Interpolation

Interpolation is a core spatial analysis technique in Geographic Information Systems (GIS) used to estimate values at locations where direct measurements are unavailable. It predicts unknown values by analyzing nearby known data points, creating continuous surfaces from discrete observations. This process transforms scattered datasets into meaningful geographic information, improving the completeness and accuracy of spatial analysis. Interpolation is widely applied in terrain modeling, climate studies, hydrology, agriculture, environmental monitoring, urban planning, and natural resource management. It is commonly used to generate digital elevation models (DEMs), rainfall and temperature maps, pollution distribution maps, groundwater level surfaces, and other predictive spatial datasets. Popular interpolation methods include Inverse Distance Weighting (IDW), Kriging, and Spline, each offering unique advantages depending on data distribution, spatial relationships, and analysis objectives. By filling gaps between sampled locations, identifying spatial patterns, and producing smooth, reliable surface models, interpolation enables better visualization, accurate decision-making, and efficient planning. It plays a vital role in transforming limited field measurements into comprehensive geographic information that supports scientific research, engineering projects, environmental management, and GIS-based decision support systems.