Dataset on Health Facilities of Nepal

 

Overview of the Dataset

The Health Facilities of Nepal dataset presents geospatial point data of registered health institutions as of the year 2015. Each point on the map represents a health facility such as hospitals, health posts, and clinics distributed across the diverse geographical terrain of Nepal.

Key Features:

1. Geographic Coordinates: Each health facility is marked using GPS coordinates.
2. Coordinate Reference System: The dataset uses the WGS 1984 datum with longitude/latitude coordinate reference.
3. Format: The dataset is provided in Shapefile (*.shp) format, which is widely used for storing geospatial vector data.

Source and Sponsorship

1. Data Source: The dataset was developed and provided by the Survey Department of Nepal, a key institution responsible for national mapping and geographic data.
2. Data Sponsor: The World Health Organization (WHO) supported the development and sharing of this dataset, emphasizing its importance in global health planning and disaster response.
3. Access Point: This dataset is made openly available through the Humanitarian Data Exchange (HDX), ensuring that it reaches a wide audience of data users.

Why This Dataset Matters

Nepal’s topography poses significant challenges to healthcare access. Having an open and accurate dataset of health facilities helps in:

1. Healthcare Planning: Identify underserved areas and plan new facilities.
2. Emergency Response: Aid coordination and delivery of medical services during natural disasters like earthquakes or floods.
3. Research and Analysis: Study geographic disparities, facility distribution, and their correlation with health outcomes.
4. Policy Making: Inform evidence-based decisions at the local and national levels.
5. How to Use the Dataset

To use this dataset, users can:

1. Download the shapefile from HDX: Nepal Health Facilities Dataset
2. Load it into GIS software like QGIS or ArcGIS.
3. Overlay it with other spatial data (e.g., population, road networks, elevation) for comprehensive spatial analysis.
4. Export or convert it into other formats (e.g., CSV, GeoJSON) for integration with data visualization or machine learning tools.

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