electricsheepafrica/africa-unhabitat-gn-indicators

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- baseline-population
- education
- health
- hxl
- indicators
- transportation
- gin
pretty_name: "Guinea - Demographic, Health, Education and Transport indicators"
dataset_info:
splits:
- name: train
num_examples: 323
- name: test
num_examples: 80
---

# Guinea - Demographic, Health, Education and Transport indicators

**Publisher:** United Nations Human Settlements Programmes, Data and Analytics Section · **Source:** [HDX](https://data.humdata.org/dataset/unhabitat-gn-indicators) · **License:** `cc-by-igo` · **Updated:** 2024-03-28

---

## Abstract

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-03-28. Geographic scope: **GIN**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 404 |
| **Columns** | 13 (5 numeric, 8 categorical, 0 datetime) |
| **Train split** | 323 rows |
| **Test split** | 80 rows |
| **Geographic scope** | GIN |
| **Publisher** | United Nations Human Settlements Programmes, Data and Analytics Section |
| **HDX last updated** | 2024-03-28 |

---

## Variables

**Geographic** — `category` (Population, Slum dwellers, Health), `indicator_friendly` (Average annual rate of change of population – Total, Average annual rate of change of population – Urban, Urban population – Countries), `type_data` (p, 1000, n), `latitude` (range 9.5315–11.0), `longitude` (range -13.6802–-10.0) and 3 others.

**Outcome / Measurement** — `value` (range 0.1–63525.0).

**Identifier / Metadata** — `name` (Guinea, Conakry, #country+name), `esa_source` (HDX), `esa_processed` (2026-04-09).

**Other** — `indicator` (avg_annual_rate_change_percentage_total, rural_population, urban_population_countries).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-unhabitat-gn-indicators")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `category` | object | 0.0% | Population, Slum dwellers, Health |
| `indicator` | object | 0.0% | avg_annual_rate_change_percentage_total, rural_population, urban_population_countries |
| `indicator_friendly` | object | 0.0% | Average annual rate of change of population – Total, Average annual rate of change of population – Urban, Urban population – Countries |
| `type_data` | object | 0.0% | p, 1000, n |
| `latitude` | float64 | 0.2% | 9.5315 – 11.0 (mean 10.9016) |
| `longitude` | float64 | 0.2% | -13.6802 – -10.0 (mean -10.2466) |
| `region_id` | float64 | 0.2% | 289.0 – 289.0 (mean 289.0) |
| `country_id` | object | 0.0% | GN, #country+code+v_iso2 |
| `name` | object | 0.0% | Guinea, Conakry, #country+name |
| `year` | float64 | 0.2% | 1950.0 – 2050.0 (mean 1999.9305) |
| `value` | float64 | 0.2% | 0.1 – 63525.0 (mean 1467.2092) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-09 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `latitude` | 9.5315 | 11.0 | 10.9016 | 11.0 |
| `longitude` | -13.6802 | -10.0 | -10.2466 | -10.0 |
| `region_id` | 289.0 | 289.0 | 289.0 | 289.0 |
| `year` | 1950.0 | 2050.0 | 1999.9305 | 2000.0 |
| `value` | 0.1 | 63525.0 | 1467.2092 | 36.1 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 5 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from United Nations Human Settlements Programmes, Data and Analytics Section and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/unhabitat-gn-indicators) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_unhabitat_gn_indicators,
title = {Guinea - Demographic, Health, Education and Transport indicators},
author = {United Nations Human Settlements Programmes, Data and Analytics Section},
year = {2024},
url = {https://data.humdata.org/dataset/unhabitat-gn-indicators},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*