electricsheepafrica/africa-rwa-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 - rwa pretty_name: "Rwanda - VIEWS conflict forecasts" dataset_info: splits: - name: train num_examples: 28 - name: test num_examples: 7 --- # Rwanda - VIEWS conflict forecasts **Publisher:** Violence & Impacts Early-Warning System · **Source:** [HDX](https://data.humdata.org/dataset/rwa-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: **RWA**. *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** | RWA | | **Publisher** | Violence & Impacts Early-Warning System | | **HDX last updated** | 2026-04-01 | --- ## Variables **Geographic** — `country_id` (range 156.0–156.0), `isoab` (RWA), `year` (range 2026.0–2029.0). **Temporal** — `month_id` (range 555.0–590.0), `month` (range 1.0–12.0). **Identifier / Metadata** — `name` (Rwanda), `gwcode` (range 517.0–517.0), `esa_source` (HDX), `esa_processed` (2026-04-08). **Other** — `main_mean_ln` (range 0.1339–0.5672), `main_mean` (range 0.1433–0.7634), `main_dich` (range 0.0–0.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-rwa-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% | 156.0 – 156.0 (mean 156.0) | | `month_id` | int64 | 0.0% | 555.0 – 590.0 (mean 572.5) | | `name` | object | 0.0% | Rwanda | | `gwcode` | int64 | 0.0% | 517.0 – 517.0 (mean 517.0) | | `isoab` | object | 0.0% | RWA | | `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% | 0.1339 – 0.5672 (mean 0.3196) | | `main_mean` | float64 | 0.0% | 0.1433 – 0.7634 (mean 0.3862) | | `main_dich` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-08 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `country_id` | 156.0 | 156.0 | 156.0 | 156.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 517.0 | 517.0 | 517.0 | 517.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 0.1339 | 0.5672 | 0.3196 | 0.3232 | | `main_mean` | 0.1433 | 0.7634 | 0.3862 | 0.3816 | | `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/rwa-views-conflict-forecasts) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_rwa_views_conflict_forecasts, title = {Rwanda - VIEWS conflict forecasts}, author = {Violence & Impacts Early-Warning System}, year = {2026}, url = {https://data.humdata.org/dataset/rwa-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: - 无标注 language_creators: - 现有公开资源采集 language: - 英语 license: cc-by-sa-4.0 multilinguality: - 单语言 size_categories: - 样本量小于1000 source_datasets: - 原始数据集 task_categories: - 表格分类 - 表格 regression task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange） - Electric Sheep Africa - 冲突暴力 - 伤亡统计 - 预测 - HXL（Humanitarian Exchange Language） - 卢旺达（RWA） pretty_name: "卢旺达——VIEWS冲突预测数据集" dataset_info: splits: - name: train num_examples: 28 - name: test num_examples: 7 --- # 卢旺达——VIEWS冲突预测数据集 **发布方：** 暴力与影响早期预警系统（Violence & Impacts Early-Warning System，VIEWS） · **来源：** [HDX（Humanitarian Data Exchange）](https://data.humdata.org/dataset/rwa-views-conflict-forecasts) · **许可证：** `cc-by-sa` · **最后更新：** 2026-04-01 --- ## 摘要 暴力与影响早期预警系统（VIEWS）是一款屡获殊荣的冲突预测系统，可提前3年为全球范围内的暴力冲突生成月度预测报告。该系统的研发得到了VIEWS联盟开展的迭代研究与开发活动的支持。 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：**卢旺达（RWA）**。 *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（Parquet）。* --- ## 数据集特征 | 项目 | 详情 | |---|---| | **应用领域** | 冲突与安全 | | **观测单元** | 国家级聚合数据 | | **总行数** | 36 | | **列数** | 12（8个数值型、4个分类型、0个日期时间型） | | **训练集划分** | 28条样本 | | **测试集划分** | 7条样本 | | **地理覆盖范围** | 卢旺达（RWA） | | **发布方** | 暴力与影响早期预警系统（VIEWS） | | **HDX平台最后更新时间** | 2026年4月1日 | --- ## 变量说明 **地理相关变量** — `country_id`（取值范围156.0–156.0）、`isoab`（RWA）、`year`（取值范围2026.0–2029.0）。 **时间相关变量** — `month_id`（取值范围555.0–590.0）、`month`（取值范围1.0–12.0）。 **标识符/元数据变量** — `name`（卢旺达）、`gwcode`（取值范围517.0–517.0）、`esa_source`（HDX）、`esa_processed`（2026-04-08）。 **其他变量** — `main_mean_ln`（取值范围0.1339–0.5672）、`main_mean`（取值范围0.1433–0.7634）、`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-rwa-views-conflict-forecasts") 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/样本值 | |---|---|---|---| | `country_id` | int64 | 0.0% | 156.0 – 156.0（均值156.0） | | `month_id` | int64 | 0.0% | 555.0 – 590.0（均值572.5） | | `name` | object | 0.0% | 卢旺达 | | `gwcode` | int64 | 0.0% | 517.0 – 517.0（均值517.0） | | `isoab` | object | 0.0% | RWA | | `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% | 0.1339 – 0.5672（均值0.3196） | | `main_mean` | float64 | 0.0% | 0.1433 – 0.7634（均值0.3862） | | `main_dich` | float64 | 0.0% | 0.0 – 0.0（均值0.0） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-08 | --- ## 数值型变量统计汇总 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `country_id` | 156.0 | 156.0 | 156.0 | 156.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 517.0 | 517.0 | 517.0 | 517.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 0.1339 | 0.5672 | 0.3196 | 0.3232 | | `main_mean` | 0.1433 | 0.7634 | 0.3862 | 0.3816 | | `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。列名统一转换为小写并采用蛇形命名法（snake_case）进行标准化。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。本数据集采用固定随机种子（42）以80/20的比例划分为训练集与测试集，并保存为Snappy压缩的Parquet格式文件。 --- ## 局限性说明 - 数据源自暴力与影响早期预警系统（VIEWS），尚未由Electric Sheep Africa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、定义不一致或采样偏差问题。 - 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/rwa-views-conflict-forecasts)获取发布方提供的方法学说明与免责声明。 --- ## 引用格式 bibtex @dataset{hdx_africa_rwa_views_conflict_forecasts, title = {Rwanda - VIEWS conflict forecasts}, author = {Violence & Impacts Early-Warning System}, year = {2026}, url = {https://data.humdata.org/dataset/rwa-views-conflict-forecasts}, note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)} } --- *[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲的机器学习数据集基础设施提供商。尼日利亚拉各斯。*