electricsheepafrica/africa-world-bank-climate-change-indicators-for-south-sudan

---
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
- climate-weather
- indicators
- ssd
pretty_name: "South Sudan - Climate Change"
dataset_info:
splits:
- name: train
num_examples: 571
- name: test
num_examples: 142
---

# South Sudan - Climate Change

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-climate-change-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/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan) on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

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** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 714 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 571 rows |
| **Test split** | 142 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 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -5.4159–11943408.0).

**Identifier / Metadata** — `indicator_name` (Urban population (% of total population), Urban population, Population, total), `indicator_code` (SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL), `esa_source` (HDX), `esa_processed` (2026-04-10).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-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 – 2024.0 (mean 2002.6625) |
| `indicator_name` | object | 0.0% | Urban population (% of total population), Urban population, Population, total |
| `indicator_code` | object | 0.0% | SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL |
| `value` | float64 | 0.0% | -5.4159 – 11943408.0 (mean 671729.0345) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-10 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 2002.6625 | 2012.0 |
| `value` | -5.4159 | 11943408.0 | 671729.0345 | 15.5 |

---

## 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`. 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-climate-change-indicators-for-south-sudan) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_climate_change_indicators_for_south_sudan,
title = {South Sudan - Climate Change},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-climate-change-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

在气候变化对发展中国家构成严峻挑战的背景下，该数据集由世界银行集团通过其数据门户系统性地收集与整合，聚焦于南苏丹的气候变化指标。原始数据经由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，通过CKAN API下载后，执行了列名标准化与缺失值统一标记为NaN的清洗流程。随后，数据集被划分为训练集与测试集，采用80/20的比例并以固定随机种子确保可复现性，最终以Snappy压缩的Parquet格式存储，为机器学习应用提供了结构化的基础。

使用方法

在气候科学与可持续发展研究领域，该数据集可直接用于训练机器学习模型，以支持回归或分类任务，例如预测气候变化指标的趋势或评估其影响因素。用户可通过Hugging Face的datasets库便捷加载，利用Python环境将数据转换为Pandas DataFrame进行探索性分析或模型构建。数据集已预分割为训练与测试部分，便于快速投入算法开发与验证，同时建议参考原始发布方的方法论说明，以确保分析结果符合实际背景与数据局限性。

背景与挑战

背景概述

世界银行集团于2026年发布的南苏丹气候变化指标数据集，聚焦于发展中国家在气候危机下面临的严峻挑战。该数据集由Electric Sheep Africa团队进行机器学习友好型重构，旨在提供国家层面的聚合数据，涵盖气候系统、气候影响暴露度、韧性、温室气体排放及能源使用等多维度指标。其核心研究问题在于量化气候变化对南苏丹等脆弱地区在农业、水资源、公共卫生及减贫进程中的具体影响，为政策制定与跨领域研究提供实证基础，对全球气候治理与发展经济学领域具有重要参考价值。

当前挑战

该数据集致力于应对气候变化影响评估与可持续发展预测中的复杂挑战，尤其在数据稀疏且社会经济环境脆弱的地区。其领域问题挑战在于如何从有限的历史观测数据中准确建模气候变量与人口、城市化等社会指标的动态关联，并克服指标间定义不一致与时间跨度不连续带来的分析困难。构建过程中的挑战则体现为原始数据可能存在报告偏差与缺失值问题，自动化清洗流程难以修正源数据的方法论局限，且国家层面聚合可能掩盖区域异质性，影响模型在微观层面的泛化能力。

常用场景

经典使用场景

在气候变化的全球背景下，该数据集为研究南苏丹的气候变化指标提供了结构化数据基础。其经典使用场景聚焦于时间序列分析与预测建模，学者们常利用数据集中的年份、人口统计及城市化比例等变量，构建回归模型以预测未来气候变化对人口分布的影响。通过整合世界银行的气候系统、温室气体排放等指标，研究人员能够深入探索气候变量与社会经济因素之间的动态关联，为区域气候适应策略提供量化依据。

解决学术问题

该数据集有效解决了气候变化研究中数据稀缺与标准化不足的学术难题。通过提供南苏丹自1960年至2024年的国家层面聚合数据，它支持了关于气候变化脆弱性、适应能力及减缓策略的实证分析。数据集涵盖了城市化进程、人口总量等关键指标，使研究者能够评估气候风险对发展中国家社会经济结构的长期影响，从而填补了区域气候数据在机器学习应用中的空白，推动了跨学科气候研究的可重复性与可比性。

实际应用

在实际应用层面，该数据集被广泛用于人道主义援助与政策制定领域。政府部门和非营利组织可依据数据集中的气候指标，设计针对南苏丹的粮食安全干预方案和城市发展规划。例如，通过分析城市化比例与人口变化趋势，决策者能够预测气候灾害对脆弱社区的影响，优化资源分配以增强社区韧性。此外，能源与环境机构利用这些数据评估温室气体排放模式，为制定国家气候行动计划提供实证支持。

数据集最近研究

最新研究方向

在气候变化与脆弱国家发展交叉领域，该数据集聚焦南苏丹的气候变化指标，为前沿研究提供了关键数据支撑。当前研究热点集中于利用机器学习模型预测气候冲击对农业、水资源及人口迁移的长期影响，结合城市化进程与人口动态指标，探索气候适应策略的优化路径。随着全球气候治理议程的深化，此类细粒度国别数据在评估气候风险、制定人道主义干预方案方面具有显著意义，推动了数据驱动的气候韧性研究在非洲冲突后地区的应用。