Spatial Interpolation

Spatial Interpolation is a method of estimating values at unsampled locations based on known values at nearby sampled points. This technique creates continuous surfaces from discrete point data, filling in gaps where direct measurements are not available.

The process uses mathematical algorithms to predict values across space, considering factors such as distance, direction, and spatial relationships between known points. Spatial interpolation is essential for creating comprehensive spatial datasets from limited sample data, enabling analysis and visualization of continuous phenomena across entire study areas.

Practical Applications

• Weather forecasting and climate modeling from weather station data
• Soil property mapping for precision agriculture applications
• Air quality monitoring and pollution level estimation
• Groundwater level prediction for water resource management
• Elevation surface creation from topographic survey points

Related Terms

• Kriging
• Inverse Distance Weighting
• Surface Modeling
• Geostatistics
• Spatial Prediction