electricsheepafrica/africa-south-sudan-current-situation-fewsnet-ipc-classification

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - tabular-classification - tabular-regression task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - food-security - ssd pretty_name: "South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data" dataset_info: splits: - name: train num_examples: 3112 - name: test num_examples: 778 --- # South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/south_sudan_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-07 --- ## Abstract South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011 Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `projection_start`, `projection_end` column(s). Geographic scope: **SSD**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Food security and nutrition | | **Unit of observation** | First-level administrative unit observations | | **Rows (total)** | 3,891 | | **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) | | **Train split** | 3,112 rows | | **Test split** | 778 rows | | **Geographic scope** | SSD | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-04-07 | --- ## Variables **Geographic** — `country` (South Sudan), `country_code` (SS), `fewsnet_region` (East Africa), `unit_type` (fsc_admin), `specialization_type` and 2 others. **Temporal** — `datacollectionperiod` (range 158825.0–377939.0), `reporting_date`. **Outcome / Measurement** — `value` (range 1.0–5.0). **Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Security Outlook, South Sudan), `geographic_unit_full_name` (Abiemnhom, Unity, South Sudan, Abyei, Warrap, South Sudan, Raga, Western Bahr el Ghazal, South Sudan), `geographic_unit_name` (Abiemnhom, Abyei, Raga), `fnid` (SS2011C11301, SS2011C13302, SS2011C11105) and 8 others. **Other** — `geographic_group` (Eastern Africa), `classification_scale`, `is_allowing_for_assistance`, `projection_start`, `projection_end` and 12 others. --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-south-sudan-current-situation-fewsnet-ipc-classification") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `source_organization` | object | 0.0% | FEWS NET | | `source_document` | object | 0.0% | Food Security Outlook, South Sudan | | `country` | object | 0.0% | South Sudan | | `country_code` | object | 0.0% | SS | | `geographic_group` | object | 0.0% | Eastern Africa | | `fewsnet_region` | object | 0.0% | East Africa | | `geographic_unit_full_name` | object | 0.0% | Abiemnhom, Unity, South Sudan, Abyei, Warrap, South Sudan, Raga, Western Bahr el Ghazal, South Sudan | | `geographic_unit_name` | object | 0.0% | Abiemnhom, Abyei, Raga | | `unit_type` | object | 0.0% | fsc_admin | | `fnid` | object | 0.0% | SS2011C11301, SS2011C13302, SS2011C11105 | | `classification_scale` | object | 0.0% | | | `scenario_name` | object | 0.0% | | | `preference_rating` | int64 | 0.0% | 90.0 – 90.0 (mean 90.0) | | `is_allowing_for_assistance` | bool | 0.0% | | | `projection_start` | datetime64[ns] | 0.0% | | | `projection_end` | datetime64[ns] | 0.0% | | | `status` | object | 0.0% | | | `value` | float64 | 2.1% | 1.0 – 5.0 (mean 2.6034) | | `description` | object | 2.1% | | | `id` | int64 | 0.0% | 24403005.0 – 41425124.0 (mean 27079974.2984) | | `datacollectionperiod` | int64 | 0.0% | 158825.0 – 377939.0 (mean 207229.8949) | | `datacollection` | int64 | 0.0% | 168447.0 – 388765.0 (mean 218009.3279) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 27032.0 – 29864.0 (mean 27644.0995) | | `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) | | `datasourcedocument` | int64 | 0.0% | 6585.0 – 6585.0 (mean 6585.0) | | `dataseries` | int64 | 0.0% | 6495678.0 – 7420829.0 (mean 6574609.8861) | | `dataseries_name` | object | 0.0% | | | `specialization_type` | object | 0.0% | | | `dataseries_specialization_type` | object | 0.0% | | | `data_usage_policy` | object | 0.0% | | | `created` | datetime64[ns] | 0.0% | | | `modified` | datetime64[ns] | 0.0% | | | `status_changed` | datetime64[ns] | 0.0% | | | `collection_status` | object | 0.0% | | | `collection_status_changed` | datetime64[ns] | 0.0% | | | `collection_schedule` | object | 0.0% | | | `reporting_date` | datetime64[ns] | 0.0% | | | `esa_source` | object | 0.0% | | | `esa_processed` | object | 0.0% | | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 | | `value` | 1.0 | 5.0 | 2.6034 | 3.0 | | `id` | 24403005.0 | 41425124.0 | 27079974.2984 | 24536313.0 | | `datacollectionperiod` | 158825.0 | 377939.0 | 207229.8949 | 159803.0 | | `datacollection` | 168447.0 | 388765.0 | 218009.3279 | 168797.0 | | `geographic_unit` | 27032.0 | 29864.0 | 27644.0995 | 27081.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6585.0 | 6585.0 | 6585.0 | 6585.0 | | `dataseries` | 6495678.0 | 7420829.0 | 6574609.8861 | 6511351.0 | --- ## 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`. 3 column(s) with >80% missing values were removed: `pct_phase3`, `pct_phase4`, `pct_phase5`. 7 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 FEWS NET 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/south_sudan_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_south_sudan_current_situation_fewsnet_ipc_classification, title = {South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/south_sudan_current_situation_fewsnet_ipc_classification}, 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（no-annotation） language_creators: - 现成获取（found） language: - 英语（en） license: CC BY 4.0（知识共享署名4.0国际许可协议） multilinguality: - 单语言（monolingual） size_categories: - 1000 < n < 10000 source_datasets: - 原生数据集（original） task_categories: - 表格分类（tabular-classification） - 表格回归（tabular-regression） task_ids: [] tags: - 非洲 - 人道主义 - HDX（Humanitarian Data Exchange） - Electric Sheep Africa - 粮食安全 - SSD（南苏丹国家代码） pretty_name: "南苏丹现状：FEWS NET（饥荒预警系统网络）急性粮食不安全分类数据" dataset_info: splits: - name: train num_examples: 3112 - name: test num_examples: 778 # 南苏丹现状：FEWS NET 急性粮食不安全分类数据 **发布方**：FEWS NET（饥荒预警系统网络） · **来源**：[HDX（人道主义数据交换，Humanitarian Data Exchange）](https://data.humdata.org/dataset/south_sudan_current_situation_fewsnet_ipc_classification) · **许可证**：`CC BY` · **更新时间**：2026-04-07 --- ## 摘要 本数据集为2011年南苏丹现状：FEWS NET急性粮食不安全分类数据。 数据集中每一行代表一级行政单元的观测记录。时间覆盖范围由`projection_start`（预测开始时间）、`projection_end`（预测结束时间）列标注。地理覆盖范围：**SSD（南苏丹国家代码）**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet（Parquet格式）。* --- ## 数据集特征 | | | |---|---| | **研究领域** | 粮食安全与营养 | | **观测单元** | 一级行政单元观测记录 | | **总样本行数** | 3891条 | | **列数** | 40列（9个数值型、23个分类型、7个日期时间型） | | **训练集划分** | 3112条 | | **测试集划分** | 778条 | | **地理覆盖范围** | SSD（南苏丹） | | **发布方** | FEWS NET | | **HDX最后更新时间** | 2026-04-07 | --- ## 变量分类 ### 地理类变量 `country`（国家：南苏丹）、`country_code`（国家代码：SS）、`fewsnet_region`（FEWS NET区域：东非）、`unit_type`（单元类型：fsc_admin）、`specialization_type`（专业类型）及另外2个变量。 ### 时间类变量 `datacollectionperiod`（数据收集时段：取值范围158825.0~377939.0）、`reporting_date`（报告日期）。 ### 结果/测量类变量 `value`（取值范围1.0~5.0）。 ### 标识符/元数据类变量 `source_organization`（来源机构：FEWS NET）、`source_document`（来源文档：《南苏丹粮食安全展望》）、`geographic_unit_full_name`（地理单元完整名称：阿比耶姆霍姆、团结州、南苏丹；阿卜耶伊、瓦拉普州、南苏丹；拉加、西加扎勒河州、南苏丹）、`geographic_unit_name`（地理单元简称：阿比耶姆霍姆、阿卜耶伊、拉加）、`fnid`（标识符：SS2011C11301、SS2011C13302、SS2011C11105）及另外8个变量。 ### 其他类变量 `geographic_group`（地理群组：东非）、`classification_scale`（分类标准）、`is_allowing_for_assistance`（是否允许援助）、`projection_start`（预测开始时间）、`projection_end`（预测结束时间）及另外12个变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-south-sudan-current-situation-fewsnet-ipc-classification") 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 | |---|---|---|---| | `source_organization` | 字符串（object） | 0.0% | FEWS NET | | `source_document` | 字符串（object） | 0.0% | 《南苏丹粮食安全展望》 | | `country` | 字符串（object） | 0.0% | 南苏丹 | | `country_code` | 字符串（object） | 0.0% | SS | | `geographic_group` | 字符串（object） | 0.0% | 东非 | | `fewsnet_region` | 字符串（object） | 0.0% | 东非 | | `geographic_unit_full_name` | 字符串（object） | 0.0% | 阿比耶姆霍姆、团结州、南苏丹；阿卜耶伊、瓦拉普州、南苏丹；拉加、西加扎勒河州、南苏丹 | | `geographic_unit_name` | 字符串（object） | 0.0% | 阿比耶姆霍姆、阿卜耶伊、拉加 | | `unit_type` | 字符串（object） | 0.0% | fsc_admin | | `fnid` | 字符串（object） | 0.0% | SS2011C11301、SS2011C13302、SS2011C11105 | | `classification_scale` | 字符串（object） | 0.0% | 无 | | `scenario_name` | 字符串（object） | 0.0% | 无 | | `preference_rating` | 整数型（int64） | 0.0% | 90.0 – 90.0（均值为90.0） | | `is_allowing_for_assistance` | 布尔型（bool） | 0.0% | 无 | | `projection_start` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `projection_end` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `status` | 字符串（object） | 0.0% | 无 | | `value` | 浮点型（float64） | 2.1% | 1.0 – 5.0（均值为2.6034） | | `description` | 字符串（object） | 2.1% | 无 | | `id` | 整数型（int64） | 0.0% | 24403005.0 – 41425124.0（均值为27079974.2984） | | `datacollectionperiod` | 整数型（int64） | 0.0% | 158825.0 – 377939.0（均值为207229.8949） | | `datacollection` | 整数型（int64） | 0.0% | 168447.0 – 388765.0（均值为218009.3279） | | `scenario` | 字符串（object） | 0.0% | 无 | | `geographic_unit` | 整数型（int64） | 0.0% | 27032.0 – 29864.0（均值为27644.0995） | | `datasourceorganization` | 整数型（int64） | 0.0% | 1.0 – 1.0（均值为1.0） | | `datasourcedocument` | 整数型（int64） | 0.0% | 6585.0 – 6585.0（均值为6585.0） | | `dataseries` | 整数型（int64） | 0.0% | 6495678.0 – 7420829.0（均值为6574609.8861） | | `dataseries_name` | 字符串（object） | 0.0% | 无 | | `specialization_type` | 字符串（object） | 0.0% | 无 | | `dataseries_specialization_type` | 字符串（object） | 0.0% | 无 | | `data_usage_policy` | 字符串（object） | 0.0% | 无 | | `created` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `modified` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `status_changed` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `collection_status` | 字符串（object） | 0.0% | 无 | | `collection_status_changed` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `collection_schedule` | 字符串（object） | 0.0% | 无 | | `reporting_date` | 日期时间型（datetime64[ns]） | 0.0% | 无 | | `esa_source` | 字符串（object） | 0.0% | 无 | | `esa_processed` | 字符串（object） | 0.0% | 无 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 | | `value` | 1.0 | 5.0 | 2.6034 | 3.0 | | `id` | 24403005.0 | 41425124.0 | 27079974.2984 | 24536313.0 | | `datacollectionperiod` | 158825.0 | 377939.0 | 207229.8949 | 159803.0 | | `datacollection` | 168447.0 | 388765.0 | 218009.3279 | 168797.0 | | `geographic_unit` | 27032.0 | 29864.0 | 27644.0995 | 27081.0 | | `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 | | `datasourcedocument` | 6585.0 | 6585.0 | 6585.0 | 6585.0 | | `dataseries` | 6495678.0 | 7420829.0 | 6574609.8861 | 6511351.0 | --- ## 数据整理流程 原始数据通过CKAN API（CKAN应用程序编程接口）从HDX下载，并转换为Parquet格式。所有列名均转为小写并标准化为蛇形命名法（snake_case）。将常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。移除了3个缺失率超过80%的列：`pct_phase3`、`pct_phase4`、`pct_phase5`。基于解析成功率（阈值>85%），将7个列从字符串类型转换为数值型或日期时间型。本数据集以固定随机种子（42）按80/20比例划分为训练集与测试集，并以Snappy（Snappy压缩格式）压缩格式保存为Parquet文件。 --- ## 数据集局限性 - 本数据源自FEWS NET，未经过Electric Sheep Africa的独立验证。 - 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/south_sudan_current_situation_fewsnet_ipc_classification)获取发布方提供的方法论说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_south_sudan_current_situation_fewsnet_ipc_classification, title = {南苏丹现状：FEWS NET 急性粮食不安全分类数据}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/south_sudan_current_situation_fewsnet_ipc_classification}, note = {由Electric Sheep Africa（https://huggingface.co/electricsheepafrica）重新打包以适配机器学习场景} } --- *[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。*