Myanmar - Risk Assessment Indicators


This dataset provides comprehensive **Risk Assessment Indicators** for **Myanmar**, 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 (`MMR_ADM2_access`)**  
- **Facilities (`MMR_ADM2_facilities`)**  
- **Coping Capacity (`MMR_ADM2_coping`)**  
- **Demographics (`MMR_ADM2_demographics`)**  
- **Rural Population (`MMR_ADM2_rural_population`)**  
- **Vulnerability (`MMR_ADM2_vulnerability`)**  
- **Flood Exposure (`MMR_ADM2_flood_exposure`)**  
- **Cyclone Exposure (`MMR_ADM2_cyclone_exposure`)**

<p>&nbsp;</p>
<p>&nbsp;</p>

---

### **Indicator Descriptions**

#### **Access to Services (`MMR_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 (`MMR_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 (`MMR_ADM2_coping`)**
Combines **Access to Services** and **Facilities** data to represent a district’s coping capacity.

---

#### **Demographics (`MMR_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 (`MMR_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 (`MMR_ADM2_vulnerability`)**
Combines **Demographics** and **Rural Population** indicators.

---

#### **Flood Exposure (`MMR_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)


---


#### **Cyclone Exposure (`MMR_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_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

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)!


Tags: affected population, cyclones-hurricanes-typhoons, demographics, flooding, hazards and risk, health facilities, indicators
Organization: HeiGIT (Heidelberg Institute for Geoinformation Technology)