electricsheepafrica/africa-uga-climate-trace

--- annotations_creators: - no-annotation language_creators: - found language: - en license: cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - tabular-classification - tabular-regression task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - climate-weather - environment - points-of-interest-poi - uga pretty_name: "Uganda: Greenhouse Gas and Air Pollutant Emissions" dataset_info: splits: - name: train num_examples: 36876 - name: test num_examples: 9219 --- # Uganda: Greenhouse Gas and Air Pollutant Emissions **Publisher:** Climate TRACE · **Source:** [HDX](https://data.humdata.org/dataset/uga-climate-trace) · **License:** `cc-by` · **Updated:** 2026-03-30 --- ## Abstract Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally. The Climate TRACE emissions inventory includes: - Annual country-level emissions by sub-sector and by gas beginning in 2015 - Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence - Emissions source ownership where and when available. Each row in this dataset represents time-series observations. Data was last updated on HDX on 2026-03-30. Geographic scope: **UGA**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Climate and environment | | **Unit of observation** | Time-series observations | | **Rows (total)** | 46,096 | | **Columns** | 13 (4 numeric, 9 categorical, 0 datetime) | | **Train split** | 36,876 rows | | **Test split** | 9,219 rows | | **Geographic scope** | UGA | | **Publisher** | Climate TRACE | | **HDX last updated** | 2026-03-30 | --- ## Variables **Geographic** — `year` (range 2024.0–2026.0), `emissionsquantity` (range 0.0–96441.9813). **Temporal** — `month` (range 1.0–12.0). **Identifier / Metadata** — `full_name` (Uganda, Wakiso District, UGA, Arua District, UGA), `id` (UGA, UGA.57_1, UGA.3_1), `level_0_id` (UGA), `level_1_id` (UGA.57_1, UGA.3_1, UGA.21_1), `name` (Uganda, Wakiso District, Arua District) and 2 others. **Other** — `level` (range 0.0–1.0), `sector` (agriculture, manufacturing, waste), `gas` (ch4). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-uga-climate-trace") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `full_name` | object | 0.0% | Uganda, Wakiso District, UGA, Arua District, UGA | | `id` | object | 0.0% | UGA, UGA.57_1, UGA.3_1 | | `level` | int64 | 0.0% | 0.0 – 1.0 (mean 0.9821) | | `level_0_id` | object | 0.0% | UGA | | `level_1_id` | object | 1.8% | UGA.57_1, UGA.3_1, UGA.21_1 | | `name` | object | 0.0% | Uganda, Wakiso District, Arua District | | `year` | int64 | 0.0% | 2024.0 – 2026.0 (mean 2024.6116) | | `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.6914) | | `sector` | object | 0.0% | agriculture, manufacturing, waste | | `gas` | object | 0.0% | ch4 | | `emissionsquantity` | float64 | 0.0% | 0.0 – 96441.9813 (mean 242.4031) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-05 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `level` | 0.0 | 1.0 | 0.9821 | 1.0 | | `year` | 2024.0 | 2026.0 | 2024.6116 | 2025.0 | | `month` | 1.0 | 12.0 | 6.6914 | 7.0 | | `emissionsquantity` | 0.0 | 96441.9813 | 242.4031 | 0.3534 | --- ## 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: `level_2_id`. 54,322 exact duplicate rows were removed. 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 Climate TRACE 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/uga-climate-trace) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_uga_climate_trace, title = {Uganda: Greenhouse Gas and Air Pollutant Emissions}, author = {Climate TRACE}, year = {2026}, url = {https://data.humdata.org/dataset/uga-climate-trace}, 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: - 现有公开数据源采集 language: - 英语 license: CC BY 4.0 multilinguality: - 单语言 size_categories: - 10000 < 样本数 < 100000 source_datasets: - 原创数据集 task_categories: - 表格分类 - 表格 regression task_ids: [] tags: - 非洲 - 人道主义 - 人道主义数据交换（HDX，Humanitarian Data Exchange） - 电动绵羊非洲（Electric Sheep Africa） - 气候与天气 - 环境 - 兴趣点（POI） - 乌干达（UGA，国家代码） pretty_name: "乌干达：温室气体与空气污染物排放" dataset_info: splits: - name: train num_examples: 36876 - name: test num_examples: 9219 # 乌干达：温室气体与空气污染物排放 **发布方：** 气候追踪（Climate TRACE） · **数据源：** [人道主义数据交换（HDX，Humanitarian Data Exchange）](https://data.humdata.org/dataset/uga-climate-trace) · **许可协议：** `CC BY` · **更新时间：** 2026-03-30 --- ## 摘要 气候追踪（Climate TRACE）是一个由多家机构组成的非营利性联盟，旨在构建及时、开放且可获取的温室气体排放源精准清单。气候追踪已对全球所有国家的超270万个排放源（源自7.44亿余项资产）的温室气体（GHG）与空气污染物排放量进行估算。 气候追踪的排放清单包含以下内容： - 2015年起按子行业与气体类型划分的国家级年度排放量数据 - 2021年起按子行业、气体类型及置信度划分的排放源级月度排放量数据 - 可获取情况下的排放源归属信息。 本数据集的每一行均代表一条时间序列观测值。数据最近一次在HDX平台更新的时间为2026-03-30。地理覆盖范围：**UGA（乌干达国家代码）**。 *本数据集已由[电动绵羊非洲（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 | | | |---|---| | **领域** | 气候与环境 | | **观测单元** | 时间序列观测值 | | **总数据行数** | 46096 | | **列数** | 13列（4列数值型、9列分类型、0列日期时间型） | | **训练集划分** | 36876行 | | **测试集划分** | 9219行 | | **地理覆盖范围** | UGA（乌干达国家代码） | | **发布方** | 气候追踪（Climate TRACE） | | **HDX平台最后更新时间** | 2026-03-30 | --- ## 变量说明 **地理相关变量** — `year`（年份，取值范围2024.0–2026.0），`emissionsquantity`（排放量，取值范围0.0–96441.9813）。 **时间相关变量** — `month`（月份，取值范围1.0–12.0）。 **标识符/元数据变量** — `full_name`（完整名称，示例值：乌干达、瓦基索区、UGA、阿鲁阿区、UGA），`id`（唯一标识，示例值：UGA、UGA.57_1、UGA.3_1），`level_0_id`（0级标识，示例值：UGA），`level_1_id`（1级标识，示例值：UGA.57_1、UGA.3_1、UGA.21_1），`name`（名称，示例值：乌干达、瓦基索区、阿鲁阿区）及另外2个变量。 **其他变量** — `level`（层级，取值范围0.0–1.0），`sector`（行业领域，可选值：农业、制造业、废弃物处理），`gas`（气体类型，可选值：ch4，即甲烷）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-uga-climate-trace") 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 | |---|---|---|---| | `full_name` | 字符串型 | 0.0% | 乌干达、瓦基索区、UGA、阿鲁阿区、UGA | | `id` | 字符串型 | 0.0% | UGA、UGA.57_1、UGA.3_1 | | `level` | 整型 | 0.0% | 0.0 – 1.0 (mean 0.9821) | | `level_0_id` | 字符串型 | 0.0% | UGA | | `level_1_id` | 字符串型 | 1.8% | UGA.57_1、UGA.3_1、UGA.21_1 | | `name` | 字符串型 | 0.0% | 乌干达、瓦基索区、阿鲁阿区 | | `year` | 整型 | 0.0% | 2024.0 – 2026.0 (mean 2024.6116) | | `month` | 整型 | 0.0% | 1.0 – 12.0 (mean 6.6914) | | `sector` | 字符串型 | 0.0% | agriculture, manufacturing, waste | | `gas` | 字符串型 | 0.0% | ch4 | | `emissionsquantity` | 浮点型 | 0.0% | 0.0 – 96441.9813 (mean 242.4031) | | `esa_source` | 字符串型 | 0.0% | HDX | | `esa_processed` | 字符串型 | 0.0% | 2026-04-05 | --- ## 数值统计摘要 | 列名 | 最小值 | 最大值 | 均值 | 中位数 | |---|---|---|---|---| | `level` | 0.0 | 1.0 | 0.9821 | 1.0 | | `year` | 2024.0 | 2026.0 | 2024.6116 | 2025.0 | | `month` | 1.0 | 12.0 | 6.6914 | 7.0 | | `emissionsquantity` | 0.0 | 96441.9813 | 242.4031 | 0.3534 | --- ## 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。列名统一转为小写并采用蛇形命名法（snake_case）进行标准化。常见缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）被统一替换为`NaN`。移除1列空值占比超80%的变量`level_2_id`，并删除54322条完全重复的数据行。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，最终以Snappy压缩格式的Parquet文件保存。 --- ## 数据局限性 - 数据源自气候追踪（Climate TRACE），尚未经电动绵羊非洲（Electric Sheep Africa）独立验证。 - 自动化清洗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[HDX平台原始数据集页面](https://data.humdata.org/dataset/uga-climate-trace)获取发布方的方法说明与注意事项。 --- ## 引用格式 bibtex @dataset{hdx_africa_uga_climate_trace, title = {乌干达：温室气体与空气污染物排放}, author = {Climate TRACE}, year = {2026}, url = {https://data.humdata.org/dataset/uga-climate-trace}, note = {由电动绵羊非洲（Electric Sheep Africa）重新打包以适配机器学习需求（https://huggingface.co/electricsheepafrica)} } --- *[电动绵羊非洲（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。*