electricsheepafrica/africa-ifrc-appeals-data-for-ghana

---
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
- funding
- gha
pretty_name: "Ghana - IFRC Appeals"
dataset_info:
splits:
- name: train
num_examples: 18
- name: test
num_examples: 4
---

# Ghana - IFRC Appeals

**Publisher:** International Federation of Red Cross and Red Crescent Societies (IFRC) · **Source:** [HDX](https://data.humdata.org/dataset/ifrc-appeals-data-for-ghana) · **License:** `cc-by-igo` · **Updated:** 2026-04-10

---

## Abstract

The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.

We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.

There is also a [global dataset](https://data.humdata.org/dataset/global-ifrc-appeals-data).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-10. Geographic scope: **GHA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 23 |
| **Columns** | 41 (14 numeric, 19 categorical, 0 datetime) |
| **Train split** | 18 rows |
| **Test split** | 4 rows |
| **Geographic scope** | GHA |
| **Publisher** | International Federation of Red Cross and Red Crescent Societies (IFRC) |
| **HDX last updated** | 2026-04-10 |

---

## Variables

**Geographic** — `dtype_id` (range 1.0–13.0), `dtype_name` (Flood, Epidemic, Other), `dtype_translation_module_original_language` (en), `atype` (range 0.0–0.0), `atype_display` (DREF) and 18 others.

**Temporal** — `start_date`, `end_date`, `real_data_update`.

**Outcome / Measurement** — `amount_requested` (range 0.0–462699.0), `amount_funded` (range 0.0–462699.0).

**Identifier / Metadata** — `aid` (range 376.0–19087.0), `name` (Ghana - Floods, Ghana - Cholera, Ghana - Cholera Outbreak), `code` (MDRGH022, MDRGH008, M99ME034), `id` (range 608.0–4105.0), `esa_source` and 1 others.

**Other** — `status` (range 0.0–1.0), `sector` (Country cluster for Nigeria, Benin, Ghana and Togo), `created_at`, `modified_at`, `event` (range 112.0–7293.0) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-ghana")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `aid` | int64 | 0.0% | 376.0 – 19087.0 (mean 10088.8261) |
| `name` | object | 0.0% | Ghana - Floods, Ghana - Cholera, Ghana - Cholera Outbreak |
| `dtype_id` | int64 | 0.0% | 1.0 – 13.0 (mean 8.087) |
| `dtype_name` | object | 0.0% | Flood, Epidemic, Other |
| `dtype_translation_module_original_language` | object | 0.0% | en |
| `atype` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `atype_display` | object | 0.0% | DREF |
| `status` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8696) |
| `status_display` | object | 0.0% | Closed, Active |
| `code` | object | 0.0% | MDRGH022, MDRGH008, M99ME034 |
| `sector` | object | 0.0% | Country cluster for Nigeria, Benin, Ghana and Togo |
| `amount_requested` | float64 | 0.0% | 0.0 – 462699.0 (mean 137777.4348) |
| `amount_funded` | float64 | 0.0% | 0.0 – 462699.0 (mean 137777.4348) |
| `start_date` | datetime64[ns, UTC] | 0.0% | |
| `end_date` | datetime64[ns, UTC] | 0.0% | |
| `real_data_update` | datetime64[ns, UTC] | 0.0% | |
| `created_at` | datetime64[ns, UTC] | 0.0% | |
| `modified_at` | datetime64[ns, UTC] | 0.0% | |
| `event` | float64 | 4.3% | 112.0 – 7293.0 (mean 1791.5455) |
| `needs_confirmation` | bool | 0.0% | |
| `country_iso` | object | 0.0% | GH |
| `country_iso3` | object | 0.0% | GHA |
| `country_id` | int64 | 0.0% | 73.0 – 73.0 (mean 73.0) |
| `country_record_type` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `country_record_type_display` | object | 0.0% | Country |
| `country_region` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `country_independent` | bool | 0.0% | |
| `country_is_deprecated` | bool | 0.0% | |
| `country_fdrs` | object | 0.0% | |
| `country_name` | object | 0.0% | |
| `country_society_name` | object | 0.0% | |
| `country_translation_module_original_language` | object | 0.0% | |
| `region_name` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `region_id` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `region_region_name` | object | 0.0% | |
| `region_label` | object | 0.0% | |
| `region_translation_module_original_language` | object | 0.0% | |
| `id` | int64 | 0.0% | 608.0 – 4105.0 (mean 2383.1739) |
| `initial_num_beneficiaries` | int64 | 0.0% | 0.0 – 300000.0 (mean 53373.1739) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `aid` | 376.0 | 19087.0 | 10088.8261 | 8302.0 |
| `dtype_id` | 1.0 | 13.0 | 8.087 | 12.0 |
| `atype` | 0.0 | 0.0 | 0.0 | 0.0 |
| `status` | 0.0 | 1.0 | 0.8696 | 1.0 |
| `amount_requested` | 0.0 | 462699.0 | 137777.4348 | 134948.0 |
| `amount_funded` | 0.0 | 462699.0 | 137777.4348 | 134948.0 |
| `event` | 112.0 | 7293.0 | 1791.5455 | 791.0 |
| `country_id` | 73.0 | 73.0 | 73.0 | 73.0 |
| `country_record_type` | 1.0 | 1.0 | 1.0 | 1.0 |
| `country_region` | 0.0 | 0.0 | 0.0 | 0.0 |
| `region_name` | 0.0 | 0.0 | 0.0 | 0.0 |
| `region_id` | 0.0 | 0.0 | 0.0 | 0.0 |
| `id` | 608.0 | 4105.0 | 2383.1739 | 2100.0 |
| `initial_num_beneficiaries` | 0.0 | 300000.0 | 53373.1739 | 6000.0 |

---

## 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) with >80% missing values were removed: `dtype_summary`, `country_average_household_size`. 5 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 International Federation of Red Cross and Red Crescent Societies (IFRC) 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/ifrc-appeals-data-for-ghana) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ifrc_appeals_data_for_ghana,
title = {Ghana - IFRC Appeals},
author = {International Federation of Red Cross and Red Crescent Societies (IFRC)},
year = {2026},
url = {https://data.humdata.org/dataset/ifrc-appeals-data-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

在构建加纳国际红十字会与红新月会联合会（IFRC）呼吁数据集的过程中，原始数据源自人道主义数据交换平台（HDX），通过CKAN API获取，并经过系统性的清洗与转换。数据清洗步骤包括将列名统一为蛇形命名法，将常见的缺失值标记标准化为NaN，并移除了缺失率超过80%的列。随后，基于解析成功率超过85%的阈值，将五列数据从字符串类型转换为数值或日期时间类型。最终，数据集以固定的随机种子（42）按80/20的比例划分为训练集和测试集，并以Snappy压缩的Parquet格式保存，确保了数据的机器学习就绪性。

使用方法

使用该数据集时，可通过Hugging Face的datasets库便捷加载，具体代码为`load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-ghana")`。加载后，数据集可直接转换为Pandas DataFrame进行探索性分析或模型训练。鉴于其表格结构，适用于监督学习任务，如基于灾害类型、时间或地理特征预测资助金额或受益者规模。用户应注意数据来源于IFRC，未经独立验证，建议参考原始HDX页面了解方法学细节，并在分析中考虑数据局限性，如可能的报告偏差或定义不一致性。

背景与挑战

背景概述

在当代人道主义援助与灾害响应领域，数据驱动的决策支持系统日益成为提升资源分配效率与应急响应精准度的关键。国际红十字与红新月会联合会（IFRC）作为全球最大的人道主义网络，于2026年发布了加纳地区的紧急呼吁数据集，旨在系统记录该国一级行政区划层面的灾害事件、资金需求与援助进展。该数据集由Electric Sheep Africa机构进行机器学习适配性重构，涵盖洪水、流行病等多种灾害类型，通过量化分析援助请求金额、实际筹资额及受益人数等核心指标，为人道主义行动的效果评估与预测建模提供了结构化数据基础。

当前挑战

该数据集致力于应对人道主义援助领域中的资源优化配置与灾害响应效果评估问题，其核心挑战在于如何从有限的、异构的原始记录中提取可泛化的模式，以支持跨灾害类型与时间序列的预测任务。在构建过程中，数据面临多重挑战：原始数据存在定义不一致与报告偏差，例如灾害分类的模糊性可能导致模型特征工程困难；样本规模仅包含23条观测记录，数据稀疏性限制了复杂机器学习模型的应用潜力；此外，自动化清洗流程虽统一了缺失值标记，但无法修正原始数据中可能存在的误报或采样偏差，这要求后续研究必须结合领域知识进行谨慎的数据验证与解释。

常用场景

经典使用场景

在灾害响应与资金管理领域，该数据集为研究人员提供了加纳地区国际红十字与红新月会联合会（IFRC）紧急呼吁的详细记录。经典使用场景聚焦于利用机器学习模型对灾害类型进行分类，或基于历史数据预测未来资金需求。通过分析如洪水、流行病等灾害事件与相应资金请求及资助金额的关系，模型能够识别模式，辅助决策者优化资源分配策略。数据集的结构化特征，如行政单位观测值、时间序列和资金变量，为监督学习任务提供了坚实基础，尤其在表格分类与回归任务中展现出重要价值。

解决学术问题

该数据集有效解决了人道主义研究中数据稀缺与标准化不足的学术难题。通过整合加纳地区的灾害呼吁数据，它支持学者探究灾害响应效率、资金缺口评估以及影响因素分析等关键问题。研究可深入探讨不同灾害类型与资金到位率之间的关联，或评估时间因素对响应效果的影响，从而推动灾害管理理论的实证发展。数据集的存在促进了跨学科研究，将人道主义实践与数据科学方法结合，为制定基于证据的政策提供了可靠的数据支撑。

实际应用

在实际应用中，该数据集被灾害管理机构和非政府组织用于增强应急规划的精准性。例如，结合历史呼吁数据，机构可以开发预测模型，预估未来类似灾害事件所需的资金规模，从而提前筹备资源。数据中的行政单位信息有助于定位高风险区域，优化本地化响应策略。此外，资助金额与请求金额的对比分析能揭示资金流动效率，支持捐赠方进行绩效评估，确保人道主义援助的及时性和有效性，最终提升整体救援行动的协调能力。

数据集最近研究

最新研究方向

在非洲人道主义援助领域，加纳国际红十字与红新月会联合会（IFRC）呼吁数据集为灾害响应资金分配研究提供了关键实证基础。当前前沿研究聚焦于利用机器学习模型预测不同灾害类型下的资金需求与到位效率，结合时间序列分析探索洪涝、流行病等事件与援助响应的动态关联。随着气候变化加剧极端天气事件频发，该数据集支持构建精细化资源优化模型，助力提升区域人道主义行动的时效性与公平性，成为灾害风险管理智能决策系统的重要数据支柱。