electricsheepafrica/africa-world-bank-urban-development-indicators-for-somalia

---
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
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- development
- hxl
- indicators
- som
pretty_name: "Somalia - Urban Development"
dataset_info:
splits:
- name: train
num_examples: 502
- name: test
num_examples: 125
---

# Somalia - Urban Development

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-somalia) · **License:** `cc-by` · **Updated:** 2025-08-28

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-somalia) on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-08-28. Geographic scope: **SOM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 628 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 502 rows |
| **Test split** | 125 rows |
| **Geographic scope** | SOM |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2025-08-28 |

---

## Variables

**Geographic** — `country_name` (Somalia, #country+name), `country_iso3` (SOM, #country+code), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -4.4311–9223050.0).

**Identifier / Metadata** — `indicator_name` (Urban population (% of total population), Population in the largest city (% of urban population), Urban population), `indicator_code` (SP.URB.TOTL.IN.ZS, EN.URB.LCTY.UR.ZS, SP.URB.TOTL), `esa_source` (HDX), `esa_processed` (2026-04-08).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-urban-development-indicators-for-somalia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Somalia, #country+name |
| `country_iso3` | object | 0.0% | SOM, #country+code |
| `year` | float64 | 0.2% | 1960.0 – 2024.0 (mean 1994.807) |
| `indicator_name` | object | 0.0% | Urban population (% of total population), Population in the largest city (% of urban population), Urban population |
| `indicator_code` | object | 0.0% | SP.URB.TOTL.IN.ZS, EN.URB.LCTY.UR.ZS, SP.URB.TOTL |
| `value` | float64 | 0.2% | -4.4311 – 9223050.0 (mean 528204.0597) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1994.807 | 1997.0 |
| `value` | -4.4311 | 9223050.0 | 528204.0597 | 29.9551 |

---

## 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`. 2 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 World Bank Group 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/world-bank-urban-development-indicators-for-somalia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_urban_development_indicators_for_somalia,
title = {Somalia - Urban Development},
author = {World Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-somalia},
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.*