Geospatial Data refers to information that describes the location and attributes of objects or phenomena on Earth. This data is typically represented as coordinates (latitude and longitude) and can include additional information about physical, environmental, or cultural characteristics. Geospatial data is critical for fields such as geography, urban planning, environmental science, and location-based services.

Types of Geospatial Data:
1. Vector Data: Represents features as points, lines, or polygons (e.g., city locations, roads, or property boundaries).
2. Raster Data: Represents data as grids or pixels, such as satellite imagery or elevation maps.
3. Attribute Data: Provides additional details about spatial features, like population density or vegetation type.

Sources of Geospatial Data:
- Global Navigation Satellite Systems (GNSS): Includes GPS for precise location data.
- Remote Sensing: Satellite and aerial imagery for large-scale data collection.
- GIS Databases: Geographic Information Systems store and manage spatial data for analysis.

Applications:
- Urban Planning: Designing infrastructure and zoning based on population and environmental data.
- Disaster Management: Mapping areas at risk for floods, earthquakes, or wildfires.
- E-commerce: Optimizing delivery routes and targeting location-based marketing.

Benefits:
- Enhances decision-making by visualizing complex spatial relationships.
- Supports predictive analytics, such as forecasting traffic patterns or climate changes.

As geospatial data becomes increasingly accessible, it is integral to solving global challenges and optimizing systems across industries.