Upload dataset folder

README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
+tags:
+- tabular
+- csv
+- africa
+- madagascar
+- official-statistics
+- open-data
+- health
+- population
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-00000-of-00001.parquet
+pretty_name: "Madagascar - Risk Assessment Indicators | Africa (Madagascar official open data)"
+---
+
+# Madagascar - Risk Assessment Indicators | Africa (Madagascar official open data)
+
+119 rows - 1 Africa country - not-applicable - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
+
+![rows](https://img.shields.io/badge/rows-119-blue)
+![countries](https://img.shields.io/badge/countries-1-green)
+![years](https://img.shields.io/badge/years-not-applicable-orange)
+![indicators](https://img.shields.io/badge/indicators-0-purple)
+![license](https://img.shields.io/badge/license-cc-by-sa-4.0-lightgrey)
+
+## TL;DR
+
+This dataset packages one official `CSV` resource from **Madagascar** as
+ML-ready Parquet. The source file is the provenance boundary; all usable
+indicators or tabular columns from the resource stay together in this repo.
+
+## About the source
+
+- **Source:** [Madagascar - Risk Assessment Indicators](https://data.humdata.org/dataset/madagascar---risk-assessment-indicators)
+- **Publisher:** HeiGIT (Heidelberg Institute for Geoinformation Technology)
+- **Resource:** [MDG_ADM2_vulnerability.csv](https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/mdg/MDG_ADM2_vulnerability.csv)
+- **Format:** `CSV`
+- **License:** [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)
+- **Packaging mode:** `tabular_resource`
+
+## Geographic coverage
+
+1 Africa country:
+
+| Country | Rows | First year | Last year | Name |
+|---------|-----:|-----------:|----------:|------|
+| `MDG` | 119 | n/a | n/a | `Madagascar` |
+
+## Indicators or Resource Contents
+
+- This source file is packaged as a normalized tabular resource.
+
+## Schema
+
+| Column | Type | Description | Example |
+|--------|------|-------------|---------|
+| `source_record_id` | `string` | Stable row identifier for tabular resources. | `05238196-0d6e-4478-ba66-41e944892d0c:0` |
+| `country_iso3` | `category` | ISO3 country code. | `MDG` |
+| `country_name` | `category` | Country name. | `Madagascar` |
+| `adm2_pcode` | `string` | Source column. | `MG11101001A` |
+| `total_pop` | `int64` | Source column. | `214985` |
+| `female_pop` | `int64` | Source column. | `107136` |
+| `children_u5` | `int64` | Source column. | `29406` |
+| `female_u5` | `int64` | Source column. | `14503` |
+| `elderly` | `int64` | Source column. | `7772` |
+| `pop_u15` | `int64` | Source column. | `81171` |
+| `female_u15` | `int64` | Source column. | `40056` |
+| `wra_pop` | `int64` | Source column. | `53883` |
+| `dependency_ratio` | `float64` | Source column. | `70.57` |
+| `total_pop_rural` | `int64` | Source column. | `0` |
+| `female_pop_rural` | `int64` | Source column. | `0` |
+| `children_u5_rural` | `int64` | Source column. | `0` |
+| `female_u5_rural` | `int64` | Source column. | `0` |
+| `elderly_rural` | `int64` | Source column. | `0` |
+| `pop_u15_rural` | `int64` | Source column. | `0` |
+| `female_u15_rural` | `int64` | Source column. | `0` |
+| `wra_pop_rural` | `int64` | Source column. | `0` |
+| `dependency_ratio_rural` | `float64` | Source column. | `0.0` |
+| `rural_pop_perc` | `float64` | Source column. | `0.0` |
+| `adm_pcode` | `string` | Source column. | `MG11101001A` |
+| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `` |
+| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `` |
+| `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `` |
+| `source_provider` | `category` | Publishing organization. | `HeiGIT (Heidelberg Institute for Geoinformation Technology)` |
+| `source_dataset` | `category` | Source package title. | `Madagascar - Risk Assessment Indicators` |
+| `source_resource` | `category` | Source resource title. | `MDG_ADM2_vulnerability.csv` |
+| `source_package_id` | `category` | CKAN package UUID. | `9ccb6e07-c40b-4c3c-a774-3905d0d6a7bf` |
+| `source_resource_id` | `category` | CKAN resource UUID. | `05238196-0d6e-4478-ba66-41e944892d0c` |
+| `source_url` | `category` | Original source resource URL. | `https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/` |
+| `license_id` | `category` | Source license identifier. | `cc-by-sa` |
+| `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-30T05:38:32Z` |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("electricsheepafrica/africa-madagascar-madagascar-risk-assessment-indicators-dc5de34e")
+df = ds["train"].to_pandas()
+print(df.head())
+```
+
+### Filter to one country
+
+```python
+sample_country = df[df["country_iso3"] == "MDG"]
+```
+
+### Work with indicators
+
+```python
+if "indicator_id" in df.columns:
+    print(df["indicator_id"].value_counts().head())
+    sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
+```
+
+## Citation
+
+```bibtex
+@misc{electric_sheep_africa_africa_madagascar_madagascar_risk_assessment_indicators_dc5de34e_2026,
+  title        = {Madagascar - Risk Assessment Indicators | Africa (Madagascar official open data)},
+  author       = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
+  year         = {2026},
+  url          = {https://data.humdata.org/dataset/madagascar---risk-assessment-indicators},
+  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-madagascar-madagascar-risk-assessment-indicators-dc5de34e}}
+}
+```
+
+## License
+
+Released under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/).
+
+Original data (c) HeiGIT (Heidelberg Institute for Geoinformation Technology). When using this dataset, please cite both the
+original source above and the Electric Sheep Africa repackaging.
+
+## About Electric Sheep
+
+Electric Sheep Africa is part of the Electric Sheep mission: a unified,
+ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
+open sources, normalize the schemas, package as Parquet, and publish with
+consistent dataset cards so researchers and developers can use `load_dataset()`
+to start working in seconds.
+
+Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
+
+---
+
+Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL:
+https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/mdg/MDG_ADM2_vulnerability.csv

data/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:26f89950d869a7784d1112c9d0b5dc2247848e01f4c302517f3e678684b58a3c
+size 33493

metadata/source_snapshot.json

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+{
+  "columns": [
+    "source_record_id",
+    "country_iso3",
+    "country_name",
+    "adm2_pcode",
+    "total_pop",
+    "female_pop",
+    "children_u5",
+    "female_u5",
+    "elderly",
+    "pop_u15",
+    "female_u15",
+    "wra_pop",
+    "dependency_ratio",
+    "total_pop_rural",
+    "female_pop_rural",
+    "children_u5_rural",
+    "female_u5_rural",
+    "elderly_rural",
+    "pop_u15_rural",
+    "female_u15_rural",
+    "wra_pop_rural",
+    "dependency_ratio_rural",
+    "rural_pop_perc",
+    "adm_pcode",
+    "source_period_start_year",
+    "source_period_end_year",
+    "source_period_label",
+    "source_provider",
+    "source_dataset",
+    "source_resource",
+    "source_package_id",
+    "source_resource_id",
+    "source_url",
+    "license_id",
+    "retrieved_at"
+  ],
+  "generated_at": "2026-08-30T05:56:30Z",
+  "indicator_count": 0,
+  "mode": "tabular_resource",
+  "repo_id": "electricsheepafrica/africa-madagascar-madagascar-risk-assessment-indicators-dc5de34e",
+  "rows": 119,
+  "source": {
+    "api_base_url": "https://data.humdata.org/api/3/action",
+    "country_iso3": "MDG",
+    "country_name": "Madagascar",
+    "group_names": "mdg",
+    "license_id": "cc-by-sa",
+    "license_title": "Creative Commons Attribution Share-Alike (CC BY-SA)",
+    "license_url": "http://www.opendefinition.org/licenses/cc-by-sa",
+    "organization_name": "heidelberg-institute-for-geoinformation-technology",
+    "organization_title": "HeiGIT (Heidelberg Institute for Geoinformation Technology)",
+    "package_id": "9ccb6e07-c40b-4c3c-a774-3905d0d6a7bf",
+    "package_name": "madagascar---risk-assessment-indicators",
+    "package_notes": "This dataset provides comprehensive **Risk Assessment Indicators** for **Madagascar**, aggregated at **admin level 2** and can in particular be used to perform a structured risk assessment for **flood** and **cyclone** hazards. It includes demographic, environmental, infrastructure, accessibility, and hazard-related data to support disaster risk and resilience analysis. All layers are derived from [HeiGIT’s GAIA Pipeline](https://giscience.github.io/gis-training-resource-center/content/GIS_AA/en_gaia_indicators_processing.html), integrating open data sources such as [WorldPop](https://www.worldpop.org/), [OpenStreetMap](https://www.openstreetmap.org/), and [Google Earth Engine](https://earthengine.google.com/) based on [HDX COD-AB](https://data.humdata.org/dataset/?q=cod-ab) boundaries. --- ### **Data Overview** - **Access to Services (`MDG_ADM2_access`)** - **Facilities (`MDG_ADM2_facilities`)** - **Coping Capacity (`MDG_ADM2_coping`)** - **Demographics (`MDG_ADM2_demographics`)** - **Rural Population (`MDG_ADM2_rural_population`)** - **Vulnerability (`MDG_ADM2_vulnerability`)** - **Flood Exposure (`MDG_ADM2_flood_exposure`)** - **Cyclone Exposure (`MDG_ADM2_cyclone_exposure`)** <p>&nbsp;</p> <p>&nbsp;</p> --- ### **Indicator Descriptions** #### **Access to Services (`MDG_ADM2_access`)** Represents the share of the population with access to key facilities within defined distances or travel times. - **ADM2_PCODE** – Administrative division code (ADM2) - **access_pop_education_5km / 10km / 20km** – Population within 5, 10, and 20 km of educational facilities - **access_pop_hospitals_30min / 1h / 2h** – Population within 30 minutes, 1 hour, and 2 hours of a hospital - **access_pop_primary_healthcare_30min / 1h / 2h** – Population within 30 minutes, 1 hour, and 2 hours of a primary health care facility Data Source: [openrouteservice (ORS)](https://openrouteservice.org/) --- #### **Facilities (`MDG_ADM2_facilities`)** Counts of essential service facilities within each district. - **ADM2_PCODE** – Administrative division code (ADM2) - **education_count** – Number of educational facilities - **hospitals_count** – Number of hospitals - **primary_healthcare_count** – Number of primary health care facilities Data Source: [OpenStreetMap (OSM)](https://www.openstreetmap.org) --- #### **Coping Capacity (`MDG_ADM2_coping`)** Combines **Access to Services** and **Facilities** data to represent a district’s coping capacity. --- #### **Demographics (`MDG_ADM2_demographics`)** Shows the population composition by age and gender. - **ADM2_PCODE** – Administrative division code (ADM2) - **total_pop** – Total population - **female_pop** – Total female population - **children_u5** – Population under 5 years old - **female_u5** – Female population under 5 years old - **elderly** – Population aged 65 and older - **pop_u15** – Population under 15 years old - **female_u15** – Female population under 15 years old Data Source: [Worldpop](https://www.worldpop.org/) --- #### **Rural Population (`MDG_ADM2_rural_population`)** Same demographic breakdown as above, but limited to rural populations. Rural areas are those outside urban extents, typically characterized by lower population density, agricultural or natural land use, and limited infrastructure compared to urban centers. - **ADM2_PCODE** – Administrative division code (ADM2) - **total_pop_rural**, **female_pop_rural**, **children_u5_rural**, **female_u5_rural**, **elderly_rural**, **pop_u15_rural**, **female_u15_rural** – Rural demographic counts - **rural_pop_perc** – Percentage of total population living in rural areas Data Source: [Global Human Settlement Layer (GHSL)](https://human-settlement.emergency.copernicus.eu/datasets.php) --- #### **Vulnerability (`MDG_ADM2_vulnerability`)** Combines **Demographics** and **Rural Population** indicators. --- #### **Flood Exposure (`MDG_ADM2_flood_exposure`)** Shows population and facility exposure to flooding at 30 cm depth for multiple return periods. - **ADM2_PCODE** – Administrative division code (ADM2) - **total_pop_30cm**, **female_pop_30cm**, **children_u5_30cm**, **female_u5_30cm**, **elderly_30cm**, **pop_u15_30cm**, **female_u15_30cm** – Exposed population by group - **education_30cm_pct / count**, **hospitals_30cm_pct / count**, **primary_healthcare_30cm_pct / count** – Facility exposure (percentage and count) Data Source: [The Joint Research Centre (JRC)](https://data.jrc.ec.europa.eu/collection/id-0054) --- #### **Cyclone Exposure (`MDG_ADM2_cyclone_exposure`)** Represents the exposure of populations and facilities to cyclones, based on historical cyclone tracks and intensity categories (1–3). Vulnerable populations and facilities are quantified per admin unit. - **ADM2_PCODE** – Administrative division code (ADM2) - **kt34_total_pop_cat1 / cat2 / cat3**, **kt34_female_pop_cat1 / cat2 / cat3**, **kt34_children_u5_cat1 / cat2 / cat3**, etc. – Population exposed to cyclone categories 1–3 - **kt34_education_perc / count_cat1 / cat2 / cat3**, **kt34_hospitals_perc / count_cat1 / cat2 / cat3**, **kt34_primary_healthcare_perc / count_cat1 / cat2 / cat3** – Facilities exposed to cyclone categories - **kt34_evac_time_minutes_mean / max / median** – Mean, max, and median travel time (minutes) from at-risk areas to safe zones - **kt34_pixels_at_risk** – Number of pixels at risk from cyclone exposure Data Source: [IBTrACS – NOAA International Best Track Archive for Climate Stewardship](https://www.ncei.noaa.gov/products/international-best-track-archive-for-climate-stewardship-ibtracs) <p>&nbsp;</p> <p>&nbsp;</p> --- ### **QGIS Plugin Risk Assessment Inputs** - **Coping Capacity** = Access + Facilities - **Vulnerability** = Demographics + Rural Population - **Exposure** = Vulnerable Population + Facilities exposed to Floods and Cyclones This dataset is part of HeiGIT’s **Risk Assessment Indicator Collection** on HDX. See more at [HeiGIT on HDX](https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology) and learn about HeiGIT’s research at [HeiGIT](https://heigit.org/). We are happy to hear about your use-cases — contact us at [communications@heigit.org](mailto:communications@heigit.org)!",
+    "package_page_url": "https://data.humdata.org/dataset/madagascar---risk-assessment-indicators",
+    "package_title": "Madagascar - Risk Assessment Indicators",
+    "portal_url": "https://data.humdata.org",
+    "resource_description": "MDG_ADM2_vulnerability.csv - Risk assessment indicator for Madagascar",
+    "resource_format": "CSV",
+    "resource_id": "05238196-0d6e-4478-ba66-41e944892d0c",
+    "resource_last_modified": "2026-05-07T09:35:04",
+    "resource_name": "MDG_ADM2_vulnerability.csv",
+    "resource_position": "1",
+    "resource_url": "https://hot.storage.heigit.org/heigit-hdx-public/risk_assessment_inputs/mdg/MDG_ADM2_vulnerability.csv",
+    "tag_names": "affected population,cyclones-hurricanes-typhoons,demographics,flooding,hazards and risk,health facilities,indicators"
+  },
+  "year_max": null,
+  "year_min": null
+}