electricsheepafrica/africa-north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018

---
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
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- geodata
- health
- operational-presence
- who-is-doing-what-and-where-3w-4w-5w
- nga
pretty_name: "North East Nigeria Health Sector Operational Presence by LGA as of June 2018"
dataset_info:
splits:
- name: train
num_examples: 52
- name: test
num_examples: 13
---

# North East Nigeria Health Sector Operational Presence by LGA as of June 2018

**Publisher:** iMMAP Inc. · **Source:** [HDX](https://data.humdata.org/dataset/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018) · **License:** `cc-by` · **Updated:** 2024-09-13

---

## Abstract

Both the shapefile and CSV feature North East Nigeria Health Sector Humanitarian Partner Operational Presence by Local Government Area in Borno, Yobe and Adamawa, the three crisis-affected states - as of June 2018.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2024-09-13. Geographic scope: **NGA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 65 |
| **Columns** | 6 (1 numeric, 5 categorical, 0 datetime) |
| **Train split** | 52 rows |
| **Test split** | 13 rows |
| **Geographic scope** | NGA |
| **Publisher** | iMMAP Inc. |
| **HDX last updated** | 2024-09-13 |

---

## Variables

**Geographic** — `state` (Borno, Adamawa, Yobe), `lga` (Demsa, Jere, Kala-Balge).

**Outcome / Measurement** — `number` (range 0.0–18.0).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-08).

**Other** — `partner_presence` (CRS, FHI 360, UNFPA, UNICEF, WHO, UNICEF, WHO, AAH, UNFPA, UNICEF, WHO).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `state` | object | 0.0% | Borno, Adamawa, Yobe |
| `lga` | object | 0.0% | Demsa, Jere, Kala-Balge |
| `partner_presence` | object | 3.1% | CRS, FHI 360, UNFPA, UNICEF, WHO, UNICEF, WHO, AAH, UNFPA, UNICEF, WHO |
| `number` | int64 | 0.0% | 0.0 – 18.0 (mean 4.9538) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `number` | 0.0 | 18.0 | 4.9538 | 4.0 |

---

## 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`. 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 iMMAP Inc. 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/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_north_east_nigeria_health_sector_operational_presence_by_lga_as_of_june_2018,
title = {North East Nigeria Health Sector Operational Presence by LGA as of June 2018},
author = {iMMAP Inc.},
year = {2024},
url = {https://data.humdata.org/dataset/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018},
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:
- 无标注
language_creators:
- 公开资源采集
language:
- 英语
license:
- CC BY 4.0
multilinguality:
- 单语言
size_categories:
- 样本量少于1000
source_datasets:
- 原生数据集
task_categories:
- 表格分类
task_ids:
- 无
tags:
- 非洲
- 人道主义
- HDX
- Electric Sheep Africa
- 地理数据
- 卫生
- 运营现状
- 谁在何处做何事（3W/4W/5W框架）
- 尼日利亚（NGA）
pretty_name: "2018年6月尼日利亚东北部各地方政府区域（Local Government Area, LGA）卫生部门运营现状"

# 2018年6月尼日利亚东北部各地方政府区域卫生部门运营现状

**发布方：** iMMAP Inc. · **来源：** [人道主义数据交换平台（Humanitarian Data Exchange, HDX）](https://data.humdata.org/dataset/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018) · **许可证：** `cc-by` · **更新时间：** 2024-09-13

---

## 摘要

本数据集同时提供shapefile与CSV格式的数据，涵盖2018年6月尼日利亚东北部博尔诺州、约贝州与阿达马瓦州这三个受危机影响州的各地方政府区域卫生部门人道主义合作伙伴运营现状。

数据集中每一行代表一个次国家级行政单元的观测值。本数据集最近一次在HDX平台的更新时间为2024-09-13，地理覆盖范围为**尼日利亚（NGA）**。

*本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 公共卫生 |
| **观测单元** | 次国家级行政单元观测值 |
| **总行数** | 65 |
| **列数** | 6（1个数值型列、5个分类型列、0个日期时间列） |
| **训练集拆分** | 52行 |
| **测试集拆分** | 13行 |
| **地理覆盖范围** | 尼日利亚（NGA） |
| **发布方** | iMMAP Inc. |
| **HDX最后更新时间** | 2024-09-13 |

---

## 变量

**地理类变量** — `state`（州：博尔诺、阿达马瓦、约贝），`lga`（地方政府区域：德姆萨、杰雷、卡拉-巴莱杰等）。

**结果/测量类变量** — `number`（取值范围0.0~18.0）。

**标识符/元数据类变量** — `esa_source`（数据来源：HDX），`esa_processed`（数据处理时间：2026-04-08）。

**其他变量** — `partner_presence`（合作伙伴机构：CRS、FHI 360、UNFPA、UNICEF、WHO、AAH等）。

---

## 快速上手

python
from datasets import load_dataset

# 加载数据集
ds = load_dataset("electricsheepafrica/africa-north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据模式

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `state` | 字符串型 | 0.0% | 博尔诺、阿达马瓦、约贝 |
| `lga` | 字符串型 | 0.0% | 德姆萨、杰雷、卡拉-巴莱杰 |
| `partner_presence` | 字符串型 | 3.1% | CRS、FHI 360、UNFPA、UNICEF、WHO、AAH等 |
| `number` | 整型 | 0.0% | 0.0 ~ 18.0（均值4.9538） |
| `esa_source` | 字符串型 | 0.0% | HDX |
| `esa_processed` | 字符串型 | 0.0% | 2026-04-08 |

---

## 数值型变量统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `number` | 0.0 | 18.0 | 4.9538 | 4.0 |

---

## 数据整理流程

原始数据通过CKAN应用程序编程接口（CKAN API）从HDX平台下载，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。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。采用固定随机种子（42）以80/20的比例将数据集划分为训练集与测试集，并保存为启用Snappy压缩的Parquet文件。

---

## 数据集局限性

- 本数据集原始数据来源于iMMAP Inc.，未经过Electric Sheep Africa的独立验证。
- 自动化清洗流程无法修正原始数据收集阶段的错报值、定义不一致或抽样偏差问题。
- 如需查看发布方提供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/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018)。

---

## 引用

bibtex
@dataset{hdx_africa_north_east_nigeria_health_sector_operational_presence_by_lga_as_of_june_2018,
title = {2018年6月尼日利亚东北部各地方政府区域卫生部门运营现状},
author = {iMMAP Inc.},
year = {2024},
url = {https://data.humdata.org/dataset/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018},
note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习任务}
}

---

*[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。*