electricsheepafrica/africa-moz-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 - moz pretty_name: "Mozambique - VIEWS conflict forecasts" dataset_info: splits: - name: train num_examples: 28 - name: test num_examples: 7 --- # Mozambique - VIEWS conflict forecasts **Publisher:** Violence & Impacts Early-Warning System · **Source:** [HDX](https://data.humdata.org/dataset/moz-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: **MOZ**. *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** | MOZ | | **Publisher** | Violence & Impacts Early-Warning System | | **HDX last updated** | 2026-04-01 | --- ## Variables **Geographic** — `country_id` (range 162.0–162.0), `isoab` (MOZ), `year` (range 2026.0–2029.0). **Temporal** — `month_id` (range 555.0–590.0), `month` (range 1.0–12.0). **Identifier / Metadata** — `name` (Mozambique), `gwcode` (range 541.0–541.0), `esa_source` (HDX), `esa_processed` (2026-04-06). **Other** — `main_mean_ln` (range 2.6646–3.1986), `main_mean` (range 13.3628–23.4977), `main_dich` (range 0.0084–0.4312). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-moz-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% | 162.0 – 162.0 (mean 162.0) | | `month_id` | int64 | 0.0% | 555.0 – 590.0 (mean 572.5) | | `name` | object | 0.0% | Mozambique | | `gwcode` | int64 | 0.0% | 541.0 – 541.0 (mean 541.0) | | `isoab` | object | 0.0% | MOZ | | `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% | 2.6646 – 3.1986 (mean 2.8896) | | `main_mean` | float64 | 0.0% | 13.3628 – 23.4977 (mean 17.1241) | | `main_dich` | float64 | 0.0% | 0.0084 – 0.4312 (mean 0.0793) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `country_id` | 162.0 | 162.0 | 162.0 | 162.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 541.0 | 541.0 | 541.0 | 541.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 2.6646 | 3.1986 | 2.8896 | 2.8951 | | `main_mean` | 13.3628 | 23.4977 | 17.1241 | 17.0842 | | `main_dich` | 0.0084 | 0.4312 | 0.0793 | 0.0556 | --- ## 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/moz-views-conflict-forecasts) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_moz_views_conflict_forecasts, title = {Mozambique - VIEWS conflict forecasts}, author = {Violence & Impacts Early-Warning System}, year = {2026}, url = {https://data.humdata.org/dataset/moz-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.*### 数据集元数据 - 注释创建者：无注释 - 语言创建方式：现有资源采集 - 语言：英语 - 许可证：CC BY-SA 4.0 - 多语言属性：单语言 - 数据规模：少于1000条数据 - 源数据集：原创数据集 - 任务类别：表格分类、表格回归 - 任务子类别：无 - 标签：非洲、人道主义、HDX、Electric Sheep Africa、冲突暴力、死亡人数、预测、HXL、莫桑比克（MOZ） - 美观名称：莫桑比克——VIEWS冲突预测数据集 - 数据集信息： - 划分集： - 训练集：28条样本 - 测试集：7条样本 # 莫桑比克——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/moz-views-conflict-forecasts) · **许可证**：`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，可提前3年生成全球范围内暴力冲突的月度预测报告，其研发依托于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。该数据最近一次在HDX平台的更新时间为2026-04-01。地理覆盖范围：**莫桑比克（MOZ）**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为可供机器学习直接使用的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条数据 | | **测试集划分** | 7条数据 | | **地理覆盖范围** | 莫桑比克（MOZ） | | **发布方** | 暴力与影响早期预警系统（VIEWS） | | **HDX平台最后更新时间** | 2026-04-01 | --- ## 变量 **地理相关变量**：`country_id`（取值范围162.0–162.0）、`isoab`（MOZ）、`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）。 **标识符与元数据变量**：`name`（莫桑比克）、`gwcode`（取值范围541.0–541.0）、`esa_source`（HDX）、`esa_processed`（2026-04-06）。 **其他变量**：`main_mean_ln`（取值范围2.6646–3.1986）、`main_mean`（取值范围13.3628–23.4977）、`main_dich`（取值范围0.0084–0.4312）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-moz-views-conflict-forecasts") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据 Schema | 列名 | 数据类型 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `country_id` | int64 | 0.0% | 162.0 – 162.0（均值 162.0） | | `month_id` | int64 | 0.0% | 555.0 – 590.0（均值 572.5） | | `name` | object | 0.0% | 莫桑比克 | | `gwcode` | int64 | 0.0% | 541.0 – 541.0（均值 541.0） | | `isoab` | object | 0.0% | MOZ | | `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% | 2.6646 – 3.1986（均值 2.8896） | | `main_mean` | float64 | 0.0% | 13.3628 – 23.4977（均值 17.1241） | | `main_dich` | float64 | 0.0% | 0.0084 – 0.4312（均值 0.0793） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `country_id` | 162.0 | 162.0 | 162.0 | 162.0 | | `month_id` | 555.0 | 590.0 | 572.5 | 572.5 | | `gwcode` | 541.0 | 541.0 | 541.0 | 541.0 | | `year` | 2026.0 | 2029.0 | 2027.1667 | 2027.0 | | `month` | 1.0 | 12.0 | 6.5 | 6.5 | | `main_mean_ln` | 2.6646 | 3.1986 | 2.8896 | 2.8951 | | `main_mean` | 13.3628 | 23.4977 | 17.1241 | 17.0842 | | `main_dich` | 0.0084 | 0.4312 | 0.0793 | 0.0556 | --- ## 数据整理 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文件存储。 --- ## 局限性说明 - 本数据集源自暴力与影响早期预警系统（VIEWS），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阶段的错报值、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/moz-views-conflict-forecasts)获取发布方提供的方法论说明与免责条款。 --- ## 引用 bibtex @dataset{hdx_africa_moz_views_conflict_forecasts, title = {Mozambique - VIEWS conflict forecasts}, author = {Violence & Impacts Early-Warning System}, year = {2026}, url = {https://data.humdata.org/dataset/moz-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。尼日利亚拉各斯。*