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SHDI #90

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982d937
first commit -- update REFERENCES.bib
gbenz23 Sep 25, 2025
74dd79b
create GLC folder with relevant files and create new source
gbenz23 Sep 25, 2025
ea886ca
Notes on conceptual framework for modelling GLC
gbenz23 Sep 29, 2025
32f1627
Map out process for beta testing
gbenz23 Sep 30, 2025
d9733ba
ref robust_transformations
gbenz23 Oct 2, 2025
2e5b05a
URL list contains all Zenodo download zip files
gbenz23 Oct 9, 2025
dc80bd9
update new source to reference v2
gbenz23 Oct 9, 2025
f07affa
wip
gbenz23 Oct 11, 2025
8eddac9
wip
gbenz23 Oct 11, 2025
ec90797
wip
gbenz23 Oct 11, 2025
652c2d0
wip
gbenz23 Oct 12, 2025
b27220e
wip
gbenz23 Oct 12, 2025
baa2602
develop baseline gen_landcover function for glc-lc types
gbenz23 Oct 12, 2025
a1dd6be
add foldersize function for flexible beta test
gbenz23 Oct 15, 2025
6ee5191
wip
gbenz23 Oct 15, 2025
b732685
wip
gbenz23 Oct 22, 2025
f858f51
adjust robust_transformation for cropping to smaller subtiles
gbenz23 Oct 22, 2025
972afc7
format for integration
gbenz23 Oct 27, 2025
939d03b
remove GLC folder -- necessary files pushed to R or data folders
gbenz23 Oct 27, 2025
091a4e4
started on read function
krihelskog Nov 18, 2025
571628e
Added SHDI prio mirror to sources
krihelskog Nov 20, 2025
d239fc1
Added SHDI prio mirror to sources
krihelskog Nov 20, 2025
ff33bf7
Keep local version for data_raw/sources.csv (resolve merge)
krihelskog Nov 20, 2025
27c8956
Created read functions for SHDI
krihelskog Nov 20, 2025
2a690d3
Created read functions for SHDI
krihelskog Nov 20, 2025
bbd1f48
Merge branch 'kh_SHDI' of github.com:prio-data/priogrid into kh_SHDI
krihelskog Nov 20, 2025
641ee4c
Started on gen function
krihelskog Nov 20, 2025
f359520
updated bibliography
krihelskog Nov 25, 2025
4f2241a
Add geoBoundaries to sources and read function
krihelskog Dec 18, 2025
67f5e00
Gen function and replacing empty geometries
krihelskog Dec 19, 2025
0eb4381
Improved SHDI function and added gen_ wrapper functions for common va…
krihelskog Jan 6, 2026
7a03f50
Fixed error in function
krihelskog Jan 6, 2026
ca9288b
Fixed error in function
krihelskog Jan 6, 2026
fd864b1
Merge branch 'kh_SHDI' of github.com:prio-data/priogrid into kh_SHDI
krihelskog Jan 6, 2026
d95d619
Changed object names in gen functions
krihelskog Jan 10, 2026
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417 changes: 417 additions & 0 deletions R/data_SHDI.R
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485 changes: 485 additions & 0 deletions R/data_glc.R
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7 changes: 6 additions & 1 deletion R/utility.R
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Expand Up @@ -236,8 +236,13 @@ rast_to_df <- function(rast, static = TRUE, varname = NULL){
#' \code{\link[terra]{aggregate}}, \code{\link[terra]{disagg}}, \code{\link[terra]{resample}}
#'
#' @export
robust_transformation <- function(r, agg_fun, disagg_method = "near", ...){
robust_transformation <- function(r, agg_fun, disagg_method = "near", tiled = FALSE, ...){
pg <- prio_blank_grid()

if(tiled){
pg <- terra::crop(pg, r)
}

temporary_directory <- file.path(pgoptions$get_rawfolder(), "tmp", tempdir() |> basename())
dir.create(temporary_directory)

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2 changes: 1 addition & 1 deletion data_raw/pgsources.R
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Expand Up @@ -4,4 +4,4 @@ if(length(pgsources$id) != length(unique(pgsources$id))){
stop("Non-unique IDs. Double check sources.csv")
}

usethis::use_data(pgsources, overwrite = TRUE)
usethis::use_data(pgsources, overwrite = FALSE)
5 changes: 4 additions & 1 deletion data_raw/sources.csv
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Expand Up @@ -21,7 +21,7 @@ id source_name source_version license citation_keys aws_bucket aws_region downlo
7dcbfbfb-9667-4684-af34-85f69fa8d0a0 Geocoded Peacekeeping Operations (Geo-PKO) 2.2 All rights are reserved cilMappingBlueHelmets2020 NA NA https://www.uu.se/download/18.24f546b0193ac54c49a1b400/1734082829555/Geo_PKO_v.2.2.rds https://www.uu.se/en/department/peace-and-conflict-research/research/research-data/geo-pko-dataset peacekeeping, social structure, un World Monthly NA NA NA NA NA 2024-12-04 07:53:58
82bc4c6f-9904-484f-aa9a-77771d076690 HILDA+ v1.0 CC BY 4.0 winklerHILDAGlobalLand2020 NA NA https://download.pangaea.de/dataset/921846/files/hildap_vGLOB-1.0_geotiff.zip https://doi.pangaea.de/10.1594/PANGAEA.921846?format=html#download landcover World Yearly NA NA TRUE TRUE FALSE 2025-05-26 16:09:20
86532b44-ce5c-48a6-96f7-704885a9afb2 GISCO Geostat Census Grid 2021 CC BY 4.0 geographicinformationsystemofthecommissionGeostatCensusGrid2024 NA NA https://gisco-services.ec.europa.eu/census/2021/Eurostat_Census-GRID_2021_V2-0.zip https://ec.europa.eu/eurostat/web/gisco/geodata/population-distribution/geostat demographic, migration Single continent Yearly NA NA TRUE TRUE NA 2024-12-03 13:53:47
8aaf6b27-6372-43da-87a9-d4235095bb2c GlobalDataLab Subnational Human Development (SHDI) v.7.0 https://globaldatalab.org/termsofuse/ globaldatalabSubnationalHumanDevelopment2019; smitsSubnationalHumanDevelopment2019 NA NA NA https://globaldatalab.org/shdi/ corruption, governance World Yearly NA NA TRUE TRUE NA 2024-12-04 08:23:14
8aaf6b27-6372-43da-87a9-d4235095bb2c GlobalDataLab Subnational Human Development (SHDI) v.7.0 CC BY-NC globaldatalabSubnationalHumanDevelopment2019; smitsSubnationalHumanDevelopment2019 NA NA NA https://globaldatalab.org/shdi/ corruption, governance World Yearly NA urls/8aaf6b27-6372-43da-87a9-d4235095bb2c.txt FALSE TRUE TRUE 2024-12-04 08:23:14
8c8192eb-cc29-4598-8f8a-ec190ba35c2d Global Multi-resolution Terrain Elevation Data GMTED2010 CC BY 4.0 danielsonGlobalMultiresolutionTerrain2011 NA NA http://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/GMTED_Metadata/GMTED2010_Spatial_Metadata.zip https://www.usgs.gov/coastal-changes-and-impacts/gmted2010 ruggedness, terrain World Static athmaniaExternalValidationASTER2014 NA TRUE FALSE FALSE 2025-05-15 11:20:25
90f82033-62d2-4751-9b0e-f6008cedb202 World Bank Geocoded Research Release 1.4.2 ODC-By 1.0 NA NA https://github.com/AidData-WM/public_datasets/blob/master/geocoded/WorldBank_GeocodedResearchRelease_Level1_v1.4.2.zip?raw=true https://www.aiddata.org/data/world-bank-geocoded-research-release-level-1-v1-4-2 aid World Yearly briggsPoorTargetingGridded2018 NA TRUE TRUE FALSE 2025-03-25 11:36:59
920663ad-d7e7-4528-b36d-4b7266def2b1 Natural Earth Breakaway and Disputed Areas 5.1.1 Public Domain naturalearthAdmin0Breakaway2024 NA NA https://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_disputed_areas.zip https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-breakaway-disputed-areas/ dispute land, social structure World Static NA NA FALSE TRUE NA 2025-09-15 13:32:01
Expand All @@ -44,3 +44,6 @@ e703f38e-5f1c-47c8-b798-e749ec503e98 World Bank Subnational Doing Business Repor
ea215b4a-56b6-48d2-a6ae-fd32a9c4fc77 GHSL GHS-DUC R2023 CC BY 4.0 schiavinaGHSDUCR2023AGHS2023 NA NA NA https://human-settlement.emergency.copernicus.eu/ghs_duc2023.php demographic, urbanization World Less than yearly florioEstimatingGeographicAccess2023 NA TRUE TRUE NA 2024-12-03 14:20:41
ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6b ETH ICR cShapes 2.0 CC BY-NC-SA 4.0 schvitzMappingInternationalSystem2022 NA NA https://icr.ethz.ch/data/cshapes/CShapes-2.0.geojson https://icr.ethz.ch/data/cshapes/ boundary, international system, political unit World Higher than monthly weidmannGeographyInternationalSystem2010; gleditschRevisedListIndependent1999 NA TRUE TRUE NA 2024-12-03 13:39:59
f37f3b1c-3b16-48e4-8aa3-7162b35a8096 GHSL GHS Settlement Model Grid R2023 CC BY 4.0 schiavinaGHSSMODR2023AGHS2023 NA NA urls/f37f3b1c-3b16-48e4-8aa3-7162b35a8096.txt https://human-settlement.emergency.copernicus.eu/download.php?ds=smod demographic, population, urbanization World Less than yearly melchiorriUnveiling25Years2018 NA NA TRUE NA 2024-12-05 11:06:50
0313d990-b4f8-42ad-9553-6d25d23ae01a GLC_FCS30 v1 CC BY 4.0 zhang_glc_fcs30_2021 NA NA urls/0313d990-b4f8-42ad-9553-6d25d23ae01a.txt https://zenodo.org/records/8239305 landcover World Yearly NA NA TRUE TRUE FALSE 2025-09-25 11:51:45
7f03a296-4329-4458-8b62-83c3d27530af GLC_FCS30 v2 CC BY 4.0 NA NA urls/7f03a296-4329-4458-8b62-83c3d27530af.txt https://zenodo.org/records/15063683 landcover World Yearly NA NA FALSE FALSE FALSE 2025-10-09 13:56:02
a8e35e36-9f7e-4194-9cc4-ce8ca59f7b51 geoBoundaries 5.0.0 CC BY 4.0 runfolaGeoBoundariesGlobalDatabase2020 NA NA https://github.com/wmgeolab/geoBoundaries/raw/main/releaseData/CGAZ/geoBoundariesCGAZ_ADM1.zip https://www.geoboundaries.org geography, boundaries World Static NA NA FALSE FALSE FALSE 2025-12-18 10:35:42
46 changes: 46 additions & 0 deletions inst/REFERENCES.bib
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Expand Up @@ -952,3 +952,49 @@ @article{harrisVersion4CRU2020
doi = {10.1038/s41597-020-0453-3},
abstract = {CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5{$^\circ$} latitude by 0.5{$^\circ$} longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901--2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.}
}

@article{zhang_glc_fcs30_2021,
title = {{GLC}\_FCS30: global land-cover product with fine classification system at 30\&thinsp;m using time-series {Landsat} imagery},
volume = {13},
issn = {1866-3508},
shorttitle = {{GLC}\_FCS30},
url = {https://essd.copernicus.org/articles/13/2753/2021/},
doi = {10.5194/essd-13-2753-2021},
abstract = {Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC\_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI\_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC\_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC\_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC\_FCS30-2015 achieved an overall accuracy of 82.5 \% and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 \% and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 \% and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI\_LC, MCD12Q1, FROM\_GLC, and GlobeLand30) indicated that GLC\_FCS30-2015 provides more spatial details than CCI\_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM\_GLC-2015 and GlobeLand30-2010. They also showed that GLC\_FCS30-2015 achieved the best overall accuracy of 82.5 \% against FROM\_GLC-2015 of 59.1 \% and GlobeLand30-2010 of 75.9 \%. Therefore, it is concluded that the GLC\_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC\_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).},
language = {English},
number = {6},
urldate = {2025-09-25},
journal = {Earth System Science Data},
author = {Zhang, Xiao and Liu, Liangyun and Chen, Xidong and Gao, Yuan and Xie, Shuai and Mi, Jun},
month = jun,
year = {2021},
pages = {2753--2776},
}

@article{Liu_glc_fcs30_v2_2022,
title = {Algorithm, Progresses, Datasets and Validation of GLC_FCS30D: the first global 30 m land-cover dynamic product with fine classification system from 1985 to 2022},
volume = {2},
shorttitle = {{GLC}\_FCS30\_v2},
url = {https://isprs-annals.copernicus.org/articles/X-2-2024/137/2024/},
doi = {10.5194/isprs-annals-X-2-2024-137-2024},
language = {English},
journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
author = {Liu, L. and Zhang, X.}
year = {2024},
pages = {137-143},
}

@article{runfolaGeoBoundariesGlobalDatabase2020,
title = {{{geoBoundaries}}: {{A}} Global Database of Political Administrative Boundaries},
author = {Runfola, Daniel and Anderson, Austin and Baier, Heather and Crittenden, Matt and Dowker, Elizabeth and Fuhrig, Sydney and Goodman, Seth and Grimsley, Grace and Layko, Rachel and Melville, Graham and Mulder, Maddy and Oberman, Rachel and Panganiban, Joshua and Peck, Andrew and Seitz, Leigh and Shea, Sylvia and Slevin, Hannah and Youngerman, Rebecca and Hobbs, Lauren},
editor = {Tang, Wenwu},
year = 2020,
month = apr,
journal = {PLOS ONE},
volume = {15},
number = {4},
pages = {e0231866},
issn = {1932-6203},
doi = {10.1371/journal.pone.0231866},
}

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https://cdn.cloud.prio.org/files/831b54bf-9efd-4be6-a618-9794109790c1/SHDI-SGDI-Total%2080.csv?inline=true
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