electricsheepafrica/africa-south-sudan-movement-distribution-bb5bd31b

Movement Distribution | Africa (AI for Good at Meta)

1,312,486 rows - 1 Africa country/area - 2026 - 2 indicators - Engineered by Electric Sheep Africa

TL;DR

This dataset contains 1,312,486 rows from AI for Good at Meta, covering Movement Distribution. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.

Source-provided context: 27 June 2026- 01 July 2026

How To Read This Dataset

• One row means: one indicator observation for one geography, time period, and optional source dimensions.
• Primary geography column: country_iso3.
• Best time column: year.
• Time coverage basis: year.
• Recommended join keys: country_iso3, year, indicator_id.

Coverage

Dimension	Value
Rows	1,312,486
Countries/areas	1
First period	2026
Last period	2026
Indicators	2
Columns	22
Source format	CSV

Geographic Coverage

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

Area	Rows	First year	Last year	Name
SSD	1,312,486	2026	2026	South Sudan

Indicators, Variables, Or Resource Contents

• movement-distribution-polygon-level-21714dde - Movement Distribution - polygon level(source_units_unspecified)
• movement-distribution-distance-category-ping-fraction-2a36d4d3 - Movement Distribution - distance category ping fraction(source_units_unspecified)

Schema

Column	Type	Description	Example
indicator_id	string	Stable source or Electric Sheep Africa indicator identifier.	movement-distribution-polygon-level-21714dde
indicator_name	string	Human-readable indicator name.	Movement Distribution - polygon level
country_iso3	string	ISO3 country or area code.	SSD
country_name	string	Country or area name.	South Sudan
year	int64	Observation year.	2026
value	double	Numeric observation value.	2.0
unit	string	Measurement unit, when supplied by the source.	source_units_unspecified
dimension_gadm_id	string	Source dimension retained during long-form normalization.	ROU.27.62_1
dimension_gadm_name	string	Source dimension retained during long-form normalization.	Targu Lapus
dimension_country	string	Source dimension retained during long-form normalization.	ROU
dimension_home_to_ping_distance_category	string	Source dimension retained during long-form normalization.	[10, 100)
source_period_start_year	int64	Start year inferred from source metadata.	2001
source_period_end_year	int64	End year inferred from source metadata.	2027
source_period_label	dictionary<values=string, indices=int8, ordered=0>	Source column from the original resource.	2001-2027
source_provider	dictionary<values=string, indices=int8, ordered=0>	Publishing organization.	AI for Good at Meta
source_dataset	dictionary<values=string, indices=int8, ordered=0>	Source dataset or package title.	Movement Distribution
source_resource	dictionary<values=string, indices=int8, ordered=0>	Source resource title, table name, or file name.	Movement Distribution 2026_June_27-2026_July_01.csv
source_package_id	dictionary<values=string, indices=int8, ordered=0>	Source package identifier.	32167ba5-ef67-4254-8eaf-04cdb8b90c1d
source_resource_id	dictionary<values=string, indices=int8, ordered=0>	Source resource identifier.	35729797-b783-4ace-9c7e-69aa8eec52ae
source_url	dictionary<values=string, indices=int8, ordered=0>	Original source URL or download URL.	https://data.humdata.org/dataset/32167ba5-ef67-4254-8eaf-04cdb8b90c1d...
license_id	dictionary<values=string, indices=int8, ordered=0>	Source license identifier.	cc-by
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-movement-distribution-bb5bd31b")
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: AI for Good at Meta
• Publisher: AI for Good at Meta
• Portal: https://data.humdata.org
• Resource: Movement Distribution 2026_June_27-2026_July_01.csv
• License: CC BY 4.0
• Retrieved/generated: 2026-08-10T18:52:02Z
• Hugging Face repo: electricsheepafrica/africa-south-sudan-movement-distribution-bb5bd31b

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

• Profile the distribution of values
• Compare categories or geographies
• Join with complementary public datasets
• Build time-series views and period-over-period comparisons
• Pivot to geography x period or indicator x period matrices
• Check missingness before modeling
• Use country_iso3 as the safest geography join key when present

Citation

@misc{electric_sheep_africa_africa_south_sudan_movement_distribution_bb5bd31b_2026,
  title        = {Movement Distribution | Africa (AI for Good at Meta)},
  author       = {AI for Good at Meta},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/movement-distribution},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-south-sudan-movement-distribution-bb5bd31b}}
}

License

Released under CC BY 4.0.

Original data is published by AI for Good at Meta. 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/movement-distribution