Skip to main content

Spatial interpolation in GIS

Spatial interpolation is a method used in GIS (Geographic Information System) to estimate values at unknown locations within a geographic area based on values observed at known locations. It is commonly used to create continuous surfaces or maps from discrete point data.

Different techniques of spatial interpolation are employed to make these estimations. Here are some commonly used methods:


1. Inverse Distance Weighting (IDW): IDW assigns weights to the known data points based on their distances from the unknown location. The closer points receive higher weights, and the values at the unknown location are calculated as a weighted average of the known values. IDW assumes that nearby points have a stronger influence on the unknown location than those farther away.

2. Kriging: Kriging is a more advanced interpolation technique that considers both spatial autocorrelation and statistical analysis. It generates a prediction surface by estimating a semivariogram model, which describes the spatial correlation of the data. Kriging provides estimates with optimal accuracy and takes into account the spatial variability and the relationships between the data points. Variants of kriging include ordinary kriging, which assumes a constant mean, and universal kriging, which incorporates a trend component.

3. Radial Basis Functions (RBF): RBF interpolation uses mathematical functions to model the spatial variation between known points. It fits a smooth surface through the data points and evaluates the function at the unknown locations to estimate their values. RBF methods can handle irregularly distributed data points and effectively capture local variations.

4. Natural Neighbor Interpolation: This method calculates the value at an unknown location by considering the proximity of that location to the known points. It creates Voronoi polygons around each known point and uses the area overlap between the unknown location and the polygons to assign weights. Natural neighbor interpolation provides smoother results and avoids abrupt changes between neighboring areas.

5. Spline Interpolation: Splines are mathematical functions that interpolate between known data points to create a smooth surface. They minimize overall curvature and provide a visually appealing result. Spline interpolation can be performed using different approaches such as ordinary splines, tension splines, or B-splines.

6. Trend Surface Analysis: Trend surface analysis examines the trend or pattern present in the data and uses it to estimate values at unknown locations. It fits a polynomial surface to the known points, capturing the general trend and allowing for prediction beyond the data extent. Trend surface analysis can be useful when there is a clear spatial trend or gradient in the data.



These are some commonly used techniques for spatial interpolation in GIS. The choice of method depends on factors such as the nature of the data, the spatial distribution, the desired level of accuracy, and the specific objectives of the analysis. It's important to assess the strengths and limitations of each technique and select the most appropriate method for the given data and analysis requirements.

Comments

Popular posts from this blog

Geologic and tectonic framework of the Indian shield

  Major Terms and Regions Explained 1. Indian Shield The Indian Shield refers to the ancient, stable core of the Indian Plate made of hard crystalline rocks. It comprises Archean to Proterozoic rocks that have remained tectonically stable over billions of years. Important Geological Features and Regions ▪️ Ch – Chhattisgarh Basin A sedimentary basin part of the Bastar Craton . Contains rocks of Proterozoic age , mainly sedimentary. Important for understanding the evolution of central India. ▪️ CIS – Central Indian Shear Zone A major tectonic shear zone , separating the Bundelkhand and Bastar cratons . It records intense deformation and metamorphism . Acts as a suture zone , marking ancient tectonic collisions. ▪️ GR – Godavari Rift A rift valley formed due to stretching and thinning of the Earth's crust. Associated with sedimentary basins and hydrocarbon resources . ▪️ M – Madras Block An Archean crustal block in...

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

Seismicity and Earthquakes, Isostasy and Gravity

1. Seismicity and Earthquakes in the Indian Subcontinent Key Concept: Seismicity Definition : The occurrence, frequency, and magnitude of earthquakes in a region. In India, seismicity is high due to active tectonic processes . Plate Tectonics 🌏 Indian Plate : Moves northward at about 5 cm/year. Collision with Eurasian Plate : Causes intense crustal deformation , mountain building (Himalayas), and earthquakes. This is an example of a continental-continental collision zone . Seismic Zones of India Classified into Zone II, III, IV, V (Bureau of Indian Standards, BIS). Zone V = highest hazard (e.g., Himalayas, Northeast India). Zone II = lowest hazard (e.g., parts of peninsular India). Earthquake Hazards ⚠️ Himalayas: prone to large shallow-focus earthquakes due to active thrust faulting. Northeast India: complex subduction and strike-slip faults . Examples: 1897 Shillong Earthquake (Magnitude ~8.1) 1950 Assam–Tib...

Vector geoprocessing - Clipping, Erase, identify, Union & Intersection

Think of your vector data (points, lines, polygons) like shapes drawn on a transparent sheet. Geoprocessing is just cutting, joining, or comparing those shapes to get new shapes or information. 1. Clipping ✂️ Imagine you have a big map and you only want to keep a part of it (like cutting a photo into a smaller rectangle). You use another shape (like the boundary of a district) to "clip" and keep only what is inside. Result: Only the data inside the clipping shape remains. 2. Erase 🚫 Opposite of clipping. You remove (erase) the area of one shape from another shape. Example: You have a city map and want to remove all the park areas from it. 3. Identify 🔍 This checks which features from one layer fall inside (or touch) another layer. Example: Identify all the schools inside a flood zone. 4. Union 🤝 Combines two shapes together and keeps everything from both. Works like stacking two transparent sheets and redrawing t...

vector data analysis in GIS Surface Analysis – Interpolation – IDW

1. Surface Analysis 🗺️ This is when we try to understand and visualize how a value changes across a surface (like land). The values might be temperature, rainfall, elevation, pollution levels, etc. We often start with only some points where we know the value, but we want to guess the values everywhere in between. 2. Interpolation 📍➡️📍 Interpolation is a way of estimating unknown values between known points. Imagine you know the temperature at a few weather stations, but you want to know the temperature everywhere in between. GIS uses math to "fill in the blanks" between the points. 3. IDW (Inverse Distance Weighted) 🎯 One popular interpolation method. The idea: Points that are closer to you have more influence than points farther away. Example: If you're standing between two rain gauges, the closer one's reading will affect your estimated rainfall more than the farther one. "Inverse Distance" means: The ...