electricsheepafrica/africa-nigeria-operational-presence

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- hxl
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- nga
pretty_name: "Nigeria : Operational Presence"
dataset_info:
splits:
- name: train
num_examples: 35365
- name: test
num_examples: 8841
---

# Nigeria : Operational Presence

**Publisher:** OCHA Nigeria · **Source:** [HDX](https://data.humdata.org/dataset/nigeria-operational-presence) · **License:** `cc-by` · **Updated:** 2025-05-05

---

## Abstract

The Who Does What Where is a core humanitarian dataset for coordination. This data contains operational presence of humanitarian partners in Nigeria at admin 3 (ward) level by cluster.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **NGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 44,207 |
| **Columns** | 19 (2 numeric, 17 categorical, 0 datetime) |
| **Train split** | 35,365 rows |
| **Test split** | 8,841 rows |
| **Geographic scope** | NGA |
| **Publisher** | OCHA Nigeria |
| **HDX last updated** | 2025-05-05 |

---

## Variables

**Geographic** — `org_acronym` (UNICEF, WHO, IOM), `type_of_organization` (UN Agency, International NGO, Government), `states` (Borno, Adamawa, Yobe), `state_pcode` (NGA008, NGA002, NGA036), `lga` (Maiduguri, Jere, Konduga) and 4 others.

**Temporal** — `month`.

**Identifier / Metadata** — `esa_source`, `esa_processed`.

**Other** — `organisation` (United Nations Children's Emergency Fund, World Health Organization, International Organization for Migration), `project_sector` (Protection, Nutrition, Health), `activities` (Nutrition Activity, Health Activities, Provide structured recreational, creative and social activities to children and adolescents), `status` (Ongoing, Completed, completed), `ishrp` and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-operational-presence")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `organisation` | object | 0.0% | United Nations Children's Emergency Fund, World Health Organization, International Organization for Migration |
| `org_acronym` | object | 0.0% | UNICEF, WHO, IOM |
| `type_of_organization` | object | 0.0% | UN Agency, International NGO, Government |
| `project_sector` | object | 0.0% | Protection, Nutrition, Health |
| `activities` | object | 0.3% | Nutrition Activity, Health Activities, Provide structured recreational, creative and social activities to children and adolescents |
| `status` | object | 0.5% | Ongoing, Completed, completed |
| `states` | object | 0.0% | Borno, Adamawa, Yobe |
| `state_pcode` | object | 83.3% | NGA008, NGA002, NGA036 |
| `lga` | object | 0.0% | Maiduguri, Jere, Konduga |
| `lga_pcode` | object | 4.1% | NG008021, NG008013, NG008016 |
| `ward` | object | 20.1% | |
| `ishrp` | object | 1.5% | |
| `response_type` | object | 0.0% | |
| `isrp` | object | 36.8% | |
| `people_reached` | float64 | 64.4% | 0.0 – 131099.0 (mean 931.1212) |
| `month` | object | 0.0% | |
| `year` | int64 | 0.0% | 2019.0 – 2020.0 (mean 2019.1673) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `people_reached` | 0.0 | 131099.0 | 931.1212 | 60.0 |
| `year` | 2019.0 | 2020.0 | 2019.1673 | 2019.0 |

---

## 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`. 82,415 exact duplicate rows were removed. 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 OCHA Nigeria 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: `state_pcode`, `ward`, `isrp`, `people_reached`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/nigeria-operational-presence) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_nigeria_operational_presence,
title = {Nigeria : Operational Presence},
author = {OCHA Nigeria},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-operational-presence},
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.*

---
注释创建者:
- 无注释
语言创建方式:
- 公开数据源获取
语言:
- en
许可协议:
- cc-by-4.0
多语言属性:
- 单语言
样本规模范畴:
- 10000 < 样本数 < 100000
源数据集:
- 原始数据集
任务类别:
- 其他
任务子类别:
- 无
标签:
- 非洲
- 人道主义
- 人类数据交换平台（Humanitarian Data Exchange, HDX）
- Electric Sheep Africa
- 人道主义交换语言（Humanitarian eXchange Language, HXL）
- 运营存在
- 谁在何处做何事（Who Does What Where, 3W/4W/5W）
- 尼日利亚（NGA）
数据集昵称: "尼日利亚：运营存在"
数据集信息:
划分集:
- 名称: 训练集（train）
样本数: 35365
- 名称: 测试集（test）
样本数: 8841
---

# 尼日利亚：运营存在

**发布方：联合国人道主义事务协调厅尼日利亚办事处（OCHA Nigeria）** · **来源：[人类数据交换平台（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/nigeria-operational-presence)** · **许可协议：`cc-by`** · **更新时间：2025-05-05**

---

## 摘要

“谁在何处做何事（Who Does What Where）”是用于人道主义协调工作的核心数据集。本数据集包含尼日利亚三级行政区域（街区，ward）层面、按人道主义集群分类的各合作伙伴运营存在情况。

本数据集的每一行均代表一条次国家行政单元观测记录。数据最近一次在HDX平台更新的时间为2025年5月5日。地理覆盖范围：**尼日利亚（NGA）**。

*本数据集已由[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

| | |
|---|---|
| **领域** | 人道主义与发展数据 |
| **观测单元** | 次国家行政单元观测记录 |
| **总样本行数** | 44207 |
| **列数** | 19列（2列为数值型，17列为分类型，0列为日期时间型） |
| **训练集划分** | 35365行 |
| **测试集划分** | 8841行 |
| **地理覆盖范围** | 尼日利亚（NGA） |
| **发布方** | 联合国人道主义事务协调厅尼日利亚办事处（OCHA Nigeria） |
| **HDX平台最后更新时间** | 2025-05-05 |

---

## 变量说明

### 地理类变量
`org_acronym`（机构缩写：联合国儿童基金会（United Nations Children's Emergency Fund, UNICEF）、世界卫生组织（World Health Organization, WHO）、国际移民组织（International Organization for Migration, IOM）等）、`type_of_organization`（组织类型：联合国机构、国际非政府组织、政府部门）、`states`（州：博尔诺州、阿达马瓦州、约贝州等）、`state_pcode`（州编码：NGA008、NGA002、NGA036等）、`lga`（地方政府区域：迈杜古里、杰雷、孔杜加等）及另外4个变量。

### 时间类变量
`month`（月份）。

### 标识符与元数据变量
`esa_source`、`esa_processed`。

### 其他变量
`organisation`（机构全称：联合国儿童基金会、世界卫生组织、国际移民组织等）、`project_sector`（项目领域：保护、营养、卫生等）、`activities`（活动内容：营养活动、卫生活动、为儿童及青少年提供结构化娱乐、创意与社交活动等）、`status`（项目状态：进行中、已完成）及另外2个变量。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-nigeria-operational-presence")
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 | 缺失率 | 取值范围/示例值 |
|---|---|---|---|
| `organisation` | 字符型 | 0.0% | 联合国儿童基金会、世界卫生组织、国际移民组织 |
| `org_acronym` | 字符型 | 0.0% | UNICEF、WHO、IOM |
| `type_of_organization` | 字符型 | 0.0% | 联合国机构、国际非政府组织、政府部门 |
| `project_sector` | 字符型 | 0.0% | 保护、营养、卫生 |
| `activities` | 字符型 | 0.3% | 营养活动、卫生活动、为儿童及青少年提供结构化娱乐、创意与社交活动 |
| `status` | 字符型 | 0.5% | 进行中、已完成 |
| `states` | 字符型 | 0.0% | 博尔诺州、阿达马瓦州、约贝州 |
| `state_pcode` | 字符型 | 83.3% | NGA008、NGA002、NGA036 |
| `lga` | 字符型 | 0.0% | 迈杜古里、杰雷、孔杜加 |
| `lga_pcode` | 字符型 | 4.1% | NG008021、NG008013、NG008016 |
| `ward` | 字符型 | 20.1% | 无 |
| `ishrp` | 字符型 | 1.5% | 无 |
| `response_type` | 字符型 | 0.0% | 无 |
| `isrp` | 字符型 | 36.8% | 无 |
| `people_reached` | 浮点型 | 64.4% | 0.0 – 131099.0（均值931.1212） |
| `month` | 字符型 | 0.0% | 无 |
| `year` | 整型 | 0.0% | 2019.0 – 2020.0（均值2019.1673） |
| `esa_source` | 字符型 | 0.0% | 无 |
| `esa_processed` | 字符型 | 0.0% | 无 |

---

## 数值型变量统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `people_reached` | 0.0 | 131099.0 | 931.1212 | 60.0 |
| `year` | 2019.0 | 2020.0 | 2019.1673 | 2019.0 |

---

## 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。列名统一转为小写并采用蛇形命名法（snake_case）进行标准化。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。共移除82415条完全重复的样本行。本数据集以固定随机种子（42）按80/20比例划分为训练集与测试集，并以Snappy压缩的Parquet格式存储。

---

## 数据集局限性

- 本数据源自联合国人道主义事务协调厅尼日利亚办事处，未由Electric Sheep Africa（ESA）进行独立验证。
- 自动化清洗流程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。
- 以下列的缺失率超过20%，在建模过程中需谨慎使用：`state_pcode`、`ward`、`isrp`、`people_reached`。
- 如需了解发布方的方法论说明与免责声明，请参阅[HDX平台原始数据集页面](https://data.humdata.org/dataset/nigeria-operational-presence)。

---

## 引用格式

bibtex
@dataset{hdx_africa_nigeria_operational_presence,
title = {尼日利亚：运营存在},
author = {联合国人道主义事务协调厅尼日利亚办事处（OCHA Nigeria）},
year = {2025},
url = {https://data.humdata.org/dataset/nigeria-operational-presence},
note = {由Electric Sheep Africa整理为机器学习可用格式（https://huggingface.co/electricsheepafrica)}
}

---

*[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。*