electricsheepafrica/africa-gabon-gabon-risk-assessment-indicators-5aeb3d7f

Gabon - Risk Assessment Indicators | Africa (Gabon official open data)

48 rows - 1 Africa country - not-applicable - Repackaged by Electric Sheep Africa

TL;DR

This dataset packages one official CSV resource from Gabon as ML-ready Parquet. The source file is the provenance boundary; all usable indicators or tabular columns from the resource stay together in this repo.

About the source

• Source: Gabon - Risk Assessment Indicators
• Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology)
• Resource: GAB_ADM2_flood_exposure.csv
• Format: CSV
• License: CC BY-SA
• Packaging mode: tabular_resource

Geographic coverage

1 Africa country:

Country	Rows	First year	Last year	Name
GAB	48	n/a	n/a	Gabon

Indicators or Resource Contents

• This source file is packaged as a normalized tabular resource.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier for tabular resources.	fd931f68-fba8-41a8-85d4-d913c002a12c:0
country_iso3	category	ISO3 country code.	GAB
country_name	category	Country name.	Gabon
adm2_pcode	string	Source column.	GAB003001
rp10_crops_30cm_km2	float64	Source column.	0.0
rp10_crops_30cm_areapct	float64	Source column.	0.0
rp10_crops_30cm_croppct	float64	Source column.	0.0
rp10_female_pop_30cm	int64	Source column.	2964
rp10_children_u5_30cm	int64	Source column.	754
rp10_female_u5_30cm	int64	Source column.	375
rp10_elderly_30cm	int64	Source column.	341
rp10_pop_u15_30cm	int64	Source column.	2259
rp10_female_u15_30cm	int64	Source column.	1111
rp10_wra_pop_30cm	int64	Source column.	1335
rp10_dependency_ratio_30cm	float64	Source column.	77.65
rp10_education_30cm_pct	int64	Source column.	0
rp10_education_30cm_count	int64	Source column.	0
rp10_hospitals_30cm_pct	int64	Source column.	0
rp10_hospitals_30cm_count	int64	Source column.	0
rp10_primary_healthcare_30cm_pct	int64	Source column.	0
rp10_primary_healthcare_30cm_count	int64	Source column.	0
rp50_crops_30cm_km2	float64	Source column.	0.0
rp50_crops_30cm_areapct	float64	Source column.	0.0
rp50_crops_30cm_croppct	float64	Source column.	0.0
rp50_female_pop_30cm	int64	Source column.	3068
rp50_children_u5_30cm	int64	Source column.	780
rp50_female_u5_30cm	int64	Source column.	388
rp50_elderly_30cm	int64	Source column.	353
rp50_pop_u15_30cm	int64	Source column.	2338
rp50_female_u15_30cm	int64	Source column.	1149
rp50_wra_pop_30cm	int64	Source column.	1382
rp50_dependency_ratio_30cm	float64	Source column.	77.65
rp50_education_30cm_pct	int64	Source column.	0
rp50_education_30cm_count	int64	Source column.	0
rp50_hospitals_30cm_pct	int64	Source column.	100
rp50_hospitals_30cm_count	int64	Source column.	1
rp50_primary_healthcare_30cm_pct	int64	Source column.	0
rp50_primary_healthcare_30cm_count	int64	Source column.	0
rp100_crops_30cm_km2	float64	Source column.	0.0
rp100_crops_30cm_areapct	float64	Source column.	0.0
rp100_crops_30cm_croppct	float64	Source column.	0.0
rp100_female_pop_30cm	int64	Source column.	3068
rp100_children_u5_30cm	int64	Source column.	780
rp100_female_u5_30cm	int64	Source column.	388
rp100_elderly_30cm	int64	Source column.	353
rp100_pop_u15_30cm	int64	Source column.	2338
rp100_female_u15_30cm	int64	Source column.	1149
rp100_wra_pop_30cm	int64	Source column.	1382
rp100_dependency_ratio_30cm	float64	Source column.	77.65
rp100_education_30cm_pct	int64	Source column.	0
rp100_education_30cm_count	int64	Source column.	0
rp100_hospitals_30cm_pct	int64	Source column.	100
rp100_hospitals_30cm_count	int64	Source column.	1
rp100_primary_healthcare_30cm_pct	int64	Source column.	0
rp100_primary_healthcare_30cm_count	int64	Source column.	0
rp500_crops_30cm_km2	float64	Source column.	0.0
rp500_crops_30cm_areapct	float64	Source column.	0.0
rp500_crops_30cm_croppct	float64	Source column.	0.0
rp500_female_pop_30cm	int64	Source column.	3126
rp500_children_u5_30cm	int64	Source column.	795
rp500_female_u5_30cm	int64	Source column.	395
rp500_elderly_30cm	int64	Source column.	359
rp500_pop_u15_30cm	int64	Source column.	2383
rp500_female_u15_30cm	int64	Source column.	1171
rp500_wra_pop_30cm	int64	Source column.	1408
rp500_dependency_ratio_30cm	float64	Source column.	77.65
rp500_education_30cm_pct	int64	Source column.	0
rp500_education_30cm_count	int64	Source column.	0
rp500_hospitals_30cm_pct	int64	Source column.	100
rp500_hospitals_30cm_count	int64	Source column.	1
rp500_primary_healthcare_30cm_pct	int64	Source column.	0
rp500_primary_healthcare_30cm_count	int64	Source column.	0
source_period_start_year	Int64	First year inferred from source resource metadata.	``
source_period_end_year	Int64	Last year inferred from source resource metadata.	``
source_period_label	string	Human-readable period inferred from source resource metadata.	``
source_provider	category	Publishing organization.	HeiGIT (Heidelberg Institute for Geoinformation Technology)
source_dataset	category	Source package title.	Gabon - Risk Assessment Indicators
source_resource	category	Source resource title.	GAB_ADM2_flood_exposure.csv
source_package_id	category	CKAN package UUID.	99e3171a-58b4-4460-889e-20178ee63ce2
source_resource_id	category	CKAN resource UUID.	fd931f68-fba8-41a8-85d4-d913c002a12c
source_url	category	Original source resource URL.	https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/
license_id	category	Source license identifier.	cc-by-sa
retrieved_at	category	UTC retrieval timestamp.	2026-08-30T05:38:32Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-gabon-gabon-risk-assessment-indicators-5aeb3d7f")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "GAB"]

Work with indicators

if "indicator_id" in df.columns:
    print(df["indicator_id"].value_counts().head())
    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])

Citation

@misc{electric_sheep_africa_africa_gabon_gabon_risk_assessment_indicators_5aeb3d7f_2026,
  title        = {Gabon - Risk Assessment Indicators | Africa (Gabon official open data)},
  author       = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/gabon---risk-assessment-indicators},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-gabon-gabon-risk-assessment-indicators-5aeb3d7f}}
}

License

Released under CC BY-SA.

Original data (c) HeiGIT (Heidelberg Institute for Geoinformation Technology). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on Hugging Face. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

---

Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/gab/GAB_ADM2_flood_exposure.csv