Add README.md

README.md

@@ -5,7 +5,7 @@ language_creators:
 - found
 language:
 - en
-license: other
+license: cc-by-sa-4.0
 multilinguality:
 - monolingual
 size_categories:
@@ -21,63 +21,145 @@ tags:
 - humanitarian
 - hdx
 - electric-sheep-africa
+- affected-population
 - demographics
-- health
+- flooding
+- hazards-and-risk
+- health-facilities
+- indicators
 - sdn
-pretty_name: Sudan - Subnational Demographic and Health Data
+pretty_name: "Sudan - Risk Assessment Indicators"
 dataset_info:
-  features:
-  - name: adm2_pcode
-    dtype: string
-  - name: adm_pcode
-    dtype: string
-  - name: female_pop
-    dtype: int64
-  - name: children_u5
-    dtype: int64
-  - name: female_u5
-    dtype: int64
-  - name: elderly
-    dtype: int64
-  - name: pop_u15
-    dtype: int64
-  - name: female_u15
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 13741
-    num_examples: 151
-  - name: test
-    num_bytes: 3458
-    num_examples: 38
-  download_size: 17728
-  dataset_size: 17199
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 151
+    - name: test
+      num_examples: 37
 ---
 
-# Sudan - Subnational Demographic and Health Data
+# Sudan - Risk Assessment Indicators
 
-**Publisher:** The DHS Program · **Source:** [HDX](https://data.humdata.org/dataset/dhs-subnational-data-for-sudan) · **License:** `hdx-other` · **Updated:** 2026-02-24
+**Publisher:** HeiGIT (Heidelberg Institute for Geoinformation Technology) · **Source:** [HDX](https://data.humdata.org/dataset/sudan---risk-assessment-indicators) · **License:** `cc-by-sa` · **Updated:** 2026-04-13
 
 ---
 
 ## Abstract
 
-Contains data from the [DHS data portal](https://api.dhsprogram.com/). There is also a dataset containing [Sudan - National Demographic and Health Data](https://data.humdata.org/dataset/dhs-data-for-sudan) on HDX.
+This dataset provides comprehensive **Risk Assessment Indicators** for **Sudan**, aggregated at **admin level 2** and 
+can in particular be used to perform a structured risk assessment for **flood** hazards.
+It includes demographic, environmental, infrastructure, accessibility, and hazard-related data to support disaster risk and resilience analysis.
 
-The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
+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.
 
-Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-24. Geographic scope: **SDN**.
+---
+
+### **Data Overview**
+
+- **Access to Services (`SDN_ADM2_access`)**  
+- **Facilities (`SDN_ADM2_facilities`)**  
+- **Coping Capacity (`SDN_ADM2_coping`)**  
+- **Demographics (`SDN_ADM2_demographics`)**  
+- **Rural Population (`SDN_ADM2_rural_population`)**  
+- **Vulnerability (`SDN_ADM2_vulnerability`)**  
+- **Flood Exposure (`SDN_ADM2_flood_exposure`)**  
+
+
+<p>&nbsp;</p>
+<p>&nbsp;</p>
+
+---
+
+### **Indicator Descriptions**
+
+#### **Access to Services (`SDN_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 (`SDN_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 (`SDN_ADM2_coping`)**
+Combines **Access to Services** and **Facilities** data to represent a district’s coping capacity.
+
+---
+
+#### **Demographics (`SDN_ADM2_demographics`)**
+Shows the population composition by age and gender.
+
+- **ADM2_PCODE** – Administrative division code (ADM2)
+- **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 (`SDN_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)
+- **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 (`SDN_ADM2_vulnerability`)**
+Combines **Demographics** and **Rural Population** indicators.
+
+---
+
+#### **Flood Exposure (`SDN_ADM2_flood_exposure`)**
+Shows population and facility exposure to flooding at 30 cm depth for multiple return periods.
+
+- **ADM2_PCODE** – Administrative division code (ADM2)
+- **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)
+
+
+---
+
+
+
+### **QGIS Plugin Risk Assessment Inputs**
+
+- **Coping Capacity** = Access + Facilities  
+- **Vulnerability** = Demographics + Rural Population  
+- **Exposure** = Vulnerable Population + Facilities exposed to Floods
+
+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)!
+
+Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-04-13. Geographic scope: **SDN**.
 
 *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
 
@@ -88,26 +170,26 @@ Each row in this dataset represents first-level administrative unit observations
 | | |
 |---|---|
 | **Domain** | Public health |
-| **Unit of observation** | First-level administrative unit observations |
-| **Rows (total)** | 42 |
-| **Columns** | 32 (15 numeric, 17 categorical, 0 datetime) |
-| **Train split** | 33 rows |
-| **Test split** | 8 rows |
+| **Unit of observation** | Tabular records |
+| **Rows (total)** | 189 |
+| **Columns** | 10 (6 numeric, 4 categorical, 0 datetime) |
+| **Train split** | 151 rows |
+| **Test split** | 37 rows |
 | **Geographic scope** | SDN |
-| **Publisher** | The DHS Program |
-| **HDX last updated** | 2026-02-24 |
+| **Publisher** | HeiGIT (Heidelberg Institute for Geoinformation Technology) |
+| **HDX last updated** | 2026-04-13 |
 
 ---
 
 ## Variables
 
-**Geographic** — `iso3` (SDN), `location` (Khartoum, Northern, Eastern), `dhs_countrycode` (SD), `countryname` (Sudan), `surveyyear` (range 1990.0–1990.0) and 8 others.
+**Geographic** — `elderly` (range 102.0–42115.0).
 
-**Outcome / Measurement** — `value` (range 0.2–178.0), `istotal` (range 0.0–0.0).
+**Demographic** — `female_pop` (range 1892.0–633763.0), `female_u5` (range 300.0–88537.0), `pop_u15` (range 1657.0–484448.0), `female_u15` (range 807.0–236599.0).
 
-**Identifier / Metadata** — `dataid` (range 117286.0–7925878.0), `indicatorid` (FE_FRTR_W_TFR, FP_CUSM_W_ANY, FP_CUSM_W_MOD), `characteristicid` (range 443001.0–443006.0), `characteristiclabel` (Khartoum, Northern, Eastern), `ispreferred` (range 1.0–1.0) and 3 others.
+**Identifier / Metadata** — `adm2_pcode` (SD07090, SD15030, SD04125), `adm_pcode` (SD07090, SD15030, SD04125), `esa_source` (HDX), `esa_processed` (2026-04-27).
 
-**Other** — `indicator` (Total fertility rate 15-49, Married women currently using any method of contraception, Married women currently using any modern method of contraception), `precision` (range 0.0–1.0), `indicatororder` (range 11763080.0–104336020.0), `characteristicorder` (range 1443001.0–1443006.0), `denominatorweighted` (range 365.0–1480.0) and 4 others.
+**Other** — `children_u5` (range 613.0–179878.0).
 
 ---
 
@@ -130,38 +212,16 @@ train.head()
 
 | Column | Type | Null % | Range / Sample Values |
 |---|---|---|---|
-| `iso3` | object | 0.0% | SDN |
-| `location` | object | 0.0% | Khartoum, Northern, Eastern |
-| `dataid` | int64 | 0.0% | 117286.0 – 7925878.0 (mean 3119772.7381) |
-| `indicator` | object | 0.0% | Total fertility rate 15-49, Married women currently using any method of contraception, Married women currently using any modern method of contraception |
-| `value` | float64 | 0.0% | 0.2 – 178.0 (mean 35.8429) |
-| `precision` | int64 | 0.0% | 0.0 – 1.0 (mean 0.7143) |
-| `dhs_countrycode` | object | 0.0% | SD |
-| `countryname` | object | 0.0% | Sudan |
-| `surveyyear` | int64 | 0.0% | 1990.0 – 1990.0 (mean 1990.0) |
-| `surveyid` | object | 0.0% | SD1990DHS |
-| `indicatorid` | object | 0.0% | FE_FRTR_W_TFR, FP_CUSM_W_ANY, FP_CUSM_W_MOD |
-| `indicatororder` | int64 | 0.0% | 11763080.0 – 104336020.0 (mean 49915757.1429) |
-| `indicatortype` | object | 0.0% | I |
-| `characteristicid` | int64 | 0.0% | 443001.0 – 443006.0 (mean 443003.5) |
-| `characteristicorder` | int64 | 0.0% | 1443001.0 – 1443006.0 (mean 1443003.5) |
-| `characteristiccategory` | object | 0.0% | Region |
-| `characteristiclabel` | object | 0.0% | Khartoum, Northern, Eastern |
-| `byvariableid` | int64 | 0.0% | 0.0 – 14003.0 (mean 4000.8571) |
-| `byvariablelabel` | object | 71.4% |  |
-| `istotal` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
-| `ispreferred` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
-| `sdrid` | object | 0.0% |  |
-| `regionid` | object | 0.0% |  |
-| `surveyyearlabel` | object | 0.0% |  |
-| `surveytype` | object | 0.0% |  |
-| `denominatorweighted` | float64 | 71.4% | 365.0 – 1480.0 (mean 900.0) |
-| `denominatorunweighted` | float64 | 71.4% | 365.0 – 1480.0 (mean 900.0) |
-| `cilow` | float64 | 71.4% | 49.0 – 144.0 (mean 87.75) |
-| `cihigh` | float64 | 71.4% | 71.0 – 212.0 (mean 126.25) |
-| `levelrank` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
-| `esa_source` | object | 0.0% |  |
-| `esa_processed` | object | 0.0% |  |
+| `adm2_pcode` | object | 0.0% | SD07090, SD15030, SD04125 |
+| `adm_pcode` | object | 0.0% | SD07090, SD15030, SD04125 |
+| `female_pop` | int64 | 0.0% | 1892.0 – 633763.0 (mean 111213.746) |
+| `children_u5` | int64 | 0.0% | 613.0 – 179878.0 (mean 32217.2222) |
+| `female_u5` | int64 | 0.0% | 300.0 – 88537.0 (mean 15752.8148) |
+| `elderly` | int64 | 0.0% | 102.0 – 42115.0 (mean 8196.8995) |
+| `pop_u15` | int64 | 0.0% | 1657.0 – 484448.0 (mean 89555.8254) |
+| `female_u15` | int64 | 0.0% | 807.0 – 236599.0 (mean 42981.0) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-27 |
 
 ---
 
@@ -169,21 +229,12 @@ train.head()
 
 | Column | Min | Max | Mean | Median |
 |---|---|---|---|---|
-| `dataid` | 117286.0 | 7925878.0 | 3119772.7381 | 1137980.0 |
-| `value` | 0.2 | 178.0 | 35.8429 | 10.15 |
-| `precision` | 0.0 | 1.0 | 0.7143 | 1.0 |
-| `surveyyear` | 1990.0 | 1990.0 | 1990.0 | 1990.0 |
-| `indicatororder` | 11763080.0 | 104336020.0 | 49915757.1429 | 41633090.0 |
-| `characteristicid` | 443001.0 | 443006.0 | 443003.5 | 443003.5 |
-| `characteristicorder` | 1443001.0 | 1443006.0 | 1443003.5 | 1443003.5 |
-| `byvariableid` | 0.0 | 14003.0 | 4000.8571 | 0.0 |
-| `istotal` | 0.0 | 0.0 | 0.0 | 0.0 |
-| `ispreferred` | 1.0 | 1.0 | 1.0 | 1.0 |
-| `denominatorweighted` | 365.0 | 1480.0 | 900.0 | 901.5 |
-| `denominatorunweighted` | 365.0 | 1480.0 | 900.0 | 901.5 |
-| `cilow` | 49.0 | 144.0 | 87.75 | 76.5 |
-| `cihigh` | 71.0 | 212.0 | 126.25 | 121.0 |
-| `levelrank` | 1.0 | 1.0 | 1.0 | 1.0 |
+| `female_pop` | 1892.0 | 633763.0 | 111213.746 | 76236.0 |
+| `children_u5` | 613.0 | 179878.0 | 32217.2222 | 23370.0 |
+| `female_u5` | 300.0 | 88537.0 | 15752.8148 | 11391.0 |
+| `elderly` | 102.0 | 42115.0 | 8196.8995 | 5563.0 |
+| `pop_u15` | 1657.0 | 484448.0 | 89555.8254 | 66467.0 |
+| `female_u15` | 807.0 | 236599.0 | 42981.0 | 31213.0 |
 
 ---
 
@@ -195,10 +246,9 @@ Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Colu
 
 ## Limitations
 
-- Data originates from The DHS Program and has not been independently validated by ESA.
+- Data originates from HeiGIT (Heidelberg Institute for Geoinformation Technology) and has not been independently validated by ESA.
 - Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- The following columns have >20% missing values and should be treated with caution in modelling: `byvariablelabel`, `denominatorweighted`, `denominatorunweighted`, `cilow`, `cihigh`.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/dhs-subnational-data-for-sudan) for the publisher's own methodology notes and caveats.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/sudan---risk-assessment-indicators) for the publisher's own methodology notes and caveats.
 
 ---
 
@@ -206,10 +256,10 @@ Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Colu
 
 ```bibtex
 @dataset{hdx_africa_demographics_sudan,
-  title     = {Sudan - Subnational Demographic and Health Data},
-  author    = {The DHS Program},
+  title     = {Sudan - Risk Assessment Indicators},
+  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
   year      = {2026},
-  url       = {https://data.humdata.org/dataset/dhs-subnational-data-for-sudan},
+  url       = {https://data.humdata.org/dataset/sudan---risk-assessment-indicators},
   note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
 }
 ```