Representing Geographic Space; Discrete and Continuous

Geographic data can be broadly classified into two categories - discrete and continuous. Discrete geographic data is information that is specific to particular locations, and can be represented by different types of values like nominal, ordinal, interval, and ratio. This type of data includes features like landownership, soils classification, zoning, and land use.

When represented on a map, discrete data typically has well-defined boundaries and is typically represented in polygon format. For example, the shape of a property boundary or a zoning district can be considered as discrete geographic data. In addition, point and line datasets such as tree locations, rivers, and streets are also considered as discrete data.

On the other hand, continuous data refers to information that does not have a clearly defined boundary and can be measured at every point on a map. Continuous data is often represented as surfaces and includes datasets like elevation, rainfall, pollution concentration, and water tables. When represented on a map, continuous data can be visualized as a continuous gradient or a smooth transition of values from one point to another.

In summary, discrete geographic data pertains to information that is specific to certain locations and has well-defined boundaries, whereas continuous geographic data pertains to information that is present throughout an area and has no clear boundaries. Understanding the difference between these two types of geographic data is essential for effective analysis and visualization of spatial data.

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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...

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...

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 ...

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