SPATIAL INTERPOLATION DEFINED Spatial interpolation is the process of estimating unknown values at specific geographic locations using data from known points in the same area. It involves generating a continuous surface (such as temperature, elevation, or pollution levels) from discrete data points by making assumptions about how the values change spatially. This method is crucial in GIS for visualizing patterns, predicting phenomena, and filling gaps in spatial datasets. THE 7 TECHNIQUES 1. 📍Inverse Distance Weighting (IDW) Principle: Nearby points have a greater influence. Best For: Smooth, gradual surfaces. 👍 Pro: Simple and intuitive. 👎 Con: Can produce unrealistic "bullseye" patterns. 2. 📊 Kriging Principle: Uses statistical models to account for spatial correlation. Best For: Data with known spatial relationships. 👍 Pro: Provides accuracy and uncertainty estimates. 👎 Con: Requires assumptions about data variability. 3. 🌀 Spline Interpolation Principle: Fits smooth curves through data points. Best For: Smooth and gradually changing phenomena. 👍 Pro: Produces smooth surfaces. 👎 Con: May oversmooth complex datasets. 4. 🌐 Natural Neighbor Principle: Uses weighted averages of surrounding points. Best For: Irregularly spaced data. 👍 Pro: No sudden jumps in the output surface. 👎 Con: Sensitive to the spatial arrangement of points. 5. 📈 Trend Surface Analysis Principle: Fits a polynomial surface over the data. Best For: Large-scale, broad trends. 👍 Pro: Good for capturing general patterns. 👎 Con: Misses local detail. 6. 🔲 Nearest Neighbor (Thiessen Polygons) Principle: Assigns unknown points to the nearest known value. Best For: Categorizing regions. 👍 Pro: Simple and fast. 👎 Con: Produces abrupt changes between zones. 7. 📐 Radial Basis Functions (RBF) Principle: Uses flexible mathematical functions to interpolate values. Best For: Complex, non-linear data. 👍 Pro: Produces very smooth surfaces. 👎 Con: Computationally intensive. 🤔 CHOOSING THE RIGHT TECHNIQUE: 🌿 Smooth and Gradual Data? Use IDW or Spline. 📏 Statistical Accuracy? Use Kriging. ⚖️ Irregular Data? Use Natural Neighbor or RBF. 📊 Categorization or Sharp Boundaries? Use Nearest Neighbor