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

---
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
- zwe
pretty_name: "Zimbabwe - IFRC Appeals"
dataset_info:
splits:
- name: train
num_examples: 26
- name: test
num_examples: 6
---

# Zimbabwe - IFRC Appeals

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

*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)** | 33 |
| **Columns** | 41 (14 numeric, 19 categorical, 0 datetime) |
| **Train split** | 26 rows |
| **Test split** | 6 rows |
| **Geographic scope** | ZWE |
| **Publisher** | International Federation of Red Cross and Red Crescent Societies (IFRC) |
| **HDX last updated** | 2026-04-10 |

---

## Variables

**Geographic** — `dtype_id` (range 1.0–21.0), `dtype_name` (Epidemic, Flood, Food Insecurity), `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 0.0–38424042.0), `amount_funded` (range 0.0–36864697.5767).

**Identifier / Metadata** — `aid` (range 120.0–19636.0), `name` (Zimbabwe - Floods, Zimbabwe - Food Insecurity, Zimbabwe - Cholera), `code` (MDRZW027, MDRZW008, M98ME091), `id` (range 52.0–4375.0), `esa_source` and 1 others.

**Other** — `status` (range 0.0–1.0), `sector` (Country cluster for Zimbabwe, Malawi and Zambia), `created_at`, `modified_at`, `event` (range 159.0–7760.0) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ifrc-appeals-data-for-zimbabwe")
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% | 120.0 – 19636.0 (mean 9761.697) |
| `name` | object | 0.0% | Zimbabwe - Floods, Zimbabwe - Food Insecurity, Zimbabwe - Cholera |
| `dtype_id` | int64 | 0.0% | 1.0 – 21.0 (mean 9.5758) |
| `dtype_name` | object | 0.0% | Epidemic, Flood, Food Insecurity |
| `dtype_translation_module_original_language` | object | 0.0% | en |
| `atype` | int64 | 0.0% | 0.0 – 1.0 (mean 0.303) |
| `atype_display` | object | 0.0% | DREF, Emergency Appeal |
| `status` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8182) |
| `status_display` | object | 0.0% | Closed, Active |
| `code` | object | 0.0% | MDRZW027, MDRZW008, M98ME091 |
| `sector` | object | 0.0% | Country cluster for Zimbabwe, Malawi and Zambia |
| `amount_requested` | float64 | 0.0% | 0.0 – 38424042.0 (mean 2010039.9091) |
| `amount_funded` | float64 | 0.0% | 0.0 – 36864697.5767 (mean 1660671.2998) |
| `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 | 6.1% | 159.0 – 7760.0 (mean 2926.0645) |
| `needs_confirmation` | bool | 0.0% | |
| `country_iso` | object | 0.0% | ZW |
| `country_iso3` | object | 0.0% | ZWE |
| `country_id` | int64 | 0.0% | 13.0 – 13.0 (mean 13.0) |
| `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% | 52.0 – 4375.0 (mean 2754.4242) |
| `initial_num_beneficiaries` | int64 | 0.0% | 0.0 – 1500000.0 (mean 121111.5152) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `aid` | 120.0 | 19636.0 | 9761.697 | 8694.0 |
| `dtype_id` | 1.0 | 21.0 | 9.5758 | 12.0 |
| `atype` | 0.0 | 1.0 | 0.303 | 0.0 |
| `status` | 0.0 | 1.0 | 0.8182 | 1.0 |
| `amount_requested` | 0.0 | 38424042.0 | 2010039.9091 | 229145.0 |
| `amount_funded` | 0.0 | 36864697.5767 | 1660671.2998 | 229145.0 |
| `event` | 159.0 | 7760.0 | 2926.0645 | 1774.0 |
| `country_id` | 13.0 | 13.0 | 13.0 | 13.0 |
| `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` | 52.0 | 4375.0 | 2754.4242 | 2874.0 |
| `initial_num_beneficiaries` | 0.0 | 1500000.0 | 121111.5152 | 10000.0 |

---

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

---

## Citation

```bibtex
@dataset{hdx_africa_ifrc_appeals_data_for_zimbabwe,
title = {Zimbabwe - 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-zimbabwe},
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

在构建人道主义援助数据集的背景下，该数据集源自国际红十字与红新月会联合会（IFRC）发布的津巴布韦紧急呼吁数据。原始数据通过HDX平台的CKAN API获取，经过系统的数据清洗与标准化处理，包括将列名转换为蛇形命名法、统一缺失值标记为NaN，并移除了缺失率超过80%的列。随后，数据被转换为Parquet格式，并按照80/20的比例使用固定随机种子划分为训练集与测试集，最终以Snappy压缩格式存储，确保了数据的机器学习就绪性。

特点

该数据集聚焦于人道主义援助领域，以津巴布韦为地理范围，记录了第一级行政单位的观测数据。其特点在于包含了41个变量，涵盖地理、时间、资金请求与资助金额等关键维度，如灾害类型、呼吁状态及受益人数等。数据规模较小，总计33行，但结构清晰，兼具14个数值型与19个类别型特征，为分析灾害响应模式与资金流动提供了细粒度的视角。

使用方法

在机器学习应用中，该数据集适用于表格分类与回归任务，例如预测援助资金需求或灾害类型。用户可通过Hugging Face的datasets库直接加载，使用load_dataset函数获取训练集与测试集，并转换为pandas DataFrame以进行进一步分析。数据已预先分割，便于快速开展模型训练与评估，同时其清晰的变量定义与元数据支持跨领域的人道主义研究。

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救援效率与资源分配精准度的关键。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，自其成立以来，持续致力于通过系统性数据收集来优化应急响应机制。该数据集由IFRC于2026年发布，并由Electric Sheep Africa机构进行机器学习友好型重构，聚焦于津巴布韦地区的灾害援助呼吁记录。其核心研究问题在于如何通过结构化数据揭示灾害类型、资金需求与援助成效之间的复杂关联，从而为灾害风险管理、资金筹措策略及长期恢复规划提供实证基础。该数据集不仅丰富了非洲地区人道主义数据的公开资源，也为跨学科研究如发展经济学、公共政策与计算社会科学提供了宝贵的微观实证材料。

当前挑战

该数据集旨在解决人道主义援助领域中的资源分配优化与灾害响应效能评估问题，其核心挑战在于如何从有限且异构的观测数据中提取稳健的预测信号。具体而言，数据规模较小（仅33条观测记录）限制了复杂机器学习模型的训练与验证，可能导致过拟合与泛化能力不足。在构建过程中，原始数据存在定义不一致、缺失值高及报告偏差等问题，例如部分字段如`dtype_summary`因缺失率过高而被移除。此外，自动化清洗流程难以校正原始数据中可能存在的误报值或采样偏差，这要求研究者在使用时需结合领域知识进行谨慎解释与补充验证。

常用场景

经典使用场景

在非洲人道主义数据分析领域，津巴布韦国际红十字与红新月会联合会（IFRC）呼吁数据集为研究者提供了结构化、标准化的灾害响应记录。该数据集最经典的使用场景在于构建预测模型，以评估不同灾害类型（如洪水、流行病、粮食不安全）下资金需求的紧迫性与规模。通过整合时间序列特征与地理行政单元信息，机器学习方法能够分析历史呼吁数据中的模式，从而预测未来灾害事件可能引发的资源请求额度与资助缺口，为人道主义组织的战略规划提供数据驱动的决策支持。

解决学术问题

该数据集有效解决了人道主义研究中资源分配效率与灾害响应预测的学术难题。通过提供详细的金额请求、资助状态、受益人数量及灾害分类等字段，研究者能够深入探讨资金流动与灾害严重性之间的关联，识别影响资助成功率的潜在因素。其意义在于为定量分析人道主义行动提供了高质量的实证基础，推动了灾害经济学、应急管理及发展研究等交叉学科的发展，增强了学术界对复杂危机中资源优化配置的理解。

衍生相关工作

围绕该数据集衍生的经典工作主要包括基于机器学习的灾害资金预测框架与跨区域比较研究。例如，研究者利用其开发了集成时间序列分析与地理信息系统的混合模型，以预测非洲不同国家的IFRC呼吁趋势。此外，该数据集常被整合入更大规模的人道主义数据平台，如HDX全球项目，促进了多源数据融合下的危机图谱构建。这些工作不仅拓展了数据在智能人道主义领域的应用边界，也为后续的开放数据协作与算法标准化奠定了基础。