Vector Data Analysis

GIS vector data analysis involves processing and interpreting geographic features represented as points, lines, and polygons to identify spatial relationships, patterns, and trends. This analysis supports decision-making in urban planning, environmental management, transportation networks, and other spatial applications.

Vector Data Types

Vector data represents discrete spatial features and is ideal for precise location analysis. The three main types are:

Point Data
- Represents individual locations.
- Example: Store locations, crime incidents, weather stations.
Line Data
- Represents linear features with length but no width.
- Example: Roads, rivers, power lines.
Polygon Data
- Represents enclosed areas with boundaries.
- Example: Administrative zones, lakes, land use areas.

Vector Data Attributes

Each vector feature has an associated attribute table, containing descriptive information such as:

Population Density (for city polygons)
Road Type (for road lines)
Land Use Classification (for land use polygons)

Vector Analysis Techniques

1. Overlay Analysis

Combines multiple layers to identify relationships where features overlap.
Example: Identifying flood-prone areas by overlaying flood zones and population data.

2. Proximity Analysis

Identifies features within a certain distance of another feature.
Example: Finding schools within 500 meters of a highway.

3. Buffer Analysis

Creates a zone of influence around a feature.
Example: Creating a 1 km buffer around a river to identify protected zones.

4. Network Analysis

Examines how features are connected within a network.
Example: Finding the shortest route between two locations on a road network.

5. Selection by Location

Selects features based on their spatial relationship with other features.
Example: Selecting all land parcels that intersect with a proposed construction site.

6. Topological Analysis

Examines connectivity, adjacency, and containment of spatial features.
Example: Ensuring roads properly connect at intersections in a transportation model.

7. Spatial Join

Transfers attribute data between spatially related features.
Example: Assigning population data to neighborhoods based on census tract polygons.

Vector Analysis Based on Feature Type

1. Point Analysis

Objective: Examines individual locations.
Example: Identifying crime hotspots by analyzing crime incident locations.

2. Line Analysis

Objective: Examines networks and linear features.
Example: Finding the most connected road segments in a city.

3. Polygon Analysis

Objective: Examines area-based relationships.
Example: Calculating total forest area within a national park.

Difference

Aspect	Raster Data Analysis	Vector Data Analysis
Data Structure	Grid-based, composed of pixels (cells) with values.	Feature-based, composed of points, lines, and polygons.
Data Representation	Represents continuous data like elevation, temperature, or land cover.	Represents discrete features like roads, buildings, and administrative boundaries.
Spatial Precision	Less precise due to cell-based structure.	Highly precise as features have defined boundaries.
Data Size	Large file sizes due to high-resolution grids.	Smaller file sizes as it stores coordinates and attributes.
Common Uses	Land cover classification, terrain analysis, remote sensing, and environmental monitoring.	Network analysis, cadastral mapping, site selection, and urban planning.
Examples	Elevation models, satellite imagery, temperature maps.	Road networks, property boundaries, utility lines.
Analysis Techniques	Local, neighborhood, zonal, and global operations.	Overlay, proximity, buffer, network, and spatial joins.
Processing Speed	Computationally intensive, especially at high resolution.	Faster processing, especially for small datasets.
Attribute Storage	Stores limited attributes (usually one per cell).	Stores multiple attributes in a database format.
Suitability	Best for continuous and large-scale analysis.	Best for discrete, object-based spatial analysis.

Raster analysis is ideal for modeling continuous phenomena (e.g., elevation, land cover).
Vector analysis is best for analyzing discrete features and relationships (e.g., road networks, property boundaries).

Comments

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO) Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...

Types of Remote Sensing

Remote Sensing means collecting information about the Earth's surface without touching it , usually using satellites, aircraft, or drones . There are different types of remote sensing based on the energy source and the wavelength region used. 🛰️ 1. Active Remote Sensing 📘 Concept: In active remote sensing , the sensor sends out its own energy (like a signal or pulse) to the Earth's surface. The sensor then records the reflected or backscattered energy that comes back from the surface. ⚙️ Key Terminology: Transmitter: sends energy (like a radar pulse or laser beam). Receiver: detects the energy that bounces back. Backscatter: energy that is reflected back to the sensor. 📊 Examples of Active Sensors: RADAR (Radio Detection and Ranging): Uses microwave signals to detect surface roughness, soil moisture, or ocean waves. LiDAR (Light Detection and Ranging): Uses laser light (near-infrared) to measure elevation, vegetation...

Natural Disasters

A natural disaster is a catastrophic event caused by natural processes of the Earth that results in significant loss of life, property, and environmental resources. It occurs when a hazard (potentially damaging physical event) interacts with a vulnerable population and leads to disruption of normal life . Key terms: Hazard → A potential natural event (e.g., cyclone, earthquake). Disaster → When the hazard causes widespread damage due to vulnerability. Risk → Probability of harmful consequences from interaction of hazard and vulnerability. Vulnerability → Degree to which a community or system is exposed and unable to cope with the hazard. Resilience → Ability of a system or society to recover from the disaster impact. 👉 Example: An earthquake in an uninhabited desert is a hazard , but not a disaster unless people or infrastructure are affected. Types Natural disasters can be classified into geophysical, hydrological, meteorological, clim...

India remote sensing

1. Foundational Phase (Early 1970s – Early 1980s) Objective: To explore the potential of space-based observation for national development. 1972: The Space Applications Programme (SAP) was initiated by the Indian Space Research Organisation (ISRO), focusing on applying space technology for societal benefits. 1975: The Department of Space (DoS) was established, providing an institutional base for space applications, including remote sensing. 1977: India began aerial and balloon-borne experiments to study Earth resources and assess how remote sensing data could aid in agriculture, forestry, and hydrology. 1978 (June 7): Bhaskara-I launched by the Soviet Union — India's first experimental Earth Observation satellite . Payloads: TV cameras (for land and ocean surface observation) and a Microwave Radiometer. Significance: Proved that satellite-based Earth observation was feasible for India's needs. 1981 (November 20): Bhaskara-II launche...

Linear Arrays Along-Track Scanners or Pushbroom Scanners

Multispectral Imaging Using Linear Arrays (Along-Track Scanners or Pushbroom Scanners) Multispectral Imaging: As previously defined, this involves capturing images using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. Linear Array of Detectors (A): This refers to a row of discrete detectors arranged in a straight line. Each detector is responsible for measuring the radiation within a specific wavelength band. Focal Plane (B): This is the plane where the image is formed by the lens system. It is the location where the detectors are placed to capture the focused image. Formed by Lens Systems (C): The lens system is responsible for collecting and focusing the incoming radiation onto the focal plane. It acts like a camera lens, creating a sharp image of the scene. Ground Resolution Cell (D): As previously defined, this is the smallest area on the ground that can be resolved by a remote sensing sensor. In the case of linear array scanne...

Vineesh V, Geography

Search This Blog