electricsheepafrica/africa-unhabitat-gw-indicators

--- 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 - other task_ids: [] tags: - africa - humanitarian - hdx - electric-sheep-africa - baseline-population - education - health - hxl - indicators - transportation - gnb pretty_name: "Guinea-bissau: - Demographic, Health, Education and Transport indicators" dataset_info: splits: - name: train num_examples: 190 - name: test num_examples: 47 --- # Guinea-bissau: - Demographic, Health, Education and Transport indicators **Publisher:** United Nations Human Settlements Programmes, Data and Analytics Section · **Source:** [HDX](https://data.humdata.org/dataset/unhabitat-gw-indicators) · **License:** `cc-by-igo` · **Updated:** 2024-03-28 --- ## Abstract The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects. Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2024-03-28. Geographic scope: **GNB**. *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).* --- ## Dataset Characteristics | | | |---|---| | **Domain** | Public health | | **Unit of observation** | First-level administrative unit observations | | **Rows (total)** | 238 | | **Columns** | 13 (5 numeric, 8 categorical, 0 datetime) | | **Train split** | 190 rows | | **Test split** | 47 rows | | **Geographic scope** | GNB | | **Publisher** | United Nations Human Settlements Programmes, Data and Analytics Section | | **HDX last updated** | 2024-03-28 | --- ## Variables **Geographic** — `category` (Population, Slum dwellers, Streets), `indicator_friendly` (Average annual rate of change of population – Total, Rural population, Total population), `type_data` (p, 1000, n), `latitude` (range 11.865–12.0), `longitude` (range -15.5984–-15.0) and 3 others. **Outcome / Measurement** — `value` (range -0.08–7616.0). **Identifier / Metadata** — `name` (Guinea-bissau, #country+name, Bissau), `esa_source` (HDX), `esa_processed` (2026-04-10). **Other** — `indicator` (avg_annual_rate_change_percentage_total, rural_population, population). --- ## Quick Start ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unhabitat-gw-indicators") train = ds["train"].to_pandas() test = ds["test"].to_pandas() print(train.shape) train.head() ``` --- ## Schema | Column | Type | Null % | Range / Sample Values | |---|---|---|---| | `category` | object | 0.0% | Population, Slum dwellers, Streets | | `indicator` | object | 0.0% | avg_annual_rate_change_percentage_total, rural_population, population | | `indicator_friendly` | object | 0.0% | Average annual rate of change of population – Total, Rural population, Total population | | `type_data` | object | 0.0% | p, 1000, n | | `latitude` | float64 | 0.4% | 11.865 – 12.0 (mean 11.9994) | | `longitude` | float64 | 0.4% | -15.5984 – -15.0 (mean -15.0025) | | `region_id` | float64 | 0.4% | 289.0 – 289.0 (mean 289.0) | | `country_id` | object | 0.0% | GW, #country+code+v_iso2 | | `name` | object | 0.0% | Guinea-bissau, #country+name, Bissau | | `year` | float64 | 0.4% | 1950.0 – 2050.0 (mean 1999.3249) | | `value` | float64 | 0.4% | -0.08 – 7616.0 (mean 437.8822) | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-10 | --- ## Numeric Summary | Column | Min | Max | Mean | Median | |---|---|---|---|---| | `latitude` | 11.865 | 12.0 | 11.9994 | 12.0 | | `longitude` | -15.5984 | -15.0 | -15.0025 | -15.0 | | `region_id` | 289.0 | 289.0 | 289.0 | 289.0 | | `year` | 1950.0 | 2050.0 | 1999.3249 | 2000.0 | | `value` | -0.08 | 7616.0 | 437.8822 | 3.17 | --- ## 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`. 5 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 United Nations Human Settlements Programmes, Data and Analytics Section 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/unhabitat-gw-indicators) for the publisher's own methodology notes and caveats. --- ## Citation ```bibtex @dataset{hdx_africa_unhabitat_gw_indicators, title = {Guinea-bissau: - Demographic, Health, Education and Transport indicators}, author = {United Nations Human Settlements Programmes, Data and Analytics Section}, year = {2024}, url = {https://data.humdata.org/dataset/unhabitat-gw-indicators}, 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.*### 数据集元数据 annotations_creators: 无标注 language_creators: 现有数据源采集 language: 英语 license: 知识共享署名4.0协议（CC BY 4.0） multilinguality: 单语言 size_categories: 样本量小于1000 source_datasets: 原创数据集 task_categories: 表格分类、其他 task_ids: 无任务子类别 tags: 非洲、人道主义、人类数据交换平台（HDX）、非洲电羊（Electric Sheep Africa）、基线人口、教育、卫生、人道主义交换语言（HXL）、指标、交通、几内亚比绍（GNB） pretty_name: 几内亚比绍：人口、卫生、教育与交通指标数据集 dataset_info: splits: - name: train num_examples: 190 - name: test num_examples: 47 # 几内亚比绍：人口、卫生、教育与交通指标数据集 **发布方**：联合国人类住区规划署（United Nations Human Settlements Programme）数据与分析科 · **来源**：[人类数据交换平台（HDX）](https://data.humdata.org/dataset/unhabitat-gw-indicators) · **许可协议**：`cc-by-igo` · **更新时间**：2024-03-28 --- ## 摘要 本数据集收录的城市指标数据由联合国人居署全球城市观测站（Global Urban Observatory）分析、整理并发布，该机构支持各国政府、地方当局及民间社会组织开发城市指标、数据与统计资料。城市统计数据由各国统计主管部门通过家庭调查与人口普查收集。全球城市观测站团队会对调查与普查得到的城市指标统计数据进行分析与整理。此外，地方城市观测站也会收集、整理并分析城市数据，用于国家政策制定。人口统计数据由联合国经济和社会事务部世界城市化展望项目编制。 本数据集的每一行均代表一级行政单元的观测记录。该数据集最后一次在HDX平台更新的时间为2024年03月28日。地理覆盖范围：**GNB（几内亚比绍国家代码）**。 *本数据集已由[非洲电羊（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** | 公共卫生 | | **观测单元** | 一级行政单元观测记录 | | **总行数** | 238 | | **列数** | 13列（5列数值型，8列分类型，0列日期时间型） | | **训练集划分** | 190行 | | **测试集划分** | 47行 | | **地理覆盖范围** | GNB（几内亚比绍） | | **发布方** | 联合国人类住区规划署数据与分析科 | | **HDX平台最后更新时间** | 2024-03-28 | --- ## 变量 **地理类变量** — `category`（取值为人口、贫民窟居民、街道）、`indicator_friendly`（友好名称指标，如人口年度平均变化率——总人口、农村人口、总人口）、`type_data`（数据类型，如p、1000、n）、`latitude`（纬度范围：11.865~12.0）、`longitude`（经度范围：-15.5984~-15.0）及另外3个变量。 **结果/测量变量** — `value`（数值范围：-0.08~7616.0）。 **标识符/元数据变量** — `name`（名称，如几内亚比绍、#country+name、比绍）、`esa_source`（数据来源，如HDX）、`esa_processed`（数据处理时间，如2026-04-10）。 **其他变量** — `indicator`（指标名称，如avg_annual_rate_change_percentage_total、rural_population、population）。 --- ## 快速上手 python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-unhabitat-gw-indicators") 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 | |---|---|---|---| | `category` | object | 0.0% | 人口、贫民窟居民、街道 | | `indicator` | object | 0.0% | avg_annual_rate_change_percentage_total、rural_population、population | | `indicator_friendly` | object | 0.0% | 人口年度平均变化率——总人口、农村人口、总人口 | | `type_data` | object | 0.0% | p、1000、n | | `latitude` | float64 | 0.4% | 11.865 ~ 12.0（平均值：11.9994） | | `longitude` | float64 | 0.4% | -15.5984 ~ -15.0（平均值：-15.0025） | | `region_id` | float64 | 0.4% | 289.0 ~ 289.0（平均值：289.0） | | `country_id` | object | 0.0% | GW、#country+code+v_iso2 | | `name` | object | 0.0% | 几内亚比绍、#country+name、比绍 | | `year` | float64 | 0.4% | 1950.0 ~ 2050.0（平均值：1999.3249） | | `value` | float64 | 0.4% | -0.08 ~ 7616.0（平均值：437.8822） | | `esa_source` | object | 0.0% | HDX | | `esa_processed` | object | 0.0% | 2026-04-10 | --- ## 数值型变量统计摘要 | 列名 | 最小值 | 最大值 | 平均值 | 中位数 | |---|---|---|---|---| | `latitude` | 11.865 | 12.0 | 11.9994 | 12.0 | | `longitude` | -15.5984 | -15.0 | -15.0025 | -15.0 | | `region_id` | 289.0 | 289.0 | 289.0 | 289.0 | | `year` | 1950.0 | 2050.0 | 1999.3249 | 2000.0 | | `value` | -0.08 | 7616.0 | 437.8822 | 3.17 | --- ## 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。列名统一转换为小写并标准化为蛇形命名法。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。根据解析成功率（阈值>85%），将5列从字符串类型转换为数值型或日期时间型。本数据集使用固定随机种子（42）按照80/20的比例划分为训练集与测试集，并保存为Snappy压缩格式的Parquet文件。 --- ## 数据集局限性 - 本数据集源自联合国人类住区规划署数据与分析科，未经过非洲电羊的独立验证。 - 自动化清洗流程无法修正原始数据收集过程中存在的错报值、定义不一致或抽样偏差问题。 - 如需查看发布方提供的方法论说明与注意事项，请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/unhabitat-gw-indicators)。 --- ## 引用 bibtex @dataset{hdx_africa_unhabitat_gw_indicators, title = {几内亚比绍：人口、卫生、教育与交通指标数据集}, author = {联合国人类住区规划署数据与分析科}, year = {2024}, url = {https://data.humdata.org/dataset/unhabitat-gw-indicators}, note = {由非洲电羊（Electric Sheep Africa）重新打包为机器学习可用数据集（https://huggingface.co/electricsheepafrica)} } --- *[非洲电羊（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。*