electricsheepafrica/africa-niger-current-situation-fewsnet-ipc-classification
收藏资源简介：
--- 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 - ner pretty_name: "Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data" dataset_info: splits: - name: train num_examples: 5456 - name: test num_examples: 1364 --- # Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/niger_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-03 --- ## Abstract Niger 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: **NER**. *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)** | 6,821 | | **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) | | **Train split** | 5,456 rows | | **Test split** | 1,364 rows | | **Geographic scope** | NER | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-04-03 | --- ## Variables **Geographic** — `country` (Niger), `country_code` (NE), `fewsnet_region` (West Africa), `unit_type` (fsc_admin_lhz), `specialization_type` and 2 others. **Temporal** — `datacollectionperiod` (range 158390.0–377285.0), `reporting_date`. **Outcome / Measurement** — `value` (range 1.0–3.0). **Identifier / Metadata** — `source_organization` (FEWS NET, Niger), `source_document` (Food Security Outlook, Niger), `geographic_unit_full_name` (Agropastoral Belt, Abalak, Tahoua, Niger, Southwestern Cereals with Fan-Palm Products, Gaya, Dosso, Niger, Southern Irrigated Cash Crops, Madaoua, Tahoua, Niger), `geographic_unit_name` (Rainfed Millet and Sorghum Belt, Agropastoral Belt, Transhumant and Nomad Pastoralism), `fnid` (NE2012C3050204, NE2012C3070214, NE2012C3060210) and 8 others. **Other** — `geographic_group` (Western 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-niger-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, Niger | | `source_document` | object | 0.0% | Food Security Outlook, Niger | | `country` | object | 0.0% | Niger | | `country_code` | object | 0.0% | NE | | `geographic_group` | object | 0.0% | Western Africa | | `fewsnet_region` | object | 0.0% | West Africa | | `geographic_unit_full_name` | object | 0.0% | Agropastoral Belt, Abalak, Tahoua, Niger, Southwestern Cereals with Fan-Palm Products, Gaya, Dosso, Niger, Southern Irrigated Cash Crops, Madaoua, Tahoua, Niger | | `geographic_unit_name` | object | 0.0% | Rainfed Millet and Sorghum Belt, Agropastoral Belt, Transhumant and Nomad Pastoralism | | `unit_type` | object | 0.0% | fsc_admin_lhz | | `fnid` | object | 0.0% | NE2012C3050204, NE2012C3070214, NE2012C3060210 | | `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 | 0.2% | 1.0 – 3.0 (mean 1.4152) | | `description` | object | 0.2% | | | `id` | int64 | 0.0% | 24350017.0 – 41224446.0 (mean 27552804.3935) | | `datacollectionperiod` | int64 | 0.0% | 158390.0 – 377285.0 (mean 212533.6596) | | `datacollection` | int64 | 0.0% | 168123.0 – 388211.0 (mean 223775.4724) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 25144.0 – 257729.0 (mean 79763.6297) | | `datasourceorganization` | int64 | 0.0% | 2032.0 – 2032.0 (mean 2032.0) | | `datasourcedocument` | int64 | 0.0% | 6569.0 – 6569.0 (mean 6569.0) | | `dataseries` | int64 | 0.0% | 6467681.0 – 7843391.0 (mean 6598560.0733) | | `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 | 3.0 | 1.4152 | 1.0 | | `id` | 24350017.0 | 41224446.0 | 27552804.3935 | 24472794.0 | | `datacollectionperiod` | 158390.0 | 377285.0 | 212533.6596 | 159229.0 | | `datacollection` | 168123.0 | 388211.0 | 223775.4724 | 168604.0 | | `geographic_unit` | 25144.0 | 257729.0 | 79763.6297 | 25348.0 | | `datasourceorganization` | 2032.0 | 2032.0 | 2032.0 | 2032.0 | | `datasourcedocument` | 6569.0 | 6569.0 | 6569.0 | 6569.0 | | `dataseries` | 6467681.0 | 7843391.0 | 6598560.0733 | 6486884.0 | --- ## 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/niger_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_niger_current_situation_fewsnet_ipc_classification, title = {Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/niger_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.*
--- 数据集创建者：无标注 语言创建方式：爬取获取 语言：英语 许可协议：CC-BY-4.0 多语言类型：单语言 数据规模：1000 < 样本数 < 10000 源数据集类型：原创数据集 任务类别：表格分类、表格回归 任务子项：无 标签：非洲、人道主义、HDX（人道主义数据交换）、Electric Sheep Africa、粮食安全、命名实体识别（NER） 友好名称："尼日尔当前局势FEWS NET急性粮食不安全等级分类数据集" 数据集信息： 划分集： - 名称：训练集 样本数：5456 - 名称：测试集 样本数：1364 --- # 尼日尔当前局势FEWS NET急性粮食不安全等级分类数据集 **发布方**：FEWS NET（饥荒早期预警系统网络） · **数据来源**：[HDX（人道主义数据交换）](https://data.humdata.org/dataset/niger_current_situation_fewsnet_ipc_classification) · **许可协议**：`CC-BY` · **更新时间**：2026-04-03 --- ## 摘要 本数据集为2011年的尼日尔当前局势FEWS NET急性粮食不安全等级分类数据。 数据集中每一行均代表一级行政单元的观测记录，时间范围由`projection_start`、`projection_end`两列标注。地理覆盖范围：**NER（尼日尔国家代码）**。 *本数据集已由[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 | 类别 | 详情 | |---|---| | **应用领域** | 粮食安全与营养 | | **观测单元** | 一级行政单元 | | **总记录数** | 6821条 | | **字段总数** | 40个（9个数值型、23个分类型、7个日期时间型） | | **训练集划分** | 5456条记录 | | **测试集划分** | 1364条记录 | | **地理覆盖范围** | NER（尼日尔） | | **发布方** | FEWS NET | | **HDX平台最后更新时间** | 2026-04-03 | --- ## 字段分类 ### 地理类字段 `country`（国家：尼日尔）、`country_code`（国家代码：NE）、`fewsnet_region`（FEWS NET区域：西非）、`unit_type`（单元类型：fsc_admin_lhz）、`specialization_type`（专业化类型）及另外2个字段。 ### 时间类字段 `datacollectionperiod`（数值范围：158390.0–377285.0）、`reporting_date`（报告日期）。 ### 结果/测量类字段 `value`（结果值，数值范围：1.0–3.0）。 ### 标识/元数据类字段 `source_organization`（来源机构：FEWS NET 尼日尔分部）、`source_document`（来源文件：尼日尔粮食安全展望）、`geographic_unit_full_name`（地理单元全称：农牧混合带、阿巴拉克、塔瓦、尼日尔；西南谷物扇棕榈产品带、加亚、多索、尼日尔；南部灌溉经济作物带、马达瓦、塔瓦、尼日尔等）、`geographic_unit_name`（地理单元名称：雨养小米高粱带、农牧混合带、迁徙与游牧畜牧业区）、`fnid`（标识代码：NE2012C3050204、NE2012C3070214、NE2012C3060210等）及另外8个字段。 ### 其他字段 `geographic_group`（地理群组：西非）、`classification_scale`（分类等级）、`is_allowing_for_assistance`（是否允许援助）、`projection_start`（预测起始时间）、`projection_end`（预测结束时间）及另外12个字段。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-niger-current-situation-fewsnet-ipc-classification") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据结构 | 字段名 | 数据类型 | 缺失率 | 范围/示例值 | |---|---|---|---| | `source_organization` | object | 0.0% | FEWS NET 尼日尔分部 | | `source_document` | object | 0.0% | 尼日尔粮食安全展望 | | `country` | object | 0.0% | 尼日尔 | | `country_code` | object | 0.0% | NE | | `geographic_group` | object | 0.0% | 西非 | | `fewsnet_region` | object | 0.0% | 西非 | | `geographic_unit_full_name` | object | 0.0% | 农牧混合带、阿巴拉克、塔瓦、尼日尔；西南谷物扇棕榈产品带、加亚、多索、尼日尔；南部灌溉经济作物带、马达瓦、塔瓦、尼日尔 | | `geographic_unit_name` | object | 0.0% | 雨养小米高粱带、农牧混合带、迁徙与游牧畜牧业区 | | `unit_type` | object | 0.0% | fsc_admin_lhz | | `fnid` | object | 0.0% | NE2012C3050204、NE2012C3070214、NE2012C3060210 | | `classification_scale` | object | 0.0% | | | `scenario_name` | object | 0.0% | | | `preference_rating` | int64 | 0.0% | 90.0 – 90.0 (均值为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 | 0.2% | 1.0 – 3.0 (均值为1.4152) | | `description` | object | 0.2% | | | `id` | int64 | 0.0% | 24350017.0 – 41224446.0 (均值为27552804.3935) | | `datacollectionperiod` | int64 | 0.0% | 158390.0 – 377285.0 (均值为212533.6596) | | `datacollection` | int64 | 0.0% | 168123.0 – 388211.0 (均值为223775.4724) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 25144.0 – 257729.0 (均值为79763.6297) | | `datasourceorganization` | int64 | 0.0% | 2032.0 – 2032.0 (均值为2032.0) | | `datasourcedocument` | int64 | 0.0% | 6569.0 – 6569.0 (均值为6569.0) | | `dataseries` | int64 | 0.0% | 6467681.0 – 7843391.0 (均值为6598560.0733) | | `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% | | --- ## 数值型字段统计 | 字段名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `preference_rating` | 90.0 | 90.0 | 90.0 | 90.0 | | `value` | 1.0 | 3.0 | 1.4152 | 1.0 | | `id` | 24350017.0 | 41224446.0 | 27552804.3935 | 24472794.0 | | `datacollectionperiod` | 158390.0 | 377285.0 | 212533.6596 | 159229.0 | | `datacollection` | 168123.0 | 388211.0 | 223775.4724 | 168604.0 | | `geographic_unit` | 25144.0 | 257729.0 | 79763.6297 | 25348.0 | | `datasourceorganization` | 2032.0 | 2032.0 | 2032.0 | 2032.0 | | `datasourcedocument` | 6569.0 | 6569.0 | 6569.0 | 6569.0 | | `dataseries` | 6467681.0 – 7843391.0 | 6598560.0733 | 6486884.0 | --- ## 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。所有字段名均转换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`。删除了3个缺失值占比超过80%的字段：`pct_phase3`、`pct_phase4`、`pct_phase5`。基于解析成功率（阈值85%），将7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，并以Snappy压缩格式存储为Parquet文件。 --- ## 数据集局限性 - 数据源自FEWS NET，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/niger_current_situation_fewsnet_ipc_classification)以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_niger_current_situation_fewsnet_ipc_classification, title = {Niger Current Situation FEWS NET Acute Food Insecurity Classifications Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/niger_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) — 非洲机器学习数据集基础设施，尼日利亚拉各斯。*
