electricsheepafrica/africa-world-bank-climate-change-indicators-for-ghana

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- climate-weather
- indicators
- gha
pretty_name: "Ghana - Climate Change"
dataset_info:
splits:
- name: train
num_examples: 1347
- name: test
num_examples: 336
---

# Ghana - Climate Change

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-ghana) · **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-ghana) 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: **GHA**.

*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)** | 1,684 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,347 rows |
| **Test split** | 336 rows |
| **Geographic scope** | GHA |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Ghana), `country_iso3` (GHA), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -0.6604–169590000.0).

**Identifier / Metadata** — `indicator_name` (Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population), `indicator_code` (EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.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-ghana")
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% | Ghana |
| `country_iso3` | object | 0.0% | GHA |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1998.0071) |
| `indicator_name` | object | 0.0% | Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population |
| `indicator_code` | object | 0.0% | EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.TOTL |
| `value` | float64 | 0.0% | -0.6604 – 169590000.0 (mean 1491422.8695) |
| `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 | 1998.0071 | 2000.0 |
| `value` | -0.6604 | 169590000.0 | 1491422.8695 | 38.1628 |

---

## 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-ghana) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_climate_change_indicators_for_ghana,
title = {Ghana - Climate Change},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-ghana},
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

在气候变化研究领域，加纳气候数据集通过系统化流程构建而成。原始数据源自世界银行集团的数据门户，经由人道主义数据交换平台获取，涵盖了从1960年至2025年的国家层面聚合指标。Electric Sheep Africa团队通过CKAN API下载原始资料，执行了标准化清洗操作，包括统一缺失值标记为NaN、将列名转换为蛇形命名法，并采用固定随机种子将数据按80/20比例划分为训练集与测试集，最终存储为Snappy压缩的Parquet格式，确保了数据的机器学习可用性。

特点

该数据集聚焦于加纳的气候变化指标，具备鲜明的结构化特征。其包含1684条观测记录，涵盖8个变量，其中2个为数值型、6个为分类型，无缺失值保证了数据的完整性。指标内容涉及城市人口比例、温室气体排放及能源使用等多维度气候相关主题，地理范围严格限定于加纳，时间跨度长达65年，为纵向分析提供了坚实基础。数据集经过精心划分，训练集与测试集分别包含1347和336条样本，支持分类与回归任务，适用于气候变化影响评估与预测建模。

使用方法

利用该数据集进行机器学习研究时，用户可通过Hugging Face的datasets库便捷加载。调用load_dataset函数并指定数据集名称，即可获取已分割的训练与测试部分。数据以Pandas DataFrame形式呈现，便于进行探索性分析与特征工程。研究者可依据indicator_code选择特定气候指标，结合年份与数值变量构建时间序列模型或跨指标关联分析。鉴于数据已预处理为标准化格式，用户可直接聚焦于模型开发，同时参考原始世界银行方法论说明以确保分析严谨性。

背景与挑战

背景概述

在气候变化研究领域，加纳作为西非重要的发展中国家，其气候脆弱性与适应能力备受国际关注。世界银行集团于2026年发布了加纳气候变化指标数据集，该数据集由Electric Sheep Africa团队重新整理为机器学习可用格式，聚焦于气候系统、温室气体排放、能源使用及气候韧性等核心维度。通过整合1960年至2025年的国家层面聚合数据，该数据集旨在量化气候变化对农业、水资源及城市发展的影响，为政策制定者与研究人员提供了评估气候风险与可持续发展进程的关键实证基础。

当前挑战

该数据集致力于解决气候变化影响评估与预测中的复杂挑战，特别是如何从多维度指标中提取可解释模式以支持气候适应策略。构建过程中面临原始数据异构性难题，包括指标定义随时间演变、缺失值标记不统一以及跨年份数据可比性受限等问题。此外，自动化清洗流程难以修正原始数据收集阶段可能存在的报告偏差或方法论不一致性，这要求使用者必须结合世界银行原始方法论说明进行谨慎解读。

常用场景

经典使用场景

在气候变化研究领域，该数据集为分析加纳的气候系统、温室气体排放及能源使用趋势提供了结构化数据支撑。研究者通常利用其时间序列特征，构建回归或分类模型，预测城市化进程与气候指标间的动态关联，例如评估城市人口比例变化对气候适应性的影响。这类应用有助于揭示发展中国家在气候压力下的社会经济演变规律。

实际应用

在实际应用中，该数据集支持政府机构与非营利组织制定气候适应战略。例如，依据城市人口聚集数据，规划者可以优化基础设施投资，提升洪涝高风险区域的防灾能力。同时，能源部门可参考排放指标设计低碳转型路径，助力加纳实现可持续发展目标，并在国际气候谈判中强化数据驱动的决策话语权。

衍生相关工作

围绕该数据集衍生的经典工作包括基于机器学习的城市扩张预测模型，以及气候韧性指数构建研究。例如，学者利用其时间序列训练循环神经网络，模拟不同政策情景下的排放轨迹；亦有团队结合遥感数据，开发了融合社会经济指标的气候风险图谱，为西非地区的适应性治理提供了方法论创新。