electricsheepafrica/africa-idmc-idp-data-lbr

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - tabular-classification task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - conflict-violence - displacement - internally-displaced-persons-idp - natural-disasters - lbr pretty_name: "Liberia - Internal Displacements (New Displacements) – IDPs" dataset_info: splits: - name: train num_examples: 4 - name: test num_examples: 1 --- # Liberia - Internal Displacements (New Displacements) – IDPs **Publisher:** Internal Displacement Monitoring Centre (IDMC) · **Source:** [HDX](https://data.humdata.org/dataset/idmc-idp-data-lbr) · **License:** `cc-by-igo` · **Updated:** 2026-03-18 --- ## Abstract The [Global Internal Displacement Database (GIDD)](http://www.internal-displacement.org/database/displacement-data), maintained by the [Internal Displacement Monitoring Centre (IDMC)](https://www.internal-displacement.org/), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the [1998 Guiding Principles](https://www.internal-displacement.org/internal-displacement/guiding-principles-on-internal-displacement/), as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. The GIDD tracks two primary metrics: "People Displaced" or population "Stock" figures, which represent the total number of people living in displacement at year-end, and "New Displacement," which counts new displacement incidents (population Flows) rather than individual people, accounting for potential multiple displacements by the same person. This dataset serves as a crucial resource for understanding long-term trends and validated displacement figures globally. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/. "Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year. "Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-18. Geographic scope: **LBR**. *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)** | 6 | | **Columns** | 8 (4 numeric, 4 categorical, 0 datetime) | | **Train split** | 4 rows | | **Test split** | 1 rows | | **Geographic scope** | LBR | | **Publisher** | Internal Displacement Monitoring Centre (IDMC) | | **HDX last updated** | 2026-03-18 | --- ## Variables **Geographic** — `iso3` (LBR), `country_name` (Liberia), `year` (range 2009.0–2014.0), `new_displacement` (range 0.0–10000.0), `total_displacement` (range 23000.0–23000.0) and 1 others. **Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-idmc-idp-data-lbr") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `iso3` | object | 0.0% | LBR | | `country_name` | object | 0.0% | Liberia | | `year` | int64 | 0.0% | 2009.0 – 2014.0 (mean 2011.5) | | `new_displacement` | int64 | 0.0% | 0.0 – 10000.0 (mean 1666.6667) | | `total_displacement` | int64 | 0.0% | 23000.0 – 23000.0 (mean 23000.0) | | `total_displacement_rounded` | int64 | 0.0% | 23000.0 – 23000.0 (mean 23000.0) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-07 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 2009.0 | 2014.0 | 2011.5 | 2011.5 | | `new_displacement` | 0.0 | 10000.0 | 1666.6667 | 0.0 | | `total_displacement` | 23000.0 | 23000.0 | 23000.0 | 23000.0 | | `total_displacement_rounded` | 23000.0 | 23000.0 | 23000.0 | 23000.0 | --- ## 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`. 1 column(s) with >80% missing values were removed: `new_displacement_rounded`. 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 Internal Displacement Monitoring Centre (IDMC) 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/idmc-idp-data-lbr) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_idmc_idp_data_lbr, title = {Liberia - Internal Displacements (New Displacements) – IDPs}, author = {Internal Displacement Monitoring Centre (IDMC)}, year = {2026}, url = {https://data.humdata.org/dataset/idmc-idp-data-lbr}, 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 language_creators: - 公开获取 language: - 英语 license: CC-BY-4.0 multilinguality: - 单语言 size_categories: - 少于1000条 source_datasets: - 原创数据集 task_categories: - 表格分类 task_ids: [] tags: - 非洲 - 人道主义 - HDX - Electric Sheep Africa - 冲突与暴力 - 流离失所 - 国内流离失所者（internally-displaced-persons-idp） - 自然灾害 - LBR pretty_name: "利比里亚——国内流离失所（新增流离失所事件）——国内流离失所者（IDPs）" dataset_info: splits: - name: train num_examples: 4 - name: test num_examples: 1 # 利比里亚——国内流离失所（新增流离失所事件）——国内流离失所者（IDPs） **发布方**：国内流离失所监测中心（Internal Displacement Monitoring Centre, IDMC） · **来源**：[HDX](https://data.humdata.org/dataset/idmc-idp-data-lbr) · **许可协议**：`cc-by-igo` · **更新时间**：2026-03-18 --- ## 摘要 由国内流离失所监测中心（Internal Displacement Monitoring Centre, IDMC）维护的[全球国内流离失所数据库（Global Internal Displacement Database, GIDD）](http://www.internal-displacement.org/database/displacement-data)，提供了全球范围内经过验证的年度国内流离失所综合估算数据。该数据库对国内流离失所者（internally-displaced-persons, IDPs）的定义符合[1998年指导原则](https://www.internal-displacement.org/internal-displacement/guiding-principles-on-internal-displacement/)，即因武装冲突、规避武装冲突后果、大规模暴力事件、人权侵犯行为、自然或人为灾害而被迫逃离或离开家园或惯常居所，且未跨越国际边境的个人或群体。 GIDD追踪两项核心指标：一是“流离失所人口”即年末流离失所总人数的“存量”数据，二是“新增流离失所事件”，即统计新增的流离失所事件（人口流动量）而非单个流离失所者人数，以覆盖同一人员多次流离失所的情况。本数据集是了解全球长期流离失所趋势与经验证的流离失所数据的重要资源。如需获取详细信息与完整API规范，建议用户查阅官方文档：https://www.internal-displacement.org/database/api-documentation/。 "国内流离失所者（IDPs）"指截至每年年末处于流离失所状态的人口数量。 "国内流离失所（新增流离失所事件）"指记录的新增流离失所案例或事件数量，而非流离失所者总人数，这是因为部分人员可能经历多次流离失所。 本数据集的每一行均代表国家级汇总数据。数据最近一次在HDX平台更新的时间为2026-03-18。地理覆盖范围：**LBR（利比里亚）**。 *本数据集由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为机器学习可用的Parquet格式。* --- ## 数据集特征 | | | |---|---| | **领域** | 冲突与安全 | | **观测单元** | 国家级汇总数据 | | **总行数** | 6 | | **列数** | 8（4个数值列、4个分类列、0个日期时间列） | | **训练集拆分** | 4行 | | **测试集拆分** | 1行 | | **地理覆盖范围** | LBR（利比里亚） | | **发布方** | 国内流离失所监测中心（IDMC） | | **HDX平台最后更新时间** | 2026-03-18 | --- ## 变量 **地理相关字段**：`iso3`（LBR，利比里亚国家代码）、`country_name`（利比里亚）、`year`（取值范围2009.0–2014.0）、`new_displacement`（取值范围0.0–10000.0）、`total_displacement`（取值范围23000.0–23000.0）及1个其他字段。 **标识符/元数据字段**：`esa_source`（HDX）、`esa_processed`（2026-04-07）。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-idmc-idp-data-lbr") 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 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `iso3` | 字符串（object） | 0.0% | LBR | | `country_name` | 字符串（object） | 0.0% | 利比里亚 | | `year` | 64位整数（int64） | 0.0% | 2009.0 – 2014.0（均值2011.5） | | `new_displacement` | 64位整数（int64） | 0.0% | 0.0 – 10000.0（均值1666.6667） | | `total_displacement` | 64位整数（int64） | 0.0% | 23000.0 – 23000.0（均值23000.0） | | `total_displacement_rounded` | 64位整数（int64） | 0.0% | 23000.0 – 23000.0（均值23000.0） | | `esa_source` | 字符串（object） | 0.0% | HDX | | `esa_processed` | 字符串（object） | 0.0% | 2026-04-07 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 2009.0 | 2014.0 | 2011.5 | 2011.5 | | `new_displacement` | 0.0 | 10000.0 | 1666.6667 | 0.0 | | `total_displacement` | 23000.0 | 23000.0 | 23000.0 | 23000.0 | | `total_displacement_rounded` | 23000.0 | 23000.0 | 23000.0 | 23000.0 | --- ## 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`。移除了1列缺失值占比超过80%的字段：`new_displacement_rounded`。本数据集采用固定随机种子（42）以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 - 数据源自国内流离失所监测中心（IDMC），未经过Electric Sheep Africa的独立验证。 - 自动化清洗无法修正原始数据收集中的错报值、定义不一致或抽样偏差问题。 - 如需查看发布方的方法说明与免责条款，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/idmc-idp-data-lbr)。 --- ## 引用 bibtex @dataset{hdx_africa_idmc_idp_data_lbr, title = {Liberia - Internal Displacements (New Displacements) – IDPs}, author = {Internal Displacement Monitoring Centre (IDMC)}, year = {2026}, url = {https://data.humdata.org/dataset/idmc-idp-data-lbr}, 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。*