R2023
#> 59 GHSL GHS Settlement Model Grid R2023
#> 60 GHSL GHS Settlement Model Grid R2023
#> 61 GHSL GHS Settlement Model Grid R2023
#> 62 GHSL GHS Settlement Model Grid R2023
#> 63 GHSL GHS Settlement Model Grid R2023
#> 64 GHSL GHS Settlement Model Grid R2023
#> 65 GHSL GHS Settlement Model Grid R2023
#> 66 GHSL GHS Settlement Model Grid R2023
#> 67 GHSL GHS Settlement Model Grid R2023
#> 68 GHSL GHS Settlement Model Grid R2023
#> 69 GHSL GHS Settlement Model Grid R2023
#> 70 GHSL GHS Settlement Model Grid R2023
#> id
#> 1 ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6b
#> 2 3868e499-5249-4582-958e-27de2b09945c
#> 3 52ac3e7e-b509-4d85-83b7-1875cb2b3afa
#> 4 5daf4962-3f07-408e-8e63-c1d7f8803070
#> 5 86532b44-ce5c-48a6-96f7-704885a9afb2
#> 6 0a746ab8-cc8e-4b31-bb71-8479a9ac8fa3
#> 7 4b61edd5-0d33-4a45-b0d3-757834c141ed
#> 8 a46019a1-4e3a-4cd0-81e6-eae6351b0415
#> 9 bdc773f4-7eb8-4f07-a4b5-663b8bc3f76e
#> 10 1e3634f6-267d-43c2-920e-34c9982e0a8d
#> 11 c1b411e0-5e6c-4b0f-9a4d-07e99f604ea9
#> 12 3900b527-a728-4c26-b0ab-f4441d3ee2e8
#> 13 2e5c66d2-d4e6-4282-9039-5b232b861093
#> 14 920663ad-d7e7-4528-b36d-4b7266def2b1
#> 15 92da9800-4520-4e87-a855-b28255452189
#> 16 514c2031-7216-4ac9-930d-ccb74ab2e73d
#> 17 2797f10a-a834-4f48-a6ea-3a1dbaf2e283
#> 18 4c471c6a-be5d-429a-8daa-3ac29b7ec36f
#> 19 e703f38e-5f1c-47c8-b798-e749ec503e98
#> 20 d8e6a15b-9353-42f5-8e79-8fa5da9428bc
#> 21 d8e6a15b-9353-42f5-8e79-8fa5da9428bc
#> 22 d8e6a15b-9353-42f5-8e79-8fa5da9428bc
#> 23 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 24 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 25 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 26 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 27 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 28 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 29 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 30 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 31 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 32 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 33 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 34 e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89
#> 35 9e85ae0c-c773-4636-a614-3933903e848c
#> 36 9e85ae0c-c773-4636-a614-3933903e848c
#> 37 9e85ae0c-c773-4636-a614-3933903e848c
#> 38 9e85ae0c-c773-4636-a614-3933903e848c
#> 39 9e85ae0c-c773-4636-a614-3933903e848c
#> 40 9e85ae0c-c773-4636-a614-3933903e848c
#> 41 9e85ae0c-c773-4636-a614-3933903e848c
#> 42 9e85ae0c-c773-4636-a614-3933903e848c
#> 43 9e85ae0c-c773-4636-a614-3933903e848c
#> 44 9e85ae0c-c773-4636-a614-3933903e848c
#> 45 9e85ae0c-c773-4636-a614-3933903e848c
#> 46 9e85ae0c-c773-4636-a614-3933903e848c
#> 47 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 48 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 49 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 50 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 51 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 52 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 53 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 54 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 55 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 56 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 57 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 58 ae6a7612-4bef-452f-acd6-d2212cf9a7c5
#> 59 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 60 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 61 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 62 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 63 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 64 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 65 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 66 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 67 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 68 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 69 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> 70 f37f3b1c-3b16-48e4-8aa3-7162b35a8096
#> filename
#> 1 ETH ICR cShapes/2.0/ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6b/CShapes-2.0.geojson
#> 2 IHME GHDx Under-5 mortality/2019/3868e499-5249-4582-958e-27de2b09945c/IHME_AFRICA_U5M_1998_2017_UNDER_5_GEO_TIFF.zip
#> 3 World Bank Geocoded Research Release/1.4.2/52ac3e7e-b509-4d85-83b7-1875cb2b3afa/WorldBank_GeocodedResearchRelease_Level1_v1.4.2.zip
#> 4 WorldPop Migration Flows/2019/5daf4962-3f07-408e-8e63-c1d7f8803070/SexDisaggregated_Migration.zip
#> 5 GISCO Geostat Census Grid/2021/86532b44-ce5c-48a6-96f7-704885a9afb2/Eurostat_Census-GRID_2021_V2-0.zip
#> 6 SEDAC Food Insecurity Hotspots/v1/0a746ab8-cc8e-4b31-bb71-8479a9ac8fa3/food-food-insecurity-hotspots-inputs-geographic-geotiff.zip
#> 7 WIDE Education Inequalities/9.23/4b61edd5-0d33-4a45-b0d3-757834c141ed/1699460825-wide_2023_sept.csv
#> 8 SEDAC Global Gridded Relative Deprivation Index (GRDI)/v1/a46019a1-4e3a-4cd0-81e6-eae6351b0415/povmap-grdi-v1-geotiff.zip
#> 9 Geocoded Disasters (GDIS) Dataset/v1/bdc773f4-7eb8-4f07-a4b5-663b8bc3f76e/pend-gdis-1960-2018-disasterlocations-rdata.zip
#> 10 SEDAC Global Subnational Infant Mortality Rates/v2.01/1e3634f6-267d-43c2-920e-34c9982e0a8d/povmap-global-subnational-infant-mortality-rates-v2-01-geotiff.zip
#> 11 ReliefWeb Disasters List/2024/c1b411e0-5e6c-4b0f-9a4d-07e99f604ea9/reliefweb-disasters-list.csv
#> 12 ETH ICR GeoEPR/2021/3900b527-a728-4c26-b0ab-f4441d3ee2e8/GeoEPR-2021.geojson
#> 13 UCDP GED/24.1/2e5c66d2-d4e6-4282-9039-5b232b861093/ged241-rds.zip
#> 14 Natural Earth Breakaway and Disputed Areas/5.1.1/920663ad-d7e7-4528-b36d-4b7266def2b1/ne_50m_admin_0_breakaway_disputed_areas.zip
#> 15 Natural Earth Physical 10m Land/5.1.1/92da9800-4520-4e87-a855-b28255452189/ne_10m_land.zip
#> 16 FAO AQUASTAT Irrigation areas/v5/514c2031-7216-4ac9-930d-ccb74ab2e73d/gmia_v5_aei_pct_asc.zip
#> 17 World Bank Global Subnational Poverty Atlas (GSAP)/Oct. 2024/2797f10a-a834-4f48-a6ea-3a1dbaf2e283/AM24%20-%20GSAP%20data%202010%202019%202021.xlsx
#> 18 MCC-PIK DOSE – Subnational Economic Output/v2.9/4c471c6a-be5d-429a-8daa-3ac29b7ec36f/DOSE_V2.9.csv
#> 19 World Bank Subnational Doing Business Reports/2022/e703f38e-5f1c-47c8-b798-e749ec503e98/Historical-subnational-database-2022.xlsx
#> 20 World Bank Subnational Poverty and Inequality Database/Oct 2024/d8e6a15b-9353-42f5-8e79-8fa5da9428bc/AM24_MASTER.ZIP
#> 21 World Bank Subnational Poverty and Inequality Database/Oct 2024/d8e6a15b-9353-42f5-8e79-8fa5da9428bc/AM24%20-%20SPID%20all%20groups.xlsx
#> 22 World Bank Subnational Poverty and Inequality Database/Oct 2024/d8e6a15b-9353-42f5-8e79-8fa5da9428bc/AM24%20-%20Subnational%20MPM.xlsx
#> 23 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2030_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 24 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2025_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 25 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2020_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 26 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2015_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 27 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2010_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 28 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2005_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 29 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E2000_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 30 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E1995_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 31 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E1990_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 32 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E1985_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 33 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E1980_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 34 GHSL GHS-BUILT-S/R2023/e59ea65b-8a6b-4f60-aa8f-6c53f1e78e89/GHS_BUILT_S_E1975_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 35 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2030_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 36 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2025_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 37 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2020_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 38 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2015_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 39 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2010_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 40 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2005_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 41 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E2000_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 42 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E1995_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 43 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E1990_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 44 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E1985_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 45 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E1980_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 46 GHSL GHS-BUILT-V/R2023/9e85ae0c-c773-4636-a614-3933903e848c/GHS_BUILT_V_E1975_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 47 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2030_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 48 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2025_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 49 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2020_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 50 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2015_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 51 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2010_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 52 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2005_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 53 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E2000_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 54 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E1995_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 55 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E1990_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 56 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E1985_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 57 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E1980_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 58 GHSL GHS Population Grid/R2023/ae6a7612-4bef-452f-acd6-d2212cf9a7c5/GHS_POP_E1975_GLOBE_R2023A_4326_3ss_V1_0.zip
#> 59 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2030_GLOBE_R2023A_54009_1000_V2_0.zip
#> 60 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2025_GLOBE_R2023A_54009_1000_V2_0.zip
#> 61 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2020_GLOBE_R2023A_54009_1000_V2_0.zip
#> 62 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2015_GLOBE_R2023A_54009_1000_V2_0.zip
#> 63 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2010_GLOBE_R2023A_54009_1000_V2_0.zip
#> 64 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2005_GLOBE_R2023A_54009_1000_V2_0.zip
#> 65 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E2000_GLOBE_R2023A_54009_1000_V2_0.zip
#> 66 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E1995_GLOBE_R2023A_54009_1000_V2_0.zip
#> 67 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E1990_GLOBE_R2023A_54009_1000_V2_0.zip
#> 68 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E1985_GLOBE_R2023A_54009_1000_V2_0.zip
#> 69 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E1980_GLOBE_R2023A_54009_1000_V2_0.zip
#> 70 GHSL GHS Settlement Model Grid/R2023/f37f3b1c-3b16-48e4-8aa3-7162b35a8096/GHS_SMOD_E1975_GLOBE_R2023A_54009_1000_V2_0.zip
#> md5
#> 1 39d8daa630edd5e803db33f0531834c0
#> 2 5cfe5e82d63dae32789cb268ecfbcc30
#> 3 0477b2d21c1a1a01fff677ad634ce965
#> 4 f2ea5907082391376270a2a70c28be5e
#> 5 5adc394e050278c2722dc2604abad8cd
#> 6 32a6b528b6838ed105118632158b0670
#> 7 6055b7f87e59fe030fe12c7e7db9322f
#> 8 b8a101f06314ab076ec501a125c3e95a
#> 9 b35ffa42a03baecc33447b180f89db1b
#> 10 3891e95caa04e8903024856d1ba22e19
#> 11 56a4b859edcf38fa41354801bbb7f9ff
#> 12 0be53272bde0c59b371f17d7aea5b388
#> 13 3bced575397fc8c313f284599d7038fc
#> 14 e40b361feb30d5c78999029dce11f245
#> 15 be3001f37196d2894e17aacd13ff2cc2
#> 16 f54514aaf4fef7c90609921e3bb66d0b
#> 17 ce260ebb683b89d929faae424bff7524
#> 18 a62d060b755863cd4451f686f01ac7bb
#> 19 91b69be2ad338dbed4b664a727d9d4e4
#> 20 ea650d2a59e9af93936d2bbaf55d00ab
#> 21 c4865c26f1bffb9dd3b55a22d8e29bae
#> 22 6d08564986442bfce16d035ff67f03d1
#> 23 d93a8e150ac71ff2de116527e378ebb9
#> 24 dce28505dc9b483b60cb8237a449a68e
#> 25 f7bb34110076de010fea1210cf231b42
#> 26 5aaae9f2999de3034b74f96f6c501bc8
#> 27 6b6d2697636012402d4a6e104db80a25
#> 28 a61bc34a8f41df72e7995210bd7e0e6b
#> 29 <NA>
#> 30 9b8a9dca05e7752e891770257262a930
#> 31 <NA>
#> 32 <NA>
#> 33 c49efeb058dba4f2ee782439d5bc8e6c
#> 34 <NA>
#> 35 038ab95df66adc5d7c87ddad74f5f409
#> 36 10b1433f1eae17c8fa4b7359f9aab6c9
#> 37 <NA>
#> 38 <NA>
#> 39 <NA>
#> 40 <NA>
#> 41 <NA>
#> 42 <NA>
#> 43 <NA>
#> 44 <NA>
#> 45 <NA>
#> 46 63e65985e6fc3f8ad3f8f781eb6e243e
#> 47 38bb5aa5146a5a122adf0984ffe9014b
#> 48 75d3ee17dcd7576ab426dc089d59959b
#> 49 <NA>
#> 50 <NA>
#> 51 <NA>
#> 52 9061454855f860632f098d5e23d43c0a
#> 53 <NA>
#> 54 <NA>
#> 55 63605c7507e8f3570d26901c4d34228c
#> 56 <NA>
#> 57 c529f884a1b31a55e20c70def61cbbc8
#> 58 <NA>
#> 59 <NA>
#> 60 <NA>
#> 61 36cbc67335ac6d52c866c7c34e33c84e
#> 62 8167c17dbf03e4d1bfddd848a7458291
#> 63 <NA>
#> 64 c7e45413ecb338d128fbf0d1998bb7ae
#> 65 <NA>
#> 66 <NA>
#> 67 95bf4aebe00608d536676f88a6c0530d
#> 68 <NA>
#> 69 2b8ae4a397ecf14c5a2d1b3bc8efceea
#> 70 164933823de4044c756ad58237f5786fUse check_pgsourcefiles() to run the check against your
local storage:
check_pgsourcefiles()
# "All files in your local storage are similar to a tested set."Metadata Linking: Variables → Sources → Bibliography
The three metadata objects form a chain:
pgvariables$source_ids → pgsources$id → pgsources$citation_keys → REFERENCES.bibHere’s the full lookup for a single variable:
# 1. Find source IDs for a variable
var_row <- pgvariables[pgvariables$name == "cru_tmp", ]
source_ids <- strsplit(var_row$source_ids, ", ")[[1]]
source_ids
#> [1] "ac037134-3567-49d9-a3ba-64f37c1ee698"
# 2. Look up source metadata
src <- pgsources[pgsources$id %in% source_ids, c("source_name", "source_version", "citation_keys", "license")]
src
#> source_name source_version citation_keys
#> 45 CRU Climate tmp v4.09 harrisVersion4CRU2020
#> license
#> 45 https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
# 3. Get citation keys
bibkeys <- unlist(strsplit(src$citation_keys, "; "))
bibkeys
#> [1] "harrisVersion4CRU2020"Then retrieve the full bibliography:
get_bibliography(bibkeys[1])
#> [1] I. Harris, T. J. Osborn, P. Jones, et al. "Version 4 of the CRU TS
#> Monthly High-Resolution Gridded Multivariate Climate Dataset". In:
#> _Scientific Data_ 7.1 (Apr. 2020), p. 109. ISSN: 2052-4463. DOI:
#> 10.1038/s41597-020-0453-3.Searching Metadata: pgsearch()
pgsearch() searches across all text fields in
pgsources using regex:
results <- pgsearch("population")It returns a named list with matches from each search field:
# Sources matching by name
results$in_name[, c("source_name", "source_version")]
#> # A tibble: 1 × 2
#> source_name source_version
#> <chr> <chr>
#> 1 GHSL GHS Population Grid R2023
# Sources matching by tag
results$in_tags[, c("source_name", "tags")]
#> # A tibble: 9 × 2
#> source_name tags
#> <chr> <chr>
#> 1 ETH ICR EPR Core ethnicity, population, social stru…
#> 2 ETH ICR GeoEPR ethnicity, population, social stru…
#> 3 MCC-PIK DOSE – Subnational Economic Output population, socio - economic
#> 4 GHSL GHS-BUILT-C demographic, infrastructure, land …
#> 5 GHSL GHS-BUILT-H demographic, land use, population
#> 6 GHSL GHS Population Grid demographic, infrastructure, land …
#> 7 GHSL GHS-BUILT-S demographic, land use, population
#> 8 GHSL GHS Settlement Model Grid demographic, population, urbanizat…
#> 9 GHS-WUP-DEGURBA urban extent, populationSearch by temporal resolution:
pgsearch("Monthly")$in_temporal_resolution[, c("source_name", "temporal_resolution")]
#> # A tibble: 9 × 2
#> source_name temporal_resolution
#> <chr> <chr>
#> 1 UCDP GED Higher than monthly
#> 2 Armed Conflict Location & Event Data (ACLED) Higher than monthly
#> 3 Geocoded Peacekeeping Operations (Geo-PKO) Monthly
#> 4 ETH ICR cShapes Higher than monthly
#> 5 CRU Climate tmp Monthly
#> 6 CRU Climate pre Monthly
#> 7 CRU Climate pet Monthly
#> 8 Global SPEI database Monthly
#> 9 UCDP GED Higher than monthlySearch by spatial extent:
pgsearch("World")$in_spatial_extent[, c("source_name", "spatial_extent")]
#> # A tibble: 46 × 2
#> source_name spatial_extent
#> <chr> <chr>
#> 1 EOG Annual VIIRS Nighttime Lights World
#> 2 Global Area Equipped for Irrigation Dataset 1900-2015 World
#> 3 GlobalDataLab Area Database World
#> 4 SEDAC Global Subnational Infant Mortality Rates World
#> 5 Li Nighttime World
#> 6 World Bank Global Subnational Poverty Atlas (GSAP) World
#> 7 ETH ICR EPR Core World
#> 8 UCDP GED World
#> 9 ETH ICR GeoEPR World
#> 10 WIDE Education Inequalities World
#> # ℹ 36 more rowsSearching Bibliography Elements
Include bib_element to also search author names, titles,
journals, or years:
# Find sources citing a specific author
harris_results <- pgsearch("Harris", bib_element = "author")
#> No results.
#> No results.
#> No results.
#> No results.
#> No results.
#> No results.
#> No results.
harris_results$in_element[, c("source_name", "citation_keys")]
#> # A tibble: 3 × 2
#> # Rowwise:
#> source_name citation_keys
#> <chr> <chr>
#> 1 CRU Climate tmp harrisVersion4CRU2020
#> 2 CRU Climate pre harrisVersion4CRU2020
#> 3 CRU Climate pet harrisVersion4CRU2020Listing Raw Files
pg_rawfiles() returns a data frame of all files PRIOGRID
can download, with their URLs and expected local paths:
files <- pg_rawfiles()
head(files[, c("source_name", "source_version", "filename")])
#> # A tibble: 6 × 3
#> source_name source_version filename
#> <chr> <chr> <chr>
#> 1 SEDAC Food Insecurity Hotspots v1 SEDAC Food …
#> 2 SEDAC Global Subnational Infant Mortality Rates v2.01 SEDAC Globa…
#> 3 Li Nighttime v8 Li Nighttim…
#> 4 World Bank Global Subnational Poverty Atlas (GSAP) Oct. 2024 World Bank …
#> 5 ETH ICR EPR Core 2023 ETH ICR EPR…
#> 6 UCDP GED 24.1 UCDP GED/24…Filter to a specific source:
files[files$source_name == "ETH ICR cShapes", c("source_name", "filename", "url")]
#> # A tibble: 1 × 3
#> source_name filename url
#> <chr> <chr> <chr>
#> 1 ETH ICR cShapes ETH ICR cShapes/2.0/ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6… http…Download a specific source:
ucdp_files <- pg_rawfiles() |> dplyr::filter(source_name == "UCDP GED")
download_pg_rawdata(file_info = ucdp_files)Get the local path to a downloaded file:
get_pgfile(
source_name = "ETH ICR cShapes",
source_version = "2.0",
id = "ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6b"
)Checking Data Availability
pg_data_availability() shows which sources have been
downloaded locally:
pg_data_availability()
# source_name source_version n_files n_present all_present
# 1 CRU TS 4.08 1 1 TRUE
# 2 ETH ICR cShapes 2.0 1 1 TRUE
# ...Next Steps
- Citations and Bibliography — citing data providers in publications
- Contributing to PRIOGRID — adding new sources and variables