electricsheepafrica/africa-mus-ibtracs-tropical-storm-tracks
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--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - cyclones-hurricanes-typhoons - hxl - mus pretty_name: "Mauritius: IBTrACS Storm Tracks" dataset_info: splits: - name: train num_examples: 91810 - name: test num_examples: 22952 --- # Mauritius: IBTrACS Storm Tracks **Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/mus-ibtracs-tropical-storm-tracks) · **License:** `cc-by-igo` · **Updated:** 2026-03-03 --- ## Abstract The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency. Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the `iso_time` column(s). Geographic scope: **MUS**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Climate and environment | | **Unit of observation** | Geolocated point observations | | **Rows (total)** | 114,763 | | **Columns** | 12 (3 numeric, 8 categorical, 1 datetime) | | **Train split** | 91,810 rows | | **Test split** | 22,952 rows | | **Geographic scope** | MUS | | **Publisher** | HDX | | **HDX last updated** | 2026-03-03 | --- ## Variables **Geographic** — `iso_time`, `lat` (range -47.4–9.4), `lon` (range 11.3–139.5). **Outcome / Measurement** — `number` (range 1.0–145.0). **Identifier / Metadata** — `sid` (1997013S08101, 2023036S12117, 2000032S11116), `esa_source` (HDX), `esa_processed` (2026-04-06). **Other** — `basin` (South Indian, North India, South Pacific), `subbasin` (Missing, Western Australia, Arabian Sea), `nature` (Tropical, Not reported, Mixture(contradicting report from different agencies)), `wmo_wind` ( , 25, 20), `wmo_pres` ( , 1000, 997). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mus-ibtracs-tropical-storm-tracks") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `sid` | object | 0.0% | 1997013S08101, 2023036S12117, 2000032S11116 | | `number` | float64 | 0.0% | 1.0 – 145.0 (mean 28.6705) | | `basin` | object | 0.0% | South Indian, North India, South Pacific | | `subbasin` | object | 0.0% | Missing, Western Australia, Arabian Sea | | `iso_time` | datetime64[ns] | 0.0% | | | `nature` | object | 0.0% | Tropical, Not reported, Mixture(contradicting report from different agencies) | | `lat` | float64 | 0.0% | -47.4 – 9.4 (mean -17.9559) | | `lon` | float64 | 0.0% | 11.3 – 139.5 (mean 62.6052) | | `wmo_wind` | object | 0.0% | , 25, 20 | | `wmo_pres` | object | 0.0% | , 1000, 997 | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-06 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `number` | 1.0 | 145.0 | 28.6705 | 12.0 | | `lat` | -47.4 | 9.4 | -17.9559 | -17.3 | | `lon` | 11.3 | 139.5 | 62.6052 | 60.7 | --- ## 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`. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 HDX 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/mus-ibtracs-tropical-storm-tracks) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_mus_ibtracs_tropical_storm_tracks, title = {Mauritius: IBTrACS Storm Tracks}, author = {HDX}, year = {2026}, url = {https://data.humdata.org/dataset/mus-ibtracs-tropical-storm-tracks}, 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: 知识共享署名4.0（CC BY 4.0） multilinguality: - 单语言 size_categories: - 10万条至100万条 source_datasets: - 原创 task_categories: - 其他 task_ids: [] tags: - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 气旋、飓风、台风 - HXL - MUS pretty_name: "毛里求斯：IBTrACS风暴路径" dataset_info: splits: - name: train num_examples: 91810 - name: test num_examples: 22952 # 毛里求斯：IBTrACS风暴路径 **发布方：** HDX · **来源：** [HDX](https://data.humdata.org/dataset/mus-ibtracs-tropical-storm-tracks) · **许可证：** `cc-by-igo` · **最后更新：** 2026-03-03 --- ## 摘要 国际气候管理最佳路径档案（IBTrACS，International Best Track Archive for Climate Stewardship）项目是目前可获取的最完整的全球热带气旋数据集集合。该项目整合了来自多家机构的最新及历史热带气旋数据，构建了统一的公开最佳路径数据集，可提升不同机构间的数据对比效率。 可用字段： SID：由IBTrACS算法分配的唯一风暴标识符（SID） ISO_TIME：采用ISO格式（YYYY-MM-DD hh:mm:ss）记录的观测时间 BASIN：当前风暴所处的洋盆 SUBBASIN：当前风暴所处的次级洋盆 NATURE：风暴类型（整合所有可用来源的各类风暴类型） NUMBER：当年的风暴编号（每年从1重新开始计数） LAT：平均位置纬度（整合所有可用观测位置计算所得） LON：平均位置经度（整合所有可用观测位置计算所得） WMO_WIND：由世界气象组织（WMO，World Meteorological Organization）指定责任机构认定的最大持续风速 WMO_PRES：由WMO指定责任机构认定的最低中心气压。 本数据集的每一行均代表一个带地理定位的点观测数据。时间覆盖范围由`iso_time`字段标注。地理覆盖范围：**MUS（毛里求斯国家代码）**。 *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** | 气候与环境 | | **观测单元** | 带地理定位的点观测数据 | | **总数据行数** | 114763条 | | **字段数** | 12个（3个数值型、8个分类型、1个日期时间型） | | **训练集划分** | 91810条 | | **测试集划分** | 22952条 | | **地理覆盖范围** | MUS（毛里求斯国家代码） | | **发布方** | HDX | | **HDX最后更新时间** | 2026-03-03 | --- ## 字段分类 **地理相关字段**：`iso_time`、`lat`（取值范围：-47.4至9.4）、`lon`（取值范围：11.3至139.5）。 **结果/测量字段**：`number`（取值范围：1.0至145.0）。 **标识符/元数据字段**：`sid`（示例值：1997013S08101、2023036S12117、2000032S11116）、`esa_source`（取值：HDX）、`esa_processed`（处理日期：2026-04-06）。 **其他字段**：`basin`（取值：南印度洋、北印度洋、南太平洋）、`subbasin`（取值：缺失、西澳大利亚海域、阿拉伯海）、`nature`（取值：热带、未报告、混合（不同机构报告存在矛盾））、`wmo_wind`（示例值：空、25、20）、`wmo_pres`（示例值：空、1000、997）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-mus-ibtracs-tropical-storm-tracks") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 字段Schema | 字段名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `sid` | 对象类型 | 0.0% | 示例值：1997013S08101、2023036S12117、2000032S11116 | | `number` | 浮点型 | 0.0% | 取值范围：1.0至145.0（均值：28.6705） | | `basin` | 对象类型 | 0.0% | 取值：南印度洋、北印度洋、南太平洋 | | `subbasin` | 对象类型 | 0.0% | 取值：缺失、西澳大利亚海域、阿拉伯海 | | `iso_time` | 日期时间型 | 0.0% | 无示例 | | `nature` | 对象类型 | 0.0% | 取值：热带、未报告、混合（不同机构报告存在矛盾） | | `lat` | 浮点型 | 0.0% | 取值范围：-47.4至9.4（均值：-17.9559） | | `lon` | 浮点型 | 0.0% | 取值范围：11.3至139.5（均值：62.6052） | | `wmo_wind` | 对象类型 | 0.0% | 示例值：空、25、20 | | `wmo_pres` | 对象类型 | 0.0% | 示例值：空、1000、997 | | `esa_source` | 对象类型 | 0.0% | 取值：HDX | | `esa_processed` | 对象类型 | 0.0% | 取值：2026-04-06 | --- ## 数值型字段统计 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `number` | 1.0 | 145.0 | 28.6705 | 12.0 | | `lat` | -47.4 | 9.4 | -17.9559 | -17.3 | | `lon` | 11.3 | 139.5 | 62.6052 | 60.7 | --- ## 数据整理流程 原始数据通过CKAN API从HDX下载并转换为Parquet格式。字段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`。根据解析成功率（阈值>85%），将4个字段从字符串类型转换为数值型或日期时间型。采用固定随机种子（42）将数据集按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。 --- ## 局限性说明 - 数据源自HDX，尚未经Electric Sheep Africa独立验证。 - 自动化清洗无法修正原始数据集中的错误报告值、定义不一致问题或采样偏差。 - 请查阅[HDX原始数据集页面](https://data.humdata.org/dataset/mus-ibtracs-tropical-storm-tracks)以获取发布方提供的方法说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_mus_ibtracs_tropical_storm_tracks, title = {Mauritius: IBTrACS Storm Tracks}, author = {HDX}, year = {2026}, url = {https://data.humdata.org/dataset/mus-ibtracs-tropical-storm-tracks}, note = {Repackaged for machine learning by Electric Sheep Africa (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。*
