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

---
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
- gin
pretty_name: "Guinea - IFRC Appeals"
dataset_info:
splits:
- name: train
num_examples: 17
- name: test
num_examples: 4
---

# Guinea - IFRC Appeals

**Publisher:** International Federation of Red Cross and Red Crescent Societies (IFRC) · **Source:** [HDX](https://data.humdata.org/dataset/ifrc-appeals-data-for-guinea) · **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: **GIN**.

*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)** | 22 |
| **Columns** | 41 (14 numeric, 19 categorical, 0 datetime) |
| **Train split** | 17 rows |
| **Test split** | 4 rows |
| **Geographic scope** | GIN |
| **Publisher** | International Federation of Red Cross and Red Crescent Societies (IFRC) |
| **HDX last updated** | 2026-04-10 |

---

## Variables

**Geographic** — `dtype_id` (range 1.0–27.0), `dtype_name` (Flood, Epidemic, Population Movement), `dtype_translation_module_original_language` (en), `atype` (range 0.0–1.0), `atype_display` (DREF, Emergency Appeal) and 18 others.

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

**Outcome / Measurement** — `amount_requested` (range 0.0–38798890.0), `amount_funded` (range 0.0–35927077.0088).

**Identifier / Metadata** — `aid` (range 33.0–19554.0), `name` (Guinea - Floods, Guinea - Floods in Kankan, Guinea - Election readiness), `code` (MDRGN019, MDRGN018, M98EA020), `id` (range 71.0–4158.0), `esa_source` and 1 others.

**Other** — `status` (range 0.0–1.0), `sector` (Country cluster for Sierra Leone, Liberia, Guinea and Guinea Bisau), `created_at`, `modified_at`, `event` (range 100.0–7297.0) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-guinea")
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% | 33.0 – 19554.0 (mean 10339.9545) |
| `name` | object | 0.0% | Guinea - Floods, Guinea - Floods in Kankan, Guinea - Election readiness |
| `dtype_id` | int64 | 0.0% | 1.0 – 27.0 (mean 8.5909) |
| `dtype_name` | object | 0.0% | Flood, Epidemic, Population Movement |
| `dtype_translation_module_original_language` | object | 0.0% | en |
| `atype` | int64 | 0.0% | 0.0 – 1.0 (mean 0.1364) |
| `atype_display` | object | 0.0% | DREF, Emergency Appeal |
| `status` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8636) |
| `status_display` | object | 0.0% | Closed, Active |
| `code` | object | 0.0% | MDRGN019, MDRGN018, M98EA020 |
| `sector` | object | 0.0% | Country cluster for Sierra Leone, Liberia, Guinea and Guinea Bisau |
| `amount_requested` | float64 | 0.0% | 0.0 – 38798890.0 (mean 2054041.4091) |
| `amount_funded` | float64 | 0.0% | 0.0 – 35927077.0088 (mean 1831492.8118) |
| `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 | 9.1% | 100.0 – 7297.0 (mean 2553.35) |
| `needs_confirmation` | bool | 0.0% | |
| `country_iso` | object | 0.0% | GN |
| `country_iso3` | object | 0.0% | GIN |
| `country_id` | int64 | 0.0% | 77.0 – 77.0 (mean 77.0) |
| `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% | 71.0 – 4158.0 (mean 2579.8182) |
| `initial_num_beneficiaries` | int64 | 0.0% | 0.0 – 12700000.0 (mean 727206.5909) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `aid` | 33.0 | 19554.0 | 10339.9545 | 9018.5 |
| `dtype_id` | 1.0 | 27.0 | 8.5909 | 12.0 |
| `atype` | 0.0 | 1.0 | 0.1364 | 0.0 |
| `status` | 0.0 | 1.0 | 0.8636 | 1.0 |
| `amount_requested` | 0.0 | 38798890.0 | 2054041.4091 | 181914.5 |
| `amount_funded` | 0.0 | 35927077.0088 | 1831492.8118 | 176565.5 |
| `event` | 100.0 | 7297.0 | 2553.35 | 1471.5 |
| `country_id` | 77.0 | 77.0 | 77.0 | 77.0 |
| `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` | 71.0 | 4158.0 | 2579.8182 | 2359.0 |
| `initial_num_beneficiaries` | 0.0 | 12700000.0 | 727206.5909 | 14175.0 |

---

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

---

## Citation

```bibtex
@dataset{hdx_africa_ifrc_appeals_data_for_guinea,
title = {Guinea - 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-guinea},
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

在构建几内亚国际红十字与红新月会联合会呼吁数据集的过程中，原始数据通过CKAN应用程序接口从人道主义数据交换平台获取。数据清洗流程包括将列名标准化为蛇形命名法，统一处理常见的缺失值标记，并移除了缺失率超过百分之八十的列。依据解析成功率超过百分之八十五的阈值，将部分列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按八比二的比例划分为训练集和测试集，并以Snappy压缩的Parquet格式存储，确保了数据的结构化和机器学习就绪性。

特点

该数据集聚焦于人道主义援助领域，记录了国际红十字与红新月会联合会在几内亚发起的紧急呼吁和救灾基金项目。其特点在于以行政区划为观测单元，包含了四十一列变量，涵盖地理标识、灾害类型、资金请求与到位情况、时间跨度以及受益人数等关键维度。数据集规模精炼，总计二十二条记录，其中数值型变量如请求金额范围从零至三千八百余万，提供了量化分析的基础。数据经过精心整理，格式统一，便于直接应用于表格分类或回归等机器学习任务。

使用方法

研究人员可通过Hugging Face的`datasets`库便捷加载此数据集。使用`load_dataset`函数并指定相应路径即可获取已分割的训练集与测试集。数据以Pandas DataFrame格式呈现后，可直接用于探索性数据分析、特征工程或模型训练。鉴于其结构化特征，该数据集适用于预测资金到位率、分析灾害响应模式或评估人道主义干预效果等研究场景。用户应参考原始发布方的方法论说明，并注意数据固有的局限性，以确保分析的严谨性。

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正日益成为优化资源分配与提升应急效率的核心工具。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年发布了针对几内亚的紧急呼吁数据集，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团队进行机器学习友好型重构，涵盖了洪水、流行病、人口流动等多种灾害类型，以及对应的资金请求与到位情况，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。其创建不仅反映了国际组织在数据透明化与标准化方面的努力，也为非洲地区的灾害韧性研究注入了新的实证资源。

当前挑战

该数据集致力于应对人道主义援助中的资金需求预测与分配优化问题，其核心挑战在于灾害事件的异质性高、数据稀疏性显著，且援助效果受多重外部因素交织影响，难以建立稳健的因果推断模型。在构建过程中，原始数据存在报告不一致、缺失值比例较高以及定义标准不统一等障碍，例如部分字段需进行类型转换与清洗，且超过80%缺失值的列被移除，这可能导致潜在信息损失。此外，数据规模有限，仅包含22条观测记录，对复杂机器学习任务的泛化能力构成制约，同时自动化处理无法修正原始收集过程中的报告偏差或定义矛盾，需依赖发布方的方法学说明进行谨慎解读。

常用场景

经典使用场景

在灾害响应与资源分配领域，该数据集为机器学习模型提供了结构化训练样本，支持对几内亚地区人道主义援助请求的分类与回归分析。研究人员利用其包含的灾害类型、资金请求与资助状态等变量，构建预测模型以评估不同紧急事件下的资金需求模式。通过分析历史援助记录，模型能够识别影响资金到位率的关键因素，为优化应急响应策略提供数据驱动见解。

实际应用

实际应用中，国际救援组织可依据该数据集构建智能决策支持系统，动态优化几内亚地区的资源调配方案。系统通过比对历史援助案例，能够预测新发灾害的潜在资金缺口，辅助制定精准的募捐目标与分配计划。地方政府亦可借鉴其模式建立区域性灾害响应数据库，提升跨部门协同效率，确保有限资源优先覆盖受灾最严重的社群。

衍生相关工作

基于该数据集衍生的经典研究包括跨区域灾害响应对比分析框架，以及援助资金预测的时序建模方法。学者通过扩展其地理覆盖范围，构建了西非多国援助效率评估模型，揭示了政治稳定性与资金到位率的潜在关联。另有工作融合卫星遥感数据，创建了多维灾害影响评估体系，为人道主义行动的智能化转型奠定了方法论基础。