electricsheepafrica/africa-namibia-namibia-risk-assessment-indicators-11b1e88e

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

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

TL;DR

This dataset packages one official CSV resource from Namibia 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: Namibia - Risk Assessment Indicators
• Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology)
• Resource: NAM_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
NAM	107	n/a	n/a	Namibia

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.	8aeb4da1-8a55-4b44-b615-31d53d28c176:0
country_iso3	category	ISO3 country code.	NAM
country_name	category	Country name.	Namibia
adm2_pcode	string	Source column.	NA0901
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.	547
rp10_children_u5_30cm	int64	Source column.	152
rp10_female_u5_30cm	int64	Source column.	73
rp10_elderly_30cm	int64	Source column.	51
rp10_pop_u15_30cm	int64	Source column.	460
rp10_female_u15_30cm	int64	Source column.	226
rp10_wra_pop_30cm	int64	Source column.	225
rp10_dependency_ratio_30cm	float64	Source column.	77.68
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.	743
rp50_children_u5_30cm	int64	Source column.	206
rp50_female_u5_30cm	int64	Source column.	100
rp50_elderly_30cm	int64	Source column.	69
rp50_pop_u15_30cm	int64	Source column.	625
rp50_female_u15_30cm	int64	Source column.	307
rp50_wra_pop_30cm	int64	Source column.	306
rp50_dependency_ratio_30cm	float64	Source column.	77.68
rp50_education_30cm_pct	int64	Source column.	0
rp50_education_30cm_count	int64	Source column.	0
rp50_hospitals_30cm_pct	int64	Source column.	0
rp50_hospitals_30cm_count	int64	Source column.	0
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.	796
rp100_children_u5_30cm	int64	Source column.	221
rp100_female_u5_30cm	int64	Source column.	107
rp100_elderly_30cm	int64	Source column.	74
rp100_pop_u15_30cm	int64	Source column.	670
rp100_female_u15_30cm	int64	Source column.	329
rp100_wra_pop_30cm	int64	Source column.	328
rp100_dependency_ratio_30cm	float64	Source column.	77.68
rp100_education_30cm_pct	int64	Source column.	0
rp100_education_30cm_count	int64	Source column.	0
rp100_hospitals_30cm_pct	int64	Source column.	0
rp100_hospitals_30cm_count	int64	Source column.	0
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.	1072
rp500_children_u5_30cm	int64	Source column.	297
rp500_female_u5_30cm	int64	Source column.	144
rp500_elderly_30cm	int64	Source column.	100
rp500_pop_u15_30cm	int64	Source column.	902
rp500_female_u15_30cm	int64	Source column.	443
rp500_wra_pop_30cm	int64	Source column.	441
rp500_dependency_ratio_30cm	float64	Source column.	77.68
rp500_education_30cm_pct	int64	Source column.	0
rp500_education_30cm_count	int64	Source column.	0
rp500_hospitals_30cm_pct	int64	Source column.	0
rp500_hospitals_30cm_count	int64	Source column.	0
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.	Namibia - Risk Assessment Indicators
source_resource	category	Source resource title.	NAM_ADM2_flood_exposure.csv
source_package_id	category	CKAN package UUID.	02f35a85-fa7f-4d6b-9239-a76a09afc796
source_resource_id	category	CKAN resource UUID.	8aeb4da1-8a55-4b44-b615-31d53d28c176
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-namibia-namibia-risk-assessment-indicators-11b1e88e")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

sample_country = df[df["country_iso3"] == "NAM"]

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_namibia_namibia_risk_assessment_indicators_11b1e88e_2026,
  title        = {Namibia - Risk Assessment Indicators | Africa (Namibia official open data)},
  author       = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/namibia---risk-assessment-indicators},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-namibia-namibia-risk-assessment-indicators-11b1e88e}}
}

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

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Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/nam/NAM_ADM2_flood_exposure.csv