Vineesh V, Geography

Posts

Overlay Analysis - Point in Line, Point in Polygon, Line in Polygon, Polygon on Polygon

What is Overlay Analysis ? Overlay means placing one layer on top of another in GIS to see how they are related. Imagine putting a transparent sheet of roads on top of a map of forests — that's overlay! 1. Point in Line Points are single locations (like bus stops). Lines are long features (like roads or rivers). "Point in Line" means checking which points lie on or near a line . ✅ Example: You have a map of bus stops (points) and roads (lines). You check which bus stops are on which roads . 2. Point in Polygon Polygon is an area (like a city, park, or forest). Point in Polygon means checking which points are inside an area . ✅ Example: You have schools (points) and a map of city boundaries (polygons). You want to see which schools are inside which city . 3. Line in Polygon Now you're checking which lines pass through or are inside areas . ✅ Example: You have rivers (lines) and forests (polygons). You w...

Proximity Analysis Buffer Analysis Multi Buffer

What is Proximity Analysis ? Proximity means "how close or far something is." So, Proximity Analysis in GIS means finding out how near or far one thing is from another on a map. What is Buffer Analysis ? Buffer means a zone (area) around something . In GIS: If you draw a circle around a school with 1 km radius , that area is called a buffer . It shows which houses or roads are within 1 km of the school. ✅ Example : You want to find all houses within 500 meters of a river . GIS will create a buffer zone (a shaded area) around the river, and then highlight all houses inside it. What is Multi Buffer Concept ? Instead of just one buffer zone , you can make many buffer zones around a place. ✅ Example : Let's say you make 3 buffer zones around a hospital: 0–1 km (red) 1–2 km (orange) 2–3 km (yellow) Now, you can: See which areas are very close (red), Which are a bit far (orange), And which are farther away (...

Mapping Tabular Data in GIS Environment

What is Tabular Data? Tabular data is data that looks like a table — rows and columns, just like in Excel. Each row is a different place (like a city or a school), and each column has information about that place (like population, name, rainfall, etc.). What is Vector Data? Vector data is the way we draw things on a map using points (like a bus stop), lines (like a road), or polygons (like a park or city area). Now, how do we map tabular data in GIS? Imagine you have a table like this: City Name Population Literacy Rate Palakkad 1,30,000 92% Kochi 6,00,000 95% And in GIS, you have a map of Kerala showing where Palakkad and Kochi are as points (vector data). In GIS, you "join" the table with the map. GIS connects the rows in the table to the points on the map using a common link , like the city name. Once they are linked: You can see the data on the map . You can color the cities based on population or literacy. You can make graphs or charts ...

Vector Spatial Relationship and Spatial Querry

Spatial relationships show how things are located in relation to each other on a map. Here are some examples: Relationship Type Example Near A school is near a hospital Inside A tree is inside a park Touching Two countries are touching at their borders Overlapping A flood zone overlaps with farmland Connected One road is connected to another road Spatial Query? A spatial query is a question that asks about the location or relationship between features on a map. It's different from a regular query (which asks about data in a table ). A spatial query asks about where things are and how they relate to each other. Examples of Spatial Queries: "Which schools are within 1 km of the main road?" "Find all rivers that cross the highway." "Show all houses inside the flood zone." "Which villages are near a hospital?" GIS uses this information to highlight or select features based on their spatial relat...

Vector Attribute Data Management and Querry

In GIS, vector data is a way of representing the world using points, lines, and polygons . A point shows a single location (like a school). A line shows things like roads or rivers. A polygon shows areas like parks, lakes, or countries. Each of these features has extra information called "attributes." Attribute Data? Think of attribute data like a table full of details about each feature on a map. For example, if you have a map of schools (points) : ID Name Type Students 1 City High Government 800 2 St. Mary's Private 650 3 Sunrise Acad Government 950 Each row is one school (point), and each column is an attribute (name, type, number of students, etc.). Query? A query means asking questions using the attribute data. In GIS, queries help us find only the information we need from the map. For example: "Show me all Government schools." "Find schools with more than 800 students ." "Which rivers ...

Zonal, neighbourhood and local operations in GIS

1. Local Operations in GIS 👀 Looks at one pixel at a time — like checking only one house on a map. We do math or comparisons using just that pixel's value. ✅ Simple Examples: Add two maps together: temperature map + rainfall map Reclassify: Change values — e.g., if temperature > 30°C, mark as HOT 📌 Think of it like doing math problem by problem, not looking at neighbors. Neighbourhood Operations in GIS 🏡 Looks at a pixel and all its surrounding neighbours — like checking your house and nearby houses. We use this to smooth , sharpen , or highlight details in the map. ✅ Simple Examples: Low Pass Filter : Makes the map look smooth (like blur in photos) High Pass Filter : Makes sharp edges stand out (like outlines in drawings) Edge Enhancement : Highlights boundaries between areas (like drawing a border between forest and farmland) 📌 Think of it like checking what your neighbours are doing before painting your own house! Zonal O...

Zonal Operations Cost Distance Analysis, Least Cost Path

Zonal Operations in GIS In GIS, the land is divided into different zones or areas like forests, roads, water bodies, hills, etc. Zonal operations help us analyze and compare things inside these areas , such as: How far is something? What is the easiest way to go somewhere? How much effort does it take to move across different land types? Now, let's focus on two important types of Zonal Operations: Cost Distance Analysis in GIS 🧭 What is it? It tells us how much "cost" (effort, time, or money) it takes to move from a starting point to other places in different zones. 🧒 Imagine this: You are standing at your home and want to visit many places in your town. Walking on a smooth road is easy (low cost). Walking through a forest or hill is hard (high cost). GIS shows a map of how hard it is to reach every location from your starting point. ✅ What it helps with: Finding how difficult it is to travel through different areas...

Neighbourhood Operations

Neighbourhood Operations in GIS? In GIS and raster data , neighbourhood operations look at a group of nearby pixels (not just one) to understand or change a pixel's value. Think of it like checking what's around a house before deciding what color to paint it! Why "Neighbourhood"? Each pixel has " neighbours " (just like how your house has nearby houses). Neighbourhood operations check these nearby pixels and do some calculation to get a new value. 1. Aggregations (Summarizing Nearby Values) Aggregation means combining values of several pixels into one. We do this to: Find the average of surrounding pixels Find the minimum or maximum value Smooth the map (make it less rough) 🧒🏻 Example: Imagine checking the test scores of 9 students sitting around you and finding the average score . That's aggregation! 2. Filtering Techniques Filtering is used to improve or highlight features in a raster image, just like f...

Local Operations in GIS

Local operations mean doing math or logic on each pixel one by one , using just the value in that pixel (not its neighbors). Map Algebra Just like we do math in school, we can do math on maps using raster data. For example: Add two maps together (e.g., rainfall map + temperature map). Multiply or subtract values in the pixels. Reclassification Reclassification means changing the values in the raster based on some rules. For example: Pixels with values 1–10 → change to 1 (low) Pixels with values 11–20 → change to 2 (medium) Pixels with values 21–30 → change to 3 (high) It helps us group data into categories (like low, medium, high). Logical Operations This is like asking Yes or No questions for each pixel. Examples: "Is the value greater than 50?" → If yes , mark it as 1 → If no , mark it as 0 This helps in finding areas that match certain conditions . Arithmetic Operations These are basic math operations done o...