electricsheepafrica/africa-climate-gambia

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- climate-weather
- conflict-violence
- economics
- education
- food-security
- funding
- hazards-and-risk
- health
- gmb
pretty_name: "HDX HAPI Data for Gambia"
dataset_info:
splits:
- name: train
num_examples: 13561
- name: test
num_examples: 3390
---

# HDX HAPI Data for Gambia

**Publisher:** HDX Humanitarian API Data · **Source:** [HDX](https://data.humdata.org/dataset/hdx-hapi-gmb) · **License:** `hdx-other` · **Updated:** 2026-02-18

---

## Abstract

This dataset contains data obtained from the
[HDX Humanitarian API](https://hapi.humdata.org/) (HDX HAPI),
which provides standardized humanitarian indicators designed
for seamless interoperability from multiple sources.
The data facilitates automated workflows and visualizations
to support humanitarian decision making.
For more information, please see the HDX HAPI
[landing page](https://data.humdata.org/hapi)
and
[documentation](https://hdx-hapi.readthedocs.io/en/latest/).

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the `reference_period_start`, `reference_period_end` column(s). Geographic scope: **GMB**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Geolocated point observations |
| **Rows (total)** | 16,952 |
| **Columns** | 16 (3 numeric, 7 categorical, 2 datetime) |
| **Train split** | 13,561 rows |
| **Test split** | 3,390 rows |
| **Geographic scope** | GMB |
| **Publisher** | HDX Humanitarian API Data |
| **HDX last updated** | 2026-02-18 |

---

## Variables

**Geographic** — `origin_location_code` (GMB, SLE, SEN), `asylum_location_code` (GMB, USA, CAN), `asylum_has_hrp`, `asylum_in_gho`, `population_group` (REF, ASY, OOC) and 2 others.

**Temporal** — `reference_period_start`, `reference_period_end`.

**Demographic** — `gender` (f, m, all), `age_range` (all, 0-4, 5-11), `min_age` (range 0.0–60.0).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-20).

**Other** — `origin_has_hrp`, `origin_in_gho`.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-climate-gambia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `origin_location_code` | object | 0.0% | GMB, SLE, SEN |
| `origin_has_hrp` | bool | 0.0% | |
| `origin_in_gho` | bool | 0.0% | |
| `asylum_location_code` | object | 0.0% | GMB, USA, CAN |
| `asylum_has_hrp` | bool | 0.0% | |
| `asylum_in_gho` | bool | 0.0% | |
| `population_group` | object | 0.0% | REF, ASY, OOC |
| `gender` | object | 0.0% | f, m, all |
| `age_range` | object | 0.0% | all, 0-4, 5-11 |
| `min_age` | float64 | 23.1% | 0.0 – 60.0 (mean 19.0) |
| `max_age` | float64 | 38.5% | 4.0 – 59.0 (mean 22.75) |
| `population` | int64 | 0.0% | 0.0 – 12968.0 (mean 58.6016) |
| `reference_period_start` | datetime64[ns] | 0.0% | |
| `reference_period_end` | datetime64[ns] | 0.0% | |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-20 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `min_age` | 0.0 | 60.0 | 19.0 | 12.0 |
| `max_age` | 4.0 | 59.0 | 22.75 | 14.0 |
| `population` | 0.0 | 12968.0 | 58.6016 | 0.0 |

---

## 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) 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 HDX Humanitarian API Data and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `min_age`, `max_age`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/hdx-hapi-gmb) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_climate_gambia,
title = {HDX HAPI Data for Gambia},
author = {HDX Humanitarian API Data},
year = {2026},
url = {https://data.humdata.org/dataset/hdx-hapi-gmb},
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
language_creators:
- 现有资源采集
language:
- 英语
license:
- 其他
multilinguality:
- 单语言
size_categories:
- 10000 < 样本数 < 100000
source_datasets:
- 原创数据集
task_categories:
- 表格分类
- 其他
task_ids:
- 无
tags:
- 非洲
- 人道主义
- HDX
- 电羊非洲（Electric Sheep Africa）
- 气候与气象
- 冲突与暴力
- 经济学
- 教育
- 粮食安全
- 资助
- 灾害与风险
- 卫生
- 冈比亚（GMB）
pretty_name: "冈比亚HDX HAPI数据集"
dataset_info:
splits:
- name: train
num_examples: 13561
- name: test
num_examples: 3390
---

# 冈比亚HDX HAPI数据集

**发布方：** HDX人道主义API数据集 · **来源：** [HDX](https://data.humdata.org/dataset/hdx-hapi-gmb) · **许可证：** `hdx-other` · **更新时间：** 2026-02-18

---

## 摘要
本数据集的数据源自[HDX人道主义API（HDX HAPI）](https://hapi.humdata.org/)，该平台提供来自多源的标准化人道主义指标，旨在实现无缝互操作。本数据集支持自动化工作流与可视化流程，助力人道主义决策制定。如需了解更多信息，请访问HDX HAPI的[主页](https://data.humdata.org/hapi)与[文档](https://hdx-hapi.readthedocs.io/en/latest/)。

本数据集的每一行均代表一个带地理定位的点位观测值。时间覆盖范围由`reference_period_start`（参考时段开始）与`reference_period_end`（参考时段结束）列标注。地理覆盖范围：**冈比亚（GMB）**。

*本数据集已由[电羊非洲（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式。*

---

## 数据集特征
| 项目 | 详情 |
|---|---|
| **研究领域** | 粮食安全与营养 |
| **观测单元** | 带地理定位的点位观测值 |
| **总样本行数** | 16952 |
| **列数** | 16列（其中3列为数值型、7列为分类型、2列为日期时间型） |
| **训练集划分** | 13561行 |
| **测试集划分** | 3390行 |
| **地理覆盖范围** | 冈比亚（GMB） |
| **发布方** | HDX人道主义API数据集 |
| **HDX最后更新时间** | 2026-02-18 |

---

## 变量说明
1. **地理类变量**：`origin_location_code`（取值：GMB、SLE、SEN）、`asylum_location_code`（取值：GMB、USA、CAN）、`asylum_has_hrp`、`asylum_in_gho`、`population_group`（取值：REF、ASY、OOC）及另外2个变量。
2. **时间类变量**：`reference_period_start`（参考时段开始）、`reference_period_end`（参考时段结束）。
3. **人口统计类变量**：`gender`（取值：f、m、all）、`age_range`（取值：all、0-4、5-11）、`min_age`（取值范围：0.0~60.0）。
4. **标识符/元数据类变量**：`esa_source`（取值：HDX）、`esa_processed`（取值：2026-04-20）。
5. **其他变量**：`origin_has_hrp`、`origin_in_gho`。

---

## 快速上手
python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-climate-gambia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据Schema
| 列名 | 数据类型 | 缺失占比 | 取值范围/示例值 |
|---|---|---|---|
| `origin_location_code` | 字符型（object） | 0.0% | GMB、SLE、SEN |
| `origin_has_hrp` | 布尔型（bool） | 0.0% | 无 |
| `origin_in_gho` | 布尔型（bool） | 0.0% | 无 |
| `asylum_location_code` | 字符型（object） | 0.0% | GMB、USA、CAN |
| `asylum_has_hrp` | 布尔型（bool） | 0.0% | 无 |
| `asylum_in_gho` | 布尔型（bool） | 0.0% | 无 |
| `population_group` | 字符型（object） | 0.0% | REF、ASY、OOC |
| `gender` | 字符型（object） | 0.0% | f、m、all |
| `age_range` | 字符型（object） | 0.0% | all、0-4、5-11 |
| `min_age` | 浮点型（float64） | 23.1% | 0.0~60.0（均值：19.0） |
| `max_age` | 浮点型（float64） | 38.5% | 4.0~59.0（均值：22.75） |
| `population` | 整型（int64） | 0.0% | 0.0~12968.0（均值：58.6016） |
| `reference_period_start` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `reference_period_end` | 日期时间型（datetime64[ns]） | 0.0% | 无 |
| `esa_source` | 字符型（object） | 0.0% | HDX |
| `esa_processed` | 字符型（object） | 0.0% | 2026-04-20 |

---

## 数值型变量统计摘要
| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `min_age` | 0.0 | 60.0 | 19.0 | 12.0 |
| `max_age` | 4.0 | 59.0 | 22.75 | 14.0 |
| `population` | 0.0 | 12968.0 | 58.6016 | 0.0 |

---

## 数据整理流程
原始数据通过CKAN API从HDX下载，并转换为Parquet格式。列名统一转换为小写并标准化为蛇形命名法（snake_case）。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。基于解析成功率（阈值>85%），将2列从字符串类型转换为数值型或日期时间型。本数据集以80:20的比例划分为训练集与测试集，使用固定随机种子（42）进行划分，并保存为Snappy压缩的Parquet格式。

---

## 数据集局限性
1. 本数据源自HDX人道主义API数据集，未经过电羊非洲（ESA）的独立验证。
2. 自动化清洗流程无法修正原始数据集中的错误报告值、定义不一致问题或采样偏差。
3. 以下列的缺失值占比超过20%，在建模时需谨慎使用：`min_age`、`max_age`。
4. 如需了解发布方的方法论说明与免责声明，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/hdx-hapi-gmb)。

---

## 引用格式
bibtex
@dataset{hdx_africa_climate_gambia,
title = {HDX HAPI Data for Gambia},
author = {HDX Humanitarian API Data},
year = {2026},
url = {https://data.humdata.org/dataset/hdx-hapi-gmb},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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