electricsheepafrica/africa-mrt-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 - mrt pretty_name: "Mauritania - VIEWS conflict forecasts" dataset_info: splits: - name: train num_examples: 28 - name: test num_examples: 7 --- # Mauritania - VIEWS conflict forecasts **Publisher:** Violence & Impacts Early-Warning System · **Source:** [HDX](https://data.humdata.org/dataset/mrt-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: **MRT**. *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** | MRT | | **Publisher** | Violence & Impacts Early-Warning System | | **HDX last updated** | 2026-04-01 | --- ## Variables **Geographic** — `country_id` (range 244.0–244.0), `isoab` (MRT), `year` (range 2026.0–2029.0). **Temporal** — `month_id` (range 555.0–590.0), `month` (range 1.0–12.0). **Identifier / Metadata** — `name` (Mauritania), `gwcode` (range 435.0–435.0), `esa_source` (HDX), `esa_processed` (2026-04-06). **Other** — `main_mean_ln` (range 0.0331–0.1447), `main_mean` (range 0.0336–0.1557), `main_dich` (range 0.0–0.0). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mrt-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% | 244.0 – 244.0 (mean 244.0) | | `month_id` | int64 | 0.0% | 555.0 – 590.0 (mean 572.5) | | `name` | object | 0.0% | Mauritania | | `gwcode` | int64 | 0.0% | 435.0 – 435.0 (mean 435.0) | | `isoab` | object | 0.0% | MRT | | `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% | 0.0331 – 0.1447 (mean 0.094) | | `main_mean` | float64 | 0.0% | 0.0336 – 0.1557 (mean 0.0992) | | `main_dich` | float64 | 0.0% | 0.0 – 0.0 (mean 0.0) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `country_id` | 244.0 | 244.0 | 244.0 | 244.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 435.0 | 435.0 | 435.0 | 435.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 0.0331 | 0.1447 | 0.094 | 0.0968 | | `main_mean` | 0.0336 | 0.1557 | 0.0992 | 0.1018 | | `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/mrt-views-conflict-forecasts) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_mrt_views_conflict_forecasts, title = {Mauritania - VIEWS conflict forecasts}, author = {Violence & Impacts Early-Warning System}, year = {2026}, url = {https://data.humdata.org/dataset/mrt-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（no-annotation） language_creators: - 公开采集（found） language: - 英语（en） license: cc-by-sa-4.0 multilinguality: - 单语言（monolingual） size_categories: - 样本量小于1000（n<1K） source_datasets: - 原始数据集（original） task_categories: - 表格分类（tabular-classification） - 表格回归（tabular-regression） task_ids: [] tags: - 非洲（africa） - 人道主义（humanitarian） - 人道主义数据交换（HDX，Humanitarian Data Exchange） - 非洲电羊（Electric Sheep Africa） - 冲突与暴力（conflict-violence） - 伤亡人数（fatalities） - 预测（forecasting） - 人道主义交换语言（HXL，Humanitarian eXchange Language） - 毛里塔尼亚（MRT，ISO 3166国家代码） pretty_name: "毛里塔尼亚——VIEWS冲突预测数据集" dataset_info: splits: - name: train num_examples: 28 - name: test num_examples: 7 # 毛里塔尼亚——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](https://data.humdata.org/dataset/mrt-views-conflict-forecasts) · **许可协议**：`cc-by-sa` · **更新时间**：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，可最长提前三年生成全球范围内暴力冲突的月度预测报告，其研发依托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2026-04-01。地理覆盖范围：**毛里塔尼亚（MRT）**。 *本数据集已由[非洲电羊（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **研究领域** | 冲突与安全 | | **观测单元** | 国家级聚合数据 | | **总样本行数** | 36 | | **列数** | 12（其中8列为数值型，4列为分类型，无日期时间列） | | **训练集样本量** | 28行 | | **测试集样本量** |7行 | | **地理覆盖范围** | MRT（毛里塔尼亚） | | **发布方** | 暴力与影响早期预警系统 | | **HDX平台最后更新时间** | 2026-04-01 | --- ## 变量说明 ### 地理类变量 `country_id`（取值范围244.0–244.0）、`isoab`（MRT）、`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`（毛里塔尼亚）、`gwcode`（取值范围435.0–435.0）、`esa_source`（HDX）、`esa_processed`（2026-04-06）。 ### 其他变量 `main_mean_ln`（取值范围0.0331–0.1447）、`main_mean`（取值范围0.0336–0.1557）、`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-mrt-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 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `country_id` | int64 | 0.0% | 244.0 – 244.0（均值244.0） | | `month_id` | int64 | 0.0% | 555.0 – 590.0（均值572.5） | | `name` | object | 0.0% | 毛里塔尼亚 | | `gwcode` | int64 | 0.0% | 435.0 – 435.0（均值435.0） | | `isoab` | object | 0.0% | MRT | | `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% | 0.0331 – 0.1447（均值0.094） | | `main_mean` | float64 | 0.0% | 0.0336 – 0.1557（均值0.0992） | | `main_dich` | float64 | 0.0% | 0.0 – 0.0（均值0.0） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## 数值型变量汇总统计 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `country_id` | 244.0 | 244.0 | 244.0 | 244.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 435.0 | 435.0 | 435.0 | 435.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 0.0331 | 0.1447 | 0.094 | 0.0968 | | `main_mean` | 0.0336 | 0.1557 | 0.0992 | 0.1018 | | `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。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，并保存为Snappy压缩格式的Parquet文件。 --- ## 局限性说明 - 本数据集源自暴力与影响早期预警系统，未经过非洲电羊（ESA）的独立验证。 - 自动化清洗流程无法修正原始数据采集阶段的错报、定义不一致或采样偏差问题。 - 如需了解发布方的方法论说明与注意事项，请参阅[HDX原始数据集页面](https://data.humdata.org/dataset/mrt-views-conflict-forecasts)。 --- ## 引用格式 bibtex @dataset{hdx_africa_mrt_views_conflict_forecasts, title = {毛里塔尼亚——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/mrt-views-conflict-forecasts}, note = {由非洲电羊（Electric Sheep Africa）重新打包以适配机器学习应用（https://huggingface.co/electricsheepafrica)} } --- *[非洲电羊（Electric Sheep Africa）](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施平台，尼日利亚拉各斯。*