electricsheepafrica/africa-south-sudan-satellite-detected-water-extents-between-17-and-21-january-dfbb2f63

Satellite Detected Water Extents Between 17 and 21 January | Africa (United Nations Satellite Centre (UNOSAT))

244 rows - 1 Africa country/area - 2023 - source table - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 244 rows from United Nations Satellite Centre (UNOSAT), covering Satellite Detected Water Extents Between 17 and 21 January. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Climate and environment datasets help analysts study exposure, resource conditions, environmental pressure, and climate-related trends.

Source-provided context: Excel file

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: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3 where available plus source-specific keys.

Coverage

Dimension	Value
Rows	244
Countries/areas	1
First period	2023
Last period	2023
Indicators	0
Columns	28
Source format	XLSX

Geographic Coverage

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

Area	Rows	First year	Last year	Name
SSD	244	2023	2023	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.	2d15fac3-9cda-40b3-aa1b-19fb9db97360:admin-1-17-21jan2023:0
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
source_sheet	string	Source column from the original resource.	Admin_1_17-21jan2023
year	int64	Observation year.	2023
province	string	Source column from the original resource.	Central Equatoria
population_in_cloud_free_areas_17_21_january_2023	double	Source column from the original resource.	1252832.642111
cloud_free_analyzed_area_17_21_january_2023_km2	double	Source column from the original resource.	43088.97
population_potentially_exposed_to_maximum_flood_water_ex	double	Source column from the original resource.	17700.0
maximum_flood_water_extent_17_21_january_2023_km2	double	Source column from the original resource.	1106.0
water_extent_variation_between_17_21_january_12_16_janua	double	Source column from the original resource.	249.0
source_period_start_year	int64	Start year inferred from source metadata.	2023
source_period_end_year	int64	End year inferred from source metadata.	2023
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2023
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	United Nations Satellite Centre (UNOSAT)
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Satellite detected water extents between 17 and 21 January 2023 over ...
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	UNOSAT_Population_Exposure_FL20220424SSD_17Jan_21Jan2023_SouthSudan_W...
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	7bc14e20-2b05-4c83-87a9-ad2ffd595f19
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	2d15fac3-9cda-40b3-aa1b-19fb9db97360
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://unosat.org/static/unosat_filesystem/3473/UNOSAT_Population_Ex...
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
province_district	string	Source column from the original resource.	``
population_in_cloud_free_areas_12_16_january_2023	double	Source column from the original resource.	``
cloud_free_analyzed_area_12_16_january_2023_km2	double	Source column from the original resource.	``
maximum_flood_water_extent_12_16_january_2023_km2	double	Source column from the original resource.	``
date	string	Observation date.	``
population_potentially_exposed	double	Source column from the original resource.	``

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-satellite-detected-water-extents-between-17-and-21-january-dfbb2f63")
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 "year" in df.columns:
    trend = df.sort_values("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: 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: United Nations Satellite Centre (UNOSAT)
• Publisher: United Nations Satellite Centre (UNOSAT)
• Portal: https://data.humdata.org
• Resource: UNOSAT_Population_Exposure_FL20220424SSD_17Jan_21Jan2023_SouthSudan_Week9.xlsx
• License: CC BY-SA 4.0
• Retrieved/generated: 2026-08-10T19:43:54Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-satellite-detected-water-extents-between-17-and-21-january-dfbb2f63

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

• Analyze seasonal or annual patterns
• Join with agriculture or health data
• Map geographic exposure
• 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_satellite_detected_water_extents_between_17_and_21_january_df_2023,
  title        = {Satellite Detected Water Extents Between 17 and 21 January | Africa (United Nations Satellite Centre (UNOSAT))},
  author       = {United Nations Satellite Centre (UNOSAT)},
  year         = {2023},
  url          = {https://data.humdata.org/dataset/satellite-detected-water-extents-between-17-and-21-january-2023-over-south-sudan},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-satellite-detected-water-extents-between-17-and-21-january-dfbb2f63}}
}

License

Released under CC BY-SA 4.0.

Original data is published by United Nations Satellite Centre (UNOSAT). 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/satellite-detected-water-extents-between-17-and-21-january-2023-over-south-sudan