electricsheepafrica/africa-world-bank-combined-indicators-for-south-sudan

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- agriculture-livestock
- aid-effectiveness
- climate-weather
- development
- economics
- education
- energy
- environment
- ssd
pretty_name: "South Sudan - Economic, Social, Environmental, Health, Education, Development and Energy"
dataset_info:
splits:
- name: train
num_examples: 14671
- name: test
num_examples: 3667
---

# South Sudan - Economic, Social, Environmental, Health, Education, Development and Energy

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/) covering the following topics which also exist as individual datasets on HDX: [Agriculture and Rural Development](https://data.humdata.org/dataset/world-bank-agriculture-and-rural-development-indicators-for-south-sudan), [Aid Effectiveness](https://data.humdata.org/dataset/world-bank-aid-effectiveness-indicators-for-south-sudan), [Economy and Growth](https://data.humdata.org/dataset/world-bank-economy-and-growth-indicators-for-south-sudan), [Education](https://data.humdata.org/dataset/world-bank-education-indicators-for-south-sudan), [Energy and Mining](https://data.humdata.org/dataset/world-bank-energy-and-mining-indicators-for-south-sudan), [Environment](https://data.humdata.org/dataset/world-bank-environment-indicators-for-south-sudan), [Financial Sector](https://data.humdata.org/dataset/world-bank-financial-sector-indicators-for-south-sudan), [Health](https://data.humdata.org/dataset/world-bank-health-indicators-for-south-sudan), [Infrastructure](https://data.humdata.org/dataset/world-bank-infrastructure-indicators-for-south-sudan), [Social Protection and Labor](https://data.humdata.org/dataset/world-bank-social-protection-and-labor-indicators-for-south-sudan), [Poverty](https://data.humdata.org/dataset/world-bank-poverty-indicators-for-south-sudan), [Private Sector](https://data.humdata.org/dataset/world-bank-private-sector-indicators-for-south-sudan), [Public Sector](https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-south-sudan), [Science and Technology](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-south-sudan), [Social Development](https://data.humdata.org/dataset/world-bank-social-development-indicators-for-south-sudan), [Urban Development](https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-south-sudan), [Gender](https://data.humdata.org/dataset/world-bank-gender-indicators-for-south-sudan), [Climate Change](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-south-sudan), [External Debt](https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-south-sudan), [Trade](https://data.humdata.org/dataset/world-bank-trade-indicators-for-south-sudan).

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **SSD**.

*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)** | 18,339 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 14,671 rows |
| **Test split** | 3,667 rows |
| **Geographic scope** | SSD |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (South Sudan), `country_iso3` (SSD), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -1078293666537.33–2504703858099.18).

**Identifier / Metadata** — `indicator_name` (Population in the largest city (% of urban population), Population in largest city, Net migration), `indicator_code` (EN.URB.LCTY.UR.ZS, EN.URB.LCTY, SM.POP.NETM), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-combined-indicators-for-south-sudan")
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% | South Sudan |
| `country_iso3` | object | 0.0% | SSD |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 2004.0339) |
| `indicator_name` | object | 0.0% | Population in the largest city (% of urban population), Population in largest city, Net migration |
| `indicator_code` | object | 0.0% | EN.URB.LCTY.UR.ZS, EN.URB.LCTY, SM.POP.NETM |
| `value` | float64 | 0.0% | -1078293666537.33 – 2504703858099.18 (mean 634628754.2693) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 2004.0339 | 2010.0 |
| `value` | -1078293666537.33 | 2504703858099.18 | 634628754.2693 | 34.3598 |

---

## 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`. 6,406 exact duplicate rows were removed. 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-combined-indicators-for-south-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_combined_indicators_for_south_sudan,
title = {South Sudan - Economic, Social, Environmental, Health, Education, Development and Energy},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan},
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.*

搜集汇总

数据集介绍

构建方式

在非洲发展研究领域，数据集的构建往往依赖于权威国际机构的系统性数据收集。本数据集源自世界银行集团的数据门户，涵盖了南苏丹在农业、经济、教育、能源、环境、健康、社会发展等二十余个关键领域的国家层面聚合指标。原始数据通过人道主义数据交换平台获取，并由Electric Sheep Africa团队进行专业化处理，包括从CKAN API下载、转换为Parquet格式、统一缺失值标记以及去除重复记录，最终按80:20的比例划分为训练集与测试集，确保了数据的机器学习可用性。

特点

该数据集的特点体现在其多维度的覆盖范围与高度的结构性。作为国家层面的聚合数据，它囊括了从1960年至2025年间南苏丹在经济、社会、环境、健康、教育及能源等广泛领域的量化指标，共计超过1.8万条记录。数据集经过精心清洗，字段统一为蛇形命名法，缺失值标准化处理，且完全不含空值，保证了数据的完整性与一致性。其表格化结构包含八个明确列，涵盖地理标识、年份、指标名称与数值，为跨领域比较分析与时序研究提供了坚实基础。

使用方法

在应用层面，该数据集适用于表格分类等机器学习任务，尤其适合用于发展经济学、公共政策分析与区域研究。研究人员可通过Hugging Face的datasets库直接加载数据，便捷地转换为Pandas DataFrame进行探索性分析。数据集已预分为训练集与测试集，支持用户直接构建预测模型，例如基于历史指标预测发展趋势或评估政策干预效果。使用时应参考原始发布方的方法说明，并注意数据源于世界银行的官方统计，可能存在定义不一致或报告偏差等固有局限。

背景与挑战

背景概述

在全球化与可持续发展议程的推动下，对非洲国家进行多维度社会经济监测成为国际发展研究的关键课题。由世界银行集团创建并于2026年通过人道主义数据交换平台发布的南苏丹综合指标数据集，汇集了该国自1960年至2025年间在农业、经济、教育、能源、环境、健康等二十余个关键领域的国家层面聚合数据。该数据集由Electric Sheep Africa团队进行机器学习友好型重构，旨在为研究人员和政策制定者提供一个全面、标准化的分析基础，以支持对南苏丹发展轨迹的深入评估和跨领域政策模拟。

当前挑战

该数据集致力于解决对脆弱国家进行综合发展评估时所面临的多维度指标整合与时间序列分析的挑战，其核心在于如何从高度异质且动态变化的领域变量中提取稳健的发展模式。在构建过程中，数据清洗面临原始数据中缺失值标记不统一、重复记录以及指标定义随时间演变可能带来的不一致性等难题。尽管经过自动化处理，但数据集仍受限于原始数据收集可能存在的报告偏差和方法学差异，这要求使用者在进行因果推断或纵向比较时需谨慎考虑潜在的数据质量限制。

常用场景

经典使用场景

在非洲发展研究领域，该数据集为南苏丹的经济、社会与环境指标提供了全面的时间序列数据。研究人员常利用这些结构化数据，通过机器学习模型预测关键发展指标的趋势，例如城市化进程或能源消耗变化。数据集覆盖了从1960年至2025年的长期观测，使得时间序列分析与回归建模成为其经典应用场景，为理解国家发展轨迹提供了量化基础。

实际应用

在实际应用层面，该数据集被国际组织与地方政府用于制定针对性发展策略。例如，援助机构可依据健康与教育指标优化资源分配，能源部门能基于历史数据规划电网扩展。这些应用不仅提升了决策的科学性，还支持了联合国可持续发展目标（SDGs）在南苏丹的本土化监测与评估工作。

衍生相关工作

基于该数据集衍生的经典工作包括南苏丹脆弱性指数构建与发展预测模型。研究团队常将其与卫星遥感数据或调查数据融合，以创建高分辨率贫困地图。此外，该数据集也催生了关于冲突后国家恢复力的计量经济学研究，为比较发展研究提供了重要的案例基准。