electricsheepafrica/africa-cameroon-current-situation-fewsnet-ipc-classification
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--- 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 - cmr pretty_name: "Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data" dataset_info: splits: - name: train num_examples: 1305 - name: test num_examples: 326 --- # Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data **Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/cameroon_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-03 --- ## Abstract Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2020 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: **CMR**. *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)** | 1,632 | | **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) | | **Train split** | 1,305 rows | | **Test split** | 326 rows | | **Geographic scope** | CMR | | **Publisher** | FEWS NET | | **HDX last updated** | 2026-04-03 | --- ## Variables **Geographic** — `country` (Cameroon), `country_code` (CM), `fewsnet_region` (West Africa), `unit_type` (fsc_admin_lhz), `specialization_type` and 2 others. **Temporal** — `datacollectionperiod` (range 158408.0–374892.0), `reporting_date`. **Outcome / Measurement** — `value` (range 1.0–3.0). **Identifier / Metadata** — `source_organization` (FEWS NET, Cameroon), `source_document` (Food Security Outlook, Cameroon), `geographic_unit_full_name` (ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Djerem, Adamawa, Cameroon, ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Faro et Deo, Adamawa, Cameroon, TIKAR PLAIN: maize, irrigated rice, robusta coffee, fishing, livestock, Ngo Ketunjia, Northwest, Cameroon), `geographic_unit_name` (WESTERN HIGHLANDS: maize, market gardening, beans, potatoes, egg production, tubers, arabica coffee, DEGRADED FOREST OF THE CENTER-SOUTH: cocoa, pineapple, cassava, maize, market-gardening, small livestock and poultry, MOUNT CAMEROON FOREST: cocoa, palm oil, robusta coffee, rubber, plantain, tubers, pepper, snails), `fnid` (CM2020C3010107, CM2020C3010207, CM2020C3070708) and 8 others. **Other** — `geographic_group` (Middle 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-cameroon-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, Cameroon | | `source_document` | object | 0.0% | Food Security Outlook, Cameroon | | `country` | object | 0.0% | Cameroon | | `country_code` | object | 0.0% | CM | | `geographic_group` | object | 0.0% | Middle Africa | | `fewsnet_region` | object | 0.0% | West Africa | | `geographic_unit_full_name` | object | 0.0% | ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Djerem, Adamawa, Cameroon, ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Faro et Deo, Adamawa, Cameroon, TIKAR PLAIN: maize, irrigated rice, robusta coffee, fishing, livestock, Ngo Ketunjia, Northwest, Cameroon | | `geographic_unit_name` | object | 0.0% | WESTERN HIGHLANDS: maize, market gardening, beans, potatoes, egg production, tubers, arabica coffee, DEGRADED FOREST OF THE CENTER-SOUTH: cocoa, pineapple, cassava, maize, market-gardening, small livestock and poultry, MOUNT CAMEROON FOREST: cocoa, palm oil, robusta coffee, rubber, plantain, tubers, pepper, snails | | `unit_type` | object | 0.0% | fsc_admin_lhz | | `fnid` | object | 0.0% | CM2020C3010107, CM2020C3010207, CM2020C3070708 | | `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 | 0.0% | 1.0 – 3.0 (mean 1.5368) | | `description` | object | 0.0% | | | `id` | int64 | 0.0% | 24352267.0 – 41122665.0 (mean 31266672.6176) | | `datacollectionperiod` | int64 | 0.0% | 158408.0 – 374892.0 (mean 277007.7059) | | `datacollection` | int64 | 0.0% | 168131.0 – 385837.0 (mean 290428.1765) | | `scenario` | object | 0.0% | | | `geographic_unit` | int64 | 0.0% | 142851.0 – 142946.0 (mean 142898.5) | | `datasourceorganization` | int64 | 0.0% | 2026.0 – 2026.0 (mean 2026.0) | | `datasourcedocument` | int64 | 0.0% | 6533.0 – 6533.0 (mean 6533.0) | | `dataseries` | int64 | 0.0% | 6470274.0 – 6796968.0 (mean 6662438.5588) | | `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 | 1.5368 | 1.0 | | `id` | 24352267.0 | 41122665.0 | 31266672.6176 | 32087928.5 | | `datacollectionperiod` | 158408.0 | 374892.0 | 277007.7059 | 292956.0 | | `datacollection` | 168131.0 | 385837.0 | 290428.1765 | 308377.0 | | `geographic_unit` | 142851.0 | 142946.0 | 142898.5 | 142898.5 | | `datasourceorganization` | 2026.0 | 2026.0 | 2026.0 | 2026.0 | | `datasourcedocument` | 6533.0 | 6533.0 | 6533.0 | 6533.0 | | `dataseries` | 6470274.0 | 6796968.0 | 6662438.5588 | 6796887.0 | --- ## 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/cameroon_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_cameroon_current_situation_fewsnet_ipc_classification, title = {Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/cameroon_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.*
注释创建者： - 无注释 语言创建者： - 现有资源采集 语言： - 英语 许可证： - cc-by-4.0 多语言属性： - 单语言 规模类别： - 1000 < 样本量 < 10000 源数据集： - 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 - electric-sheep-africa - 粮食安全 - CMR 数据集展示名称："喀麦隆当前局势FEWS NET急性粮食不安全分类数据" 数据集信息： 划分方式： - 名称：训练集 样本数：1305 - 名称：测试集 样本数：326 # 喀麦隆当前局势FEWS NET急性粮食不安全分类数据 **发布方：** FEWS NET · **来源：** [HDX（人道主义数据交换，Humanitarian Data Exchange）](https://data.humdata.org/dataset/cameroon_current_situation_fewsnet_ipc_classification) · **许可证：** `cc-by` · **更新时间：** 2026-04-03 --- ## 摘要 本数据集为2020年喀麦隆当前局势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`、`projection_end`列标注。地理覆盖范围：**CMR（喀麦隆）**。 *本数据集已由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适合机器学习使用的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 粮食安全与营养 | | **观测单元** | 一级行政单元观测样本 | | **总样本行数** | 1632 | | **列数** | 40列（9列数值型、23列分类型、7列日期时间型） | | **训练集划分** | 1305行 | | **测试集划分** | 326行 | | **地理覆盖范围** | CMR（喀麦隆） | | **发布方** | FEWS NET | | **HDX最后更新时间** | 2026-04-03 | --- ## 变量分类 **地理类变量** — `country`（喀麦隆）、`country_code`（CM）、`fewsnet_region`（西非）、`unit_type`（fsc_admin_lhz）、`specialization_type`及另外2个变量。 **时间类变量** — `datacollectionperiod`（取值范围158408.0–374892.0）、`reporting_date`（报告日期）。 **结果/测量变量** — `value`（取值范围1.0–3.0）。 **标识符/元数据变量** — `source_organization`（FEWS NET 喀麦隆分部）、`source_document`（《喀麦隆粮食安全展望》）、`geographic_unit_full_name`（示例：ADAMAWA HIGH PLATEUX: 牛、玉米、木薯、山药、甜薯、豆类、蜂蜜、Djerem、阿达马瓦、喀麦隆；ADAMAWA HIGH PLATEUX: 牛、玉米、木薯、山药、甜薯、豆类、蜂蜜、Faro et Deo、阿达马瓦、喀麦隆；TIKAR PLAIN: 玉米、灌溉水稻、罗布斯塔咖啡、渔业、畜牧业、Ngo Ketunjia、西北省、喀麦隆）、`geographic_unit_name`（示例：WESTERN HIGHLANDS: 玉米、商品菜种植、豆类、马铃薯、禽蛋生产、块根作物、阿拉比卡咖啡；CENTER-SOUTH退化林区: 可可、菠萝、木薯、玉米、商品菜种植、小型畜禽养殖；MOUNT CAMEROON FOREST: 可可、棕榈油、罗布斯塔咖啡、橡胶、大蕉、块根作物、胡椒、蜗牛）、`fnid`（CM2020C3010107、CM2020C3010207、CM2020C3070708）及另外8个变量。 **其他变量** — `geographic_group`（中非）、`classification_scale`（分类尺度）、`is_allowing_for_assistance`（是否允许援助）、`projection_start`、`projection_end`及另外12个变量。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-cameroon-current-situation-fewsnet-ipc-classification") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() 上述代码展示了如何加载该数据集并转换为Pandas DataFrame格式。 --- ## 数据结构 | 列名 | 数据类型 | 缺失率 | 取值范围/示例值 | |---|---|---|---| | `source_organization` | 字符串型（object） | 0.0% | FEWS NET, 喀麦隆 | | `source_document` | 字符串型 | 0.0% | 《喀麦隆粮食安全展望》 | | `country` | 字符串型 | 0.0% | 喀麦隆 | | `country_code` | 字符串型 | 0.0% | CM | | `geographic_group` | 字符串型 | 0.0% | 中非 | | `fewsnet_region` | 字符串型 | 0.0% | 西非 | | `geographic_unit_full_name` | 字符串型 | 0.0% | ADAMAWA HIGH PLATEUX: 牛、玉米、木薯、山药、甜薯、豆类、蜂蜜、Djerem、阿达马瓦、喀麦隆；ADAMAWA HIGH PLATEUX: 牛、玉米、木薯、山药、甜薯、豆类、蜂蜜、Faro et Deo、阿达马瓦、喀麦隆；TIKAR PLAIN: 玉米、灌溉水稻、罗布斯塔咖啡、渔业、畜牧业、Ngo Ketunjia、西北省、喀麦隆 | | `geographic_unit_name` | 字符串型 | 0.0% | WESTERN HIGHLANDS: 玉米、商品菜种植、豆类、马铃薯、禽蛋生产、块根作物、阿拉比卡咖啡；CENTER-SOUTH退化林区: 可可、菠萝、木薯、玉米、商品菜种植、小型畜禽养殖；MOUNT CAMEROON FOREST: 可可、棕榈油、罗布斯塔咖啡、橡胶、大蕉、块根作物、胡椒、蜗牛 | | `unit_type` | 字符串型 | 0.0% | fsc_admin_lhz | | `fnid` | 字符串型 | 0.0% | CM2020C3010107、CM2020C3010207、CM2020C3070708 | | `classification_scale` | 字符串型 | 0.0% | 无 | | `scenario_name` | 字符串型 | 0.0% | 无 | | `preference_rating` | 整型（int64） | 0.0% | 90.0 – 90.0（均值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 | 0.0% | 无 | | `status` | 字符串型 | 0.0% | 无 | | `value` | 浮点型（float64） | 0.0% | 1.0 – 3.0（均值1.5368） | | `description` | 字符串型 | 0.0% | 无 | | `id` | 整型 | 0.0% | 24352267.0 – 41122665.0（均值31266672.6176） | | `datacollectionperiod` | 整型 | 0.0% | 158408.0 – 374892.0（均值277007.7059） | | `datacollection` | 整型 | 0.0% | 168131.0 – 385837.0（均值290428.1765） | | `scenario` | 字符串型 | 0.0% | 无 | | `geographic_unit` | 整型 | 0.0% | 142851.0 – 142946.0（均值142898.5） | | `datasourceorganization` | 整型 | 0.0% | 2026.0 – 2026.0（均值2026.0） | | `datasourcedocument` | 整型 | 0.0% | 6533.0 – 6533.0（均值6533.0） | | `dataseries` | 整型 | 0.0% | 6470274.0 – 6796968.0（均值6662438.5588） | | `dataseries_name` | 字符串型 | 0.0% | 无 | | `specialization_type` | 字符串型 | 0.0% | 无 | | `dataseries_specialization_type` | 字符串型 | 0.0% | 无 | | `data_usage_policy` | 字符串型 | 0.0% | 无 | | `created` | 日期时间型 | 0.0% | 无 | | `modified` | 日期时间型 | 0.0% | 无 | | `status_changed` | 日期时间型 | 0.0% | 无 | | `collection_status` | 字符串型 | 0.0% | 无 | | `collection_status_changed` | 日期时间型 | 0.0% | 无 | | `collection_schedule` | 字符串型 | 0.0% | 无 | | `reporting_date` | 日期时间型 | 0.0% | 无 | | `esa_source` | 字符串型 | 0.0% | 无 | | `esa_processed` | 字符串型 | 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 | 3.0 | 1.5368 | 1.0 | | `id` | 24352267.0 | 41122665.0 | 31266672.6176 | 32087928.5 | | `datacollectionperiod` | 158408.0 | 374892.0 | 277007.7059 | 292956.0 | | `datacollection` | 168131.0 | 385837.0 | 290428.1765 | 308377.0 | | `geographic_unit` | 142851.0 | 142946.0 | 142898.5 | 142898.5 | | `datasourceorganization` | 2026.0 | 2026.0 | 2026.0 | 2026.0 | | `datasourcedocument` | 6533.0 | 6533.0 | 6533.0 | 6533.0 | | `dataseries` | 6470274.0 | 6796968.0 | 6662438.5588 | 6796887.0 | --- ## 数据集整理流程 原始数据通过CKAN API从HDX下载，并转换为Parquet格式。所有列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列缺失率超过80%的变量：`pct_phase3`、`pct_phase4`、`pct_phase5`。基于解析成功率（阈值85%），将7列从字符串类型转换为数值型或日期时间型。使用固定随机种子（42）将数据集按80/20划分为训练集和测试集，并保存为Snappy压缩的Parquet格式文件。 --- ## 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/cameroon_current_situation_fewsnet_ipc_classification)查看发布方提供的方法说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_cameroon_current_situation_fewsnet_ipc_classification, title = {喀麦隆当前局势FEWS NET急性粮食不安全分类数据}, author = {FEWS NET}, year = {2026}, url = {https://data.humdata.org/dataset/cameroon_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。尼日利亚拉各斯。*
