electricsheepafrica/africa-world-bank-climate-change-indicators-for-central-african-republic

---
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
- caf
pretty_name: "Central African Republic - Climate Change"
dataset_info:
splits:
- name: train
num_examples: 1032
- name: test
num_examples: 258
---

# Central African Republic - Climate Change

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

*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)** | 1,291 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 1,032 rows |
| **Test split** | 258 rows |
| **Geographic scope** | CAF |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Central African Republic), `country_iso3` (CAF), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -0.7992–5330690.0).

**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` (2026-04-16).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-central-african-republic")
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% | Central African Republic |
| `country_iso3` | object | 0.0% | CAF |
| `year` | int64 | 0.0% | 1960.0 – 2024.0 (mean 1998.4415) |
| `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% | -0.7992 – 5330690.0 (mean 241162.8097) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-16 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1998.4415 | 2002.0 |
| `value` | -0.7992 | 5330690.0 | 241162.8097 | 23.5 |

---

## 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-central-african-republic) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_climate_change_indicators_for_central_african_republic,
title = {Central African Republic - Climate Change},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-central-african-republic},
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.*

annotations_creators:
- 无注释（no-annotation）
language_creators:
- 现有资源获取（found）
language:
- 英语（en）
license: CC-BY-4.0
multilinguality:
- 单语言（monolingual）
size_categories:
- 1000<n<10000
source_datasets:
- 原创数据集（original）
task_categories:
- 表格分类（tabular-classification）
- 表格回归（tabular-regression）
task_ids: []
tags:
- 非洲（africa）
- 人道主义（humanitarian）
- HDX（Humanitarian Data Exchange）
- electric-sheep-africa
- 气候与天气（climate-weather）
- 指标（indicators）
- CAF（中非共和国）
pretty_name: "中非共和国——气候变化"
dataset_info:
splits:
- name: train
num_examples: 1032
- name: test
num_examples: 258

# 中非共和国——气候变化

**发布方**：世界银行集团（World Bank Group） · **数据源**：[HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-central-african-republic) · **许可协议**：`CC-BY` · **更新时间**：2026-03-27

---

## 摘要

本数据集包含来自世界银行[数据门户（data portal）](http://data.worldbank.org/)的相关数据，同时HDX平台上还提供了一份[整合型国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-central-african-republic)。

气候变化预计将对发展中国家造成最为严重的冲击。其带来的影响——气温升高、降水格局改变、海平面上升以及愈发频发的气象灾害——将对农业、粮食与水资源供应构成威胁。发展中国家在消除贫困、饥饿与疾病方面取得的近期成果，以及数十亿民众的生计与生活都将因此受到影响。应对气候变化需要各国开展前所未有的跨境全球合作。世界银行集团正助力支持发展中国家，为全球气候治理贡献力量，同时针对不同发展中国家伙伴的差异化需求调整工作方案。本数据集涵盖气候系统、气候影响暴露度、韧性、温室气体排放以及能源使用等相关数据。其他与气候变化相关的指标可在其他数据页面查阅，特别是环境、农业与农村发展、能源与矿业、卫生、基础设施、贫困与城市发展等板块。

本数据集的每一行均代表国家层面的汇总数据。本数据集最后一次在HDX平台更新的时间为2026-03-27。地理覆盖范围：**CAF（中非共和国）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 粮食安全与营养 |
| **观测单元** | 国家层面汇总数据 |
| **总数据行数** | 1291 |
| **列数** | 8（2个数值型、6个分类型、0个日期时间型） |
| **训练集划分** | 1032行 |
| **测试集划分** | 258行 |
| **地理覆盖范围** | CAF（中非共和国） |
| **发布方** | 世界银行集团（World Bank Group） |
| **HDX最后更新时间** | 2026-03-27 |

---

## 变量分类

**地理类变量**：`country_name`（国家名称：中非共和国）、`country_iso3`（国家代码：CAF）、`year`（年份：取值范围1960.0–2024.0）。

**结果/测量类变量**：`value`（指标数值：取值范围-0.7992–5330690.0）。

**标识符/元数据类变量**：`indicator_name`（指标名称：城镇人口占总人口比例、城镇人口总数、总人口数）、`indicator_code`（指标代码：SP.URB.TOTL.IN.ZS、SP.URB.TOTL、SP.POP.TOTL）、`esa_source`（数据来源：HDX）、`esa_processed`（数据处理时间：2026-04-16）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-central-african-republic")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据结构

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_name` | 字符型（object） | 0.0% | 中非共和国 |
| `country_iso3` | 字符型（object） | 0.0% | CAF |
| `year` | 64位整数型（int64） | 0.0% | 1960.0 – 2024.0（均值1998.4415） |
| `indicator_name` | 字符型（object） | 0.0% | 城镇人口占总人口比例、城镇人口总数、总人口数 |
| `indicator_code` | 字符型（object） | 0.0% | SP.URB.TOTL.IN.ZS、SP.URB.TOTL、SP.POP.TOTL |
| `value` | 64位浮点型（float64） | 0.0% | -0.7992 – 5330690.0（均值241162.8097） |
| `esa_source` | 字符型（object） | 0.0% | HDX |
| `esa_processed` | 字符型（object） | 0.0% | 2026-04-16 |

---

## 数值型统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1998.4415 | 2002.0 |
| `value` | -0.7992 | 5330690.0 | 241162.8097 | 23.5 |

---

## 数据整理流程

原始数据通过CKAN API从HDX平台下载，并转换为Parquet格式。列名均转换为小写并标准化为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件保存。

---

## 局限性说明

- 本数据集源自世界银行集团，并未由Electric Sheep Africa（ESA）进行独立验证。
- 自动化数据清洗无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。
- 如需了解发布方提供的方法说明与注意事项，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-central-african-republic)。

---

## 引用格式

bibtex
@dataset{hdx_africa_world_bank_climate_change_indicators_for_central_african_republic,
title = {Central African Republic - Climate Change},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-central-african-republic},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)——非洲机器学习数据集基础设施提供商，总部位于尼日利亚拉各斯。*