electricsheepafrica/africa-south-sudan-south-sudan-risk-assessment-indicators-3644e0b8

South Sudan Risk Assessment Indicators | Africa (HeiGIT (Heidelberg Institute for Geoinformation Technology))

79 rows - 1 Africa country/area - detected - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 79 rows from HeiGIT (Heidelberg Institute for Geoinformation Technology), covering South Sudan Risk Assessment Indicators. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Health datasets help analysts monitor disease burden, service delivery, population health outcomes, and public-health program performance.

Source-provided context: SSD_ADM2_flood_exposure.csv - Risk assessment indicator for South Sudan

How To Read This Dataset

• One row means: one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
• Primary geography column: country_iso3.
• Best time column: source_period_start_year.
• Time coverage basis: source_period_start_year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	79
Countries/areas	1
First period	detected
Last period	detected
Indicators	0
Columns	64
Source format	CSV

Geographic Coverage

Top areas shown below, sorted by row count when available:

Area	Rows	First year	Last year	Name
SSD	79	detected	detected	South Sudan

Indicators, Variables, Or Resource Contents

• This repo preserves one source tabular resource with its usable columns kept together.

Schema

Column	Type	Description	Example
source_record_id	string	Stable row identifier assigned during Electric Sheep Africa engineering.	4917732e-e8f4-4fcf-b07d-be4d80796b98:0
country_iso3	dictionary<values=string, indices=int8, ordered=0>	ISO3 country or area code.	SSD
country_name	dictionary<values=string, indices=int8, ordered=0>	Country or area name.	South Sudan
adm2_pcode	string	Source column from the original resource.	SS0001
adm_pcode	string	Source column from the original resource.	SS0001
rp10_female_pop_30cm	int64	Source column from the original resource.	0
rp10_children_u5_30cm	int64	Source column from the original resource.	0
rp10_female_u5_30cm	int64	Source column from the original resource.	0
rp10_elderly_30cm	int64	Source column from the original resource.	0
rp10_pop_u15_30cm	int64	Source column from the original resource.	0
rp10_female_u15_30cm	int64	Source column from the original resource.	0
rp10_education_30cm_pct	int64	Source column from the original resource.	100
rp10_education_30cm_count	int64	Source column from the original resource.	7
rp10_hospitals_30cm_pct	int64	Source column from the original resource.	100
rp10_hospitals_30cm_count	int64	Source column from the original resource.	3
rp10_primary_healthcare_30cm_pct	int64	Source column from the original resource.	100
rp10_primary_healthcare_30cm_count	int64	Source column from the original resource.	4
rp50_female_pop_30cm	int64	Source column from the original resource.	0
rp50_children_u5_30cm	int64	Source column from the original resource.	0
rp50_female_u5_30cm	int64	Source column from the original resource.	0
rp50_elderly_30cm	int64	Source column from the original resource.	0
rp50_pop_u15_30cm	int64	Source column from the original resource.	0
rp50_female_u15_30cm	int64	Source column from the original resource.	0
rp50_education_30cm_pct	int64	Source column from the original resource.	100
rp50_education_30cm_count	int64	Source column from the original resource.	7
rp50_hospitals_30cm_pct	int64	Source column from the original resource.	100
rp50_hospitals_30cm_count	int64	Source column from the original resource.	3
rp50_primary_healthcare_30cm_pct	int64	Source column from the original resource.	100
rp50_primary_healthcare_30cm_count	int64	Source column from the original resource.	4
rp100_female_pop_30cm	int64	Source column from the original resource.	0
rp100_children_u5_30cm	int64	Source column from the original resource.	0
rp100_female_u5_30cm	int64	Source column from the original resource.	0
rp100_elderly_30cm	int64	Source column from the original resource.	0
rp100_pop_u15_30cm	int64	Source column from the original resource.	0
rp100_female_u15_30cm	int64	Source column from the original resource.	0
rp100_education_30cm_pct	int64	Source column from the original resource.	100
rp100_education_30cm_count	int64	Source column from the original resource.	7
rp100_hospitals_30cm_pct	int64	Source column from the original resource.	100
rp100_hospitals_30cm_count	int64	Source column from the original resource.	3
rp100_primary_healthcare_30cm_pct	int64	Source column from the original resource.	100
rp100_primary_healthcare_30cm_count	int64	Source column from the original resource.	4
rp500_female_pop_30cm	int64	Source column from the original resource.	0
rp500_children_u5_30cm	int64	Source column from the original resource.	0
rp500_female_u5_30cm	int64	Source column from the original resource.	0
rp500_elderly_30cm	int64	Source column from the original resource.	0
rp500_pop_u15_30cm	int64	Source column from the original resource.	0
rp500_female_u15_30cm	int64	Source column from the original resource.	0
rp500_education_30cm_pct	int64	Source column from the original resource.	100
rp500_education_30cm_count	int64	Source column from the original resource.	7
rp500_hospitals_30cm_pct	int64	Source column from the original resource.	100
rp500_hospitals_30cm_count	int64	Source column from the original resource.	3
rp500_primary_healthcare_30cm_pct	int64	Source column from the original resource.	100
rp500_primary_healthcare_30cm_count	int64	Source column from the original resource.	4
source_period_start_year	int64	Start year inferred from source metadata.	``
source_period_end_year	int64	End year inferred from source metadata.	``
source_period_label	string	Source column from the original resource.	``
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	HeiGIT (Heidelberg Institute for Geoinformation Technology)
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	South Sudan - Risk Assessment Indicators
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	SSD_ADM2_flood_exposure.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	5e58fd14-8e86-41e8-8f04-093cf0874805
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	4917732e-e8f4-4fcf-b07d-be4d80796b98
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inpu...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by-sa
retrieved_at	dictionary<values=string, indices=int8, ordered=0>	UTC source retrieval timestamp from the Electric Sheep Africa pipeline.	2026-08-10T18:18:23Z

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-south-sudan-risk-assessment-indicators-3644e0b8")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "SSD"]

Time-Series Pattern

if "value" in df.columns and "source_period_start_year" in df.columns:
    trend = df.sort_values("source_period_start_year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• Canonical time field: source_period_start_year.
• Missing values are preserved rather than silently imputed.
• Column names are standardized for machine use; source meanings are preserved where known.
• Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• Source: HeiGIT (Heidelberg Institute for Geoinformation Technology)
• Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology)
• Portal: https://data.humdata.org
• Resource: SSD_ADM2_flood_exposure.csv
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-10T19:00:30Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-south-sudan-risk-assessment-indicators-3644e0b8

Transformations Applied

• Converted the source table to Parquet for efficient analytics and ML workflows.
• Added or preserved source provenance columns where available.
• Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• Preserved source-reported values without analytical imputation.

Suggested Analyses

• Compare health outcomes across geographies
• Track changes over time
• Join with population or facility data
• Build time-series views and period-over-period comparisons
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_sudan_south_sudan_risk_assessment_indicators_3644e0b8_2026,
  title        = {South Sudan Risk Assessment Indicators | Africa (HeiGIT (Heidelberg Institute for Geoinformation Technology))},
  author       = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/south-sudan---risk-assessment-indicators},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-south-sudan-risk-assessment-indicators-3644e0b8}}
}

License

Released under CC BY-SA 4.0.

Original data is published by HeiGIT (Heidelberg Institute for Geoinformation Technology). Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

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Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/south-sudan---risk-assessment-indicators