electricsheepafrica/africa-world-bank-climate-change-indicators-for-cote-d-ivoire
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 - climate-weather - indicators - civ pretty_name: "Cote d'Ivoire - Climate Change" dataset_info: splits: - name: train num_examples: 1300 - name: test num_examples: 325 --- # Cote d'Ivoire - Climate Change **Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-cote-d-ivoire) · **License:** `cc-by` · **Updated:** 2026-03-27 --- ## Abstract Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-cote-d-ivoire) on HDX. Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development. Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **CIV**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Food security and nutrition | | **Unit of observation** | Country-level aggregates | | **Rows (total)** | 1,625 | | **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) | | **Train split** | 1,300 rows | | **Test split** | 325 rows | | **Geographic scope** | CIV | | **Publisher** | World Bank Group | | **HDX last updated** | 2026-03-27 | --- ## Variables **Geographic** — `country_name` (Cote d'Ivoire), `country_iso3` (CIV), `year` (range 1960.0–2025.0). **Outcome / Measurement** — `value` (range -3800000.0–31934230.0). **Identifier / Metadata** — `indicator_name` (Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population), `indicator_code` (EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.TOTL), `esa_source` (HDX), `esa_processed` (2026-04-15). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-cote-d-ivoire") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `country_name` | object | 0.0% | Cote d'Ivoire | | `country_iso3` | object | 0.0% | CIV | | `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1998.3994) | | `indicator_name` | object | 0.0% | Population in urban agglomerations of more than 1 million (% of total population), Urban population (% of total population), Urban population | | `indicator_code` | object | 0.0% | EN.URB.MCTY.TL.ZS, SP.URB.TOTL.IN.ZS, SP.URB.TOTL | | `value` | float64 | 0.0% | -3800000.0 – 31934230.0 (mean 904611.0739) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-15 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 1998.3994 | 2000.0 | | `value` | -3800000.0 | 31934230.0 | 904611.0739 | 36.0173 | --- ## 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`. 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 World Bank Group 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/world-bank-climate-change-indicators-for-cote-d-ivoire) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_world_bank_climate_change_indicators_for_cote_d_ivoire, title = {Cote d'Ivoire - Climate Change}, author = {World Bank Group}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-cote-d-ivoire}, 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: - 语言采集自现有资源 language: - 英语（en） license: CC-BY-4.0 multilinguality: - 单语言 size_categories: - 1000 < 样本数 < 10000 source_datasets: - 原创数据集 task_categories: - 表格分类 - 表格回归 task_ids: - 无 tags: - 非洲 - 人道主义 - HDX（人道主义数据交换平台） - Electric Sheep Africa（电动绵羊非洲） - 气候与气象 - 指标 - CIV（科特迪瓦） pretty_name: "科特迪瓦——气候变化" dataset_info: splits: - name: 训练集（train） num_examples: 1300 - name: 测试集（test） num_examples: 325 # 科特迪瓦——气候变化 **发布方：** 世界银行集团（World Bank Group） · **来源：** [HDX（人道主义数据交换平台）](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-cote-d-ivoire) · **许可证：** `CC-BY` · **更新时间：** 2026-03-27 --- ## 摘要 本数据集的数据源自世界银行集团（World Bank Group）的[数据门户](http://data.worldbank.org/)，HDX（人道主义数据交换平台）上还提供了一份[科特迪瓦综合国家数据集](https://data.humdata.org/dataset/world-bank-combined-indicators-for-cote-d-ivoire)。 气候变化对发展中国家的冲击预计将最为严重。其影响——包括气温升高、降水模式改变、海平面上升以及愈发频发的气象灾害——对农业、粮食与水供应构成威胁。发展中国家在减贫、抗饥与防病方面取得的近期成果，以及数十亿民众的生计与生命安全均面临风险。应对气候变化需要史无前例的跨境全球合作。世界银行集团（World Bank Group）正助力支持发展中国家，为全球应对方案贡献力量，同时针对不同发展中国家合作伙伴的差异化需求调整工作方式。本数据集涵盖气候系统、气候影响暴露度、恢复力、温室气体排放以及能源使用相关数据。其他与气候变化相关的指标可在其他数据页面查询，尤其是环境、农业与农村发展、能源与采矿、卫生、基础设施、减贫以及城市发展板块。 本数据集的每一行均代表国家级汇总数据。数据最后一次在HDX（人道主义数据交换平台）平台更新的时间为2026-03-27。地理覆盖范围：**CIV（科特迪瓦）**。 *本数据集已由[Electric Sheep Africa（电动绵羊非洲）](https://huggingface.co/electricsheepafrica)整理为适配机器学习的Parquet格式（Parquet）。* --- ## 数据集特征 | | | |---|---| | **领域** | 粮食安全与营养 | | **观测单元** | 国家级汇总数据 | | **总样本行数** | 1625 | | **列数** | 8（2个数值型、6个分类型、0个日期时间型） | | **训练集划分** | 1300行 | | **测试集划分** | 325行 | | **地理覆盖范围** | CIV（科特迪瓦） | | **发布方** | 世界银行集团（World Bank Group） | | **HDX平台最后更新时间** | 2026-03-27 | --- ## 变量 **地理类变量** — `country_name`（科特迪瓦国名）、`country_iso3`（CIV（科特迪瓦）国家代码）、`year`（年份范围：1960.0–2025.0）。 **结果/测量变量** — `value`（数值范围：-3800000.0–31934230.0）。 **标识符/元数据变量** — `indicator_name`（包含：百万以上人口都市集聚区人口占总人口比例、城镇人口占总人口比例、城镇人口总量）、`indicator_code`（EN.URB.MCTY.TL.ZS、SP.URB.TOTL.IN.ZS、SP.URB.TOTL）、`esa_source`（数据来源：HDX（人道主义数据交换平台））、`esa_processed`（数据处理时间：2026-04-15）。 --- ## 快速入门 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-cote-d-ivoire") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() --- ## 数据模式 | 列名 | 数据类型 | 空值占比 | 取值范围/示例值 | |---|---|---|---| | `country_name` | 对象型（object） | 0.0% | 科特迪瓦 | | `country_iso3` | 对象型（object） | 0.0% | CIV（科特迪瓦国家代码） | | `year` | 64位整型（int64） | 0.0% | 1960.0 – 2025.0（均值：1998.3994） | | `indicator_name` | 对象型（object） | 0.0% | 百万以上人口都市集聚区人口占总人口比例、城镇人口占总人口比例、城镇人口总量 | | `indicator_code` | 对象型（object） | 0.0% | EN.URB.MCTY.TL.ZS、SP.URB.TOTL.IN.ZS、SP.URB.TOTL | | `value` | 64位浮点型（float64） | 0.0% | -3800000.0 – 31934230.0（均值：904611.0739） | | `esa_source` | 对象型（object） | 0.0% | HDX（人道主义数据交换平台） | | `esa_processed` | 对象型（object） | 0.0% | 2026-04-15 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `year` | 1960.0 | 2025.0 | 1998.3994 | 2000.0 | | `value` | -3800000.0 | 31934230.0 | 904611.0739 | 36.0173 | --- ## 数据整理说明 原始数据通过CKAN应用程序编程接口（CKAN API）从HDX（人道主义数据交换平台）平台下载，并转换为Parquet格式（Parquet）。列名均转换为小写并统一为蛇形命名法（snake_case）。常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。本数据集以固定随机种子（42）按80/20的比例划分为训练集与测试集，并以Snappy压缩的Parquet格式（Parquet）存储。 --- ## 数据集局限性 - 本数据集源自世界银行集团（World Bank Group），尚未由Electric Sheep Africa（电动绵羊非洲）进行独立验证。 - 自动化数据清洗无法修正原始数据集中的错报值、定义不一致问题或采样偏差。 - 如需查看发布方的方法说明与免责条款，请参阅[HDX（人道主义数据交换平台）平台原始数据集页面](https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-cote-d-ivoire)。 --- ## 引用格式 bibtex @dataset{hdx_africa_world_bank_climate_change_indicators_for_cote_d_ivoire, title = {科特迪瓦——气候变化}, author = {世界银行集团（World Bank Group)}, year = {2026}, url = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-cote-d-ivoire}, note = {由Electric Sheep Africa（电动绵羊非洲）(https://huggingface.co/electricsheepafrica)重新打包为机器学习可用格式} } --- *[Electric Sheep Africa（电动绵羊非洲）](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施。尼日利亚拉各斯。*
