Skip to main content

Thiessen polygons or Voronoi polygons or Thiessen tessellation

Thiessen polygons, also known as Voronoi polygons or Thiessen tessellation, are a fundamental concept in GIS that define the spatial extent of influence or control of a set of points or observation sites. They are named after the American meteorologist Alfred H. Thiessen, who introduced the concept in 1911.

The basic idea behind Thiessen polygons is to partition a geographic space into contiguous polygons based on proximity to a set of input points. Each polygon is assigned to the nearest point, and all locations within that polygon are closer to that particular point than to any other point in the dataset.

The construction of Thiessen polygons involves connecting the midpoints between each pair of adjacent points, forming perpendicular bisectors. These bisectors are extended to create a network of lines that delimit the boundaries of the polygons. Each polygon encompasses the area that is closer to its associated point than to any other point.

The resulting Thiessen polygons have several applications in GIS:

1. Spatial interpolation: Thiessen polygons can be used to interpolate values between points. The value at any location within a Thiessen polygon is assumed to be equal to the value at the associated point.

2. Network analysis: Thiessen polygons can be used to determine the service area or coverage of specific facilities, such as determining which customers are closest to a particular store or service location.

3. Hydrology and catchment delineation: Thiessen polygons can assist in delineating watersheds or catchment areas by assigning each stream gauge or monitoring point to its nearest catchment.

4. Resource allocation and planning: Thiessen polygons can aid in allocating resources or planning infrastructure by identifying areas of influence or control for specific facilities or services.

To create Thiessen polygons in GIS software, you can typically find a specific tool or function that generates them based on a given set of points. Once generated, the polygons can be analyzed and used for various spatial analyses within the GIS environment.

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 ...