This dataset covers statistical information on health systems in ASEAN countries. Details of each dataset are provided below. If you need access, please fill in the attached Data Application Form and send it to the email address: bolun@diinsider.com to submit your request.

Timor-Leste Health Service Accessibility Dataset

This dataset documents geospatial and service attributes of various medical and health facilities across Timor-Leste. It serves as a key reference for evaluating local public health coverage and planning sustainable development projects. The dataset contains 246 medical facility records, with 13 fields for each entry, including facility name, type, administrative division affiliation, and precise geographic coordinates. It systematically records facility name, type, administrative division, accessibility level, spatial distribution, and other structured information.

Cambodia Health Facility Dataset

This dataset provides geospatial information on health facilities in Cambodia, supported by OpenStreetMap (OSM). It includes standard GIS file formats: .shp (geometric data), .shx (index), .dbf (attribute table), .prj (projection information), and .cpg (encoding). Covering the entire territory of Cambodia, the dataset stores point locations and detailed attributes of all types of health care facilities, including hospitals, clinics, pharmacies, and health centers. It can be used to locate the nearest medical resources during natural disasters or public health crises, analyze the spatial equity and accessibility of health care resources in Cambodia, and assist governments or NGOs in health facility siting and resource allocation.

Myanmar Health Facility Dataset

This dataset provides approximate central point locations of public hospitals and hospital sites in Myanmar as of 2020. It was compiled by CDE Myanmar based on public reference lists and local knowledge, with geographic coordinates refined mainly using high-resolution satellite imagery/Google Satellite.

Thailand Health Facility Dataset

Derived from OpenStreetMap (OSM), this dataset contains point locations of various health facilities in Thailand, including hospitals, pharmacies, clinics, dentists, doctors, and health centers. Major hospitals include Hang Dong Hospital, Yasothon Hospital, and Nakhon Ratchasima Hospital. The dataset also covers numerous Subdistrict Health Promoting Hospitals, dental clinics (e.g., Bangkok Smile), cosmetic surgery clinics (e.g., MAXi Cosmetic Surgery), traditional/alternative medicine clinics, and pharmacies (chain brands such as Boots, Watsons, eXta Plus, and local private pharmacies). It can be applied to analyze the geographic distribution of medical resources in Thailand, rapidly locate medical points during natural disasters or humanitarian relief operations, and support site selection analysis for the pharmaceutical retail industry.

Indonesia Health Facility Dataset

This dataset provides point locations of medical facilities in Indonesia (IDN) in standard Shapefile (SHP) format for geographic information systems. It consists of the following core files that collectively define geospatial information: 

IDN_hospital_point.shp: Main graphic file storing geometric point locations of hospitals;

IDN_hospital_point.dbf: Attribute table with detailed hospital information (name, type, operator, etc.) ;

IDN_hospital_point.prj: Projection file defining the coordinate system (typically WGS 84) ;

IDN_hospital_point.shx: Index file for accelerated geometric data retrieval;

IDN_hospital_point.cpg: Character encoding specification file.

The dataset includes: 

Facility identification: hospital name, unique identifier (UUID/ID);

Geographic coordinates: longitude and latitude;

Classification: public/private ownership, general or specialized hospital;

Administrative affiliation: province, regency/city, and district;

Data sources: primarily the Ministry of Health of Indonesia (Kemenkes) and open data platforms such as OpenStreetMap .

These files can be directly loaded in QGIS, ArcGIS, or Python’s Geopandas library to visualize the precise distribution of hospitals across Indonesia on a map.