README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- tabular-regression
- time-series-forecasting
multilinguality: monolingual
size_categories:
- 1M<n<10M
tags:
- tabular
- csv
- africa
- congo
- official-statistics
- open-data
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-00000-of-00001.parquet
pretty_name: "Movement Distribution | Africa (Congo official open data)"
---

# Movement Distribution | Africa (Congo official open data)

1,312,486 rows - 1 Africa country - 2026 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

![rows](https://img.shields.io/badge/rows-1312486-blue)
![countries](https://img.shields.io/badge/countries-1-green)
![years](https://img.shields.io/badge/years-2026-orange)
![indicators](https://img.shields.io/badge/indicators-2-purple)
![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)

## TL;DR

This dataset packages one official `CSV` resource from **Congo** 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:** [Movement Distribution](https://data.humdata.org/dataset/movement-distribution)
- **Publisher:** AI for Good at Meta
- **Resource:** [Movement Distribution 2026_June_19-2026_June_23.csv](https://data.humdata.org/dataset/32167ba5-ef67-4254-8eaf-04cdb8b90c1d/resource/c89a5d03-dfcb-4524-96f6-6843c9d4f140/download/1922039342088483_combined_part2.csv)
- **Format:** `CSV`
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Packaging mode:** `indicator_long`

## Geographic coverage

1 Africa country:

| Country | Rows | First year | Last year | Name |
|---------|-----:|-----------:|----------:|------|
| `COG` | 1,312,486 | 2026 | 2026 | `Congo` |

## Indicators or Resource Contents

- `movement-distribution-polygon-level-b272dfd5` - Movement Distribution - polygon level
- `movement-distribution-distance-category-ping-fraction-6f5ac9d1` - Movement Distribution - distance category ping fraction

## Schema

| Column | Type | Description | Example |
|--------|------|-------------|---------|
| `indicator_id` | `string` | Stable indicator identifier. | `movement-distribution-polygon-level-b272dfd5` |
| `indicator_name` | `string` | Human-readable indicator name. | `Movement Distribution - polygon level` |
| `country_iso3` | `string` | ISO3 country code. | `COG` |
| `country_name` | `string` | Country name. | `Congo` |
| `year` | `Int64` | Observation year. | `2026` |
| `value` | `float64` | Numeric observation value. | `2.0` |
| `unit` | `string` | Measurement unit, when available. | `source_units_unspecified` |
| `dimension_gadm_id` | `string` | Source dimension. | `FRA.8.8_1` |
| `dimension_gadm_name` | `string` | Source dimension. | `Yvelines` |
| `dimension_country` | `string` | Source dimension. | `FRA` |
| `dimension_home_to_ping_distance_category` | `string` | Source dimension. | `(0, 10)` |
| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2019` |
| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2026` |
| `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2019-2026` |
| `source_provider` | `category` | Publishing organization. | `AI for Good at Meta` |
| `source_dataset` | `category` | Source package title. | `Movement Distribution` |
| `source_resource` | `category` | Source resource title. | `Movement Distribution 2026_June_19-2026_June_23.csv` |
| `source_package_id` | `category` | CKAN package UUID. | `32167ba5-ef67-4254-8eaf-04cdb8b90c1d` |
| `source_resource_id` | `category` | CKAN resource UUID. | `c89a5d03-dfcb-4524-96f6-6843c9d4f140` |
| `source_url` | `category` | Original source resource URL. | `https://data.humdata.org/dataset/32167ba5-ef67-4254-8eaf-04cdb8b90c1d/re` |
| `license_id` | `category` | Source license identifier. | `cc-by` |
| `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-16T12:36:20Z` |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-congo-movement-distribution-89b4af37")
df = ds["train"].to_pandas()
print(df.head())
```

### Filter to one country

```python
sample_country = df[df["country_iso3"] == "COG"]
```

### Work with indicators

```python
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

```bibtex
@misc{electric_sheep_africa_africa_congo_movement_distribution_89b4af37_2026,
  title        = {Movement Distribution | Africa (Congo official open data)},
  author       = {AI for Good at Meta},
  year         = {2026},
  url          = {https://data.humdata.org/dataset/movement-distribution},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-congo-movement-distribution-89b4af37}}
}
```

## License

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

Original data (c) AI for Good at Meta. 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](https://huggingface.co/electricsheepafrica)

---

Provenance: ingested 2026-08-16 via the Electric Sheep pipeline. Source URL:
https://data.humdata.org/dataset/32167ba5-ef67-4254-8eaf-04cdb8b90c1d/resource/c89a5d03-dfcb-4524-96f6-6843c9d4f140/download/1922039342088483_combined_part2.csv