electricsheepafrica/africa-ago-views-conflict-forecasts

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-sa-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
- conflict-violence
- fatalities
- forecasting
- hxl
- ago
pretty_name: "Angola - VIEWS conflict forecasts"
dataset_info:
splits:
- name: train
num_examples: 28
- name: test
num_examples: 7
---

# Angola - VIEWS conflict forecasts

**Publisher:** Violence & Impacts Early-Warning System · **Source:** [HDX](https://data.humdata.org/dataset/ago-views-conflict-forecasts) · **License:** `cc-by-sa` · **Updated:** 2026-04-01

---

## Abstract

The Violence & Impacts Early-Warning System (VIEWS) is an award-winning conflict prediction system that generates monthly forecasts for violent conflicts across the world up to three years in advance. It is supported by the iterative research and development activities undertaken by the VIEWS consortium.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-01. Geographic scope: **AGO**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Conflict and security |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 36 |
| **Columns** | 12 (8 numeric, 4 categorical, 0 datetime) |
| **Train split** | 28 rows |
| **Test split** | 7 rows |
| **Geographic scope** | AGO |
| **Publisher** | Violence & Impacts Early-Warning System |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country_id` (range 165.0–165.0), `isoab` (AGO), `year` (range 2026.0–2029.0).

**Temporal** — `month_id` (range 555.0–590.0), `month` (range 1.0–12.0).

**Identifier / Metadata** — `name` (Angola), `gwcode` (range 540.0–540.0), `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `main_mean_ln` (range 0.12–0.2763), `main_mean` (range 0.1275–0.3183), `main_dich` (range 0.0–0.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ago-views-conflict-forecasts")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_id` | int64 | 0.0% | 165.0 – 165.0 (mean 165.0) |
| `month_id` | int64 | 0.0% | 555.0 – 590.0 (mean 572.5) |
| `name` | object | 0.0% | Angola |
| `gwcode` | int64 | 0.0% | 540.0 – 540.0 (mean 540.0) |
| `isoab` | object | 0.0% | AGO |
| `year` | int64 | 0.0% | 2026.0 – 2029.0 (mean 2027.1667) |
| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.5) |
| `main_mean_ln` | float64 | 0.0% | 0.12 – 0.2763 (mean 0.1718) |
| `main_mean` | float64 | 0.0% | 0.1275 – 0.3183 (mean 0.1885) |
| `main_dich` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `country_id` | 165.0 | 165.0 | 165.0 | 165.0 |
| `month_id` | 555.0 | 590.0 | 572.5 | 572.5 |
| `gwcode` | 540.0 | 540.0 | 540.0 | 540.0 |
| `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 |
| `month` | 1.0 | 12.0 | 6.5 | 6.5 |
| `main_mean_ln` | 0.12 | 0.2763 | 0.1718 | 0.1558 |
| `main_mean` | 0.1275 | 0.3183 | 0.1885 | 0.1686 |
| `main_dich` | 0.0 | 0.0 | 0.0 | 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`. 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 Violence & Impacts Early-Warning System 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/ago-views-conflict-forecasts) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ago_views_conflict_forecasts,
title = {Angola - VIEWS conflict forecasts},
author = {Violence & Impacts Early-Warning System},
year = {2026},
url = {https://data.humdata.org/dataset/ago-views-conflict-forecasts},
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:
- 采集所得
language:
- 英语
license: cc-by-sa-4.0
multilinguality:
- 单语言
size_categories:
- 少于1000条
source_datasets:
- 原创数据集
task_categories:
- 表格分类
- 表格回归
task_ids: []
tags:
- 非洲
- 人道主义
- HDX
- Electric Sheep Africa
- 冲突暴力
- 伤亡
- 预测
- HXL
- AGO
pretty_name: "安哥拉——VIEWS冲突预测数据集"
dataset_info:
splits:
- name: train
num_examples: 28
- name: test
num_examples: 7

# 安哥拉——VIEWS冲突预测数据集

**发布方：** 暴力与影响早期预警系统 · **来源：** [人道主义数据交换（HDX）](https://data.humdata.org/dataset/ago-views-conflict-forecasts) · **许可协议：** `cc-by-sa` · **更新日期：** 2026-04-01

---

## 摘要

暴力与影响早期预警系统（Violence & Impacts Early-Warning System，简称VIEWS）是一款屡获殊荣的冲突预测系统，可针对全球范围内的暴力冲突生成最长提前三年的月度预测，其研发支撑来自VIEWS联盟开展的迭代研究与开发工作。

本数据集的每一行均代表国家级聚合数据。数据最近一次在HDX平台更新的时间为2026年4月1日。地理覆盖范围：**AGO（安哥拉）**。

*本数据集由[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** | 冲突与安全 |
| **观测单元** | 国家级聚合数据 |
| **总数据行数** | 36 |
| **列数** | 12（8个数值型、4个分类型、0个日期时间型） |
| **训练集行数** | 28 |
| **测试集行数** | 7 |
| **地理覆盖范围** | AGO（安哥拉） |
| **发布方** | 暴力与影响早期预警系统 |
| **HDX最后更新时间** | 2026-04-01 |

---

## 变量

**地理类变量** — `country_id`（取值范围165.0–165.0）、`isoab`（AGO）、`year`（取值范围2026.0–2029.0）。
**时间类变量** — `month_id`（取值范围555.0–590.0）、`month`（取值范围1.0–12.0）。
**标识符/元数据类变量** — `name`（安哥拉）、`gwcode`（取值范围540.0–540.0）、`esa_source`（HDX）、`esa_processed`（2026-04-07）。
**其他变量** — `main_mean_ln`（取值范围0.12–0.2763）、`main_mean`（取值范围0.1275–0.3183）、`main_dich`（取值范围0.0–0.0）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ago-views-conflict-forecasts")
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 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `country_id` | int64 | 0.0% | 165.0 – 165.0（均值165.0） |
| `month_id` | int64 | 0.0% | 555.0 – 590.0（均值572.5） |
| `name` | object | 0.0% | 安哥拉 |
| `gwcode` | int64 | 0.0% | 540.0 – 540.0（均值540.0） |
| `isoab` | object | 0.0% | AGO |
| `year` | int64 | 0.0% | 2026.0 – 2029.0（均值2027.1667） |
| `month` | int64 | 0.0% | 1.0 – 12.0（均值6.5） |
| `main_mean_ln` | float64 | 0.0% | 0.12 – 0.2763（均值0.1718） |
| `main_mean` | float64 | 0.0% | 0.1275 – 0.3183（均值0.1885） |
| `main_dich` | float64 | 0.0% | 0.0 – 0.0（均值0.0） |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `country_id` | 165.0 | 165.0 | 165.0 | 165.0 |
| `month_id` | 555.0 | 590.0 | 572.5 | 572.5 |
| `gwcode` | 540.0 | 540.0 | 540.0 | 540.0 |
| `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 |
| `month` | 1.0 | 12.0 | 6.5 | 6.5 |
| `main_mean_ln` | 0.12 | 0.2763 | 0.1718 | 0.1558 |
| `main_mean` | 0.1275 | 0.3183 | 0.1885 | 0.1686 |
| `main_dich` | 0.0 | 0.0 | 0.0 | 0.0 |

---

## 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。列名均转为小写并标准化为蛇形命名法。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按照80/20的比例划分为训练集与测试集，并以Snappy压缩格式的Parquet文件保存。

---

## 局限性说明

- 本数据源自暴力与影响早期预警系统，尚未经Electric Sheep Africa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。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/ago-views-conflict-forecasts)获取发布方提供的方法论说明与免责条款。

---

## 引用格式

bibtex
@dataset{hdx_africa_ago_views_conflict_forecasts,
title = {安哥拉——VIEWS冲突预测数据集},
author = {暴力与影响早期预警系统},
year = {2026},
url = {https://data.humdata.org/dataset/ago-views-conflict-forecasts},
note = {由Electric Sheep Africa重新打包以适配机器学习（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。*