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GIS Concepts

Spatial Data Components

  1. Location or Position

    • This defines where a spatial object exists on the Earth's surface.
    • It is represented using coordinate systems, such as:
      • Geographic Coordinate System (GCS) – Uses latitude and longitude (e.g., WGS84).
      • Projected Coordinate System (PCS) – Converts Earth's curved surface into a flat map using projections (e.g., UTM, Mercator).
    • Example: The Eiffel Tower is located at 48.8584° N, 2.2945° E in the WGS84 coordinate system.

  2. Attribute Data (Descriptive Information About Location)

    • Describes characteristics of spatial features and is stored in attribute tables.
    • Types of attribute data:
      • Nominal Data – Categories without a numerical value (e.g., land use type: residential, commercial).
      • Ordinal Data – Ranked categories (e.g., soil quality: poor, moderate, good).
      • Interval Data – Numeric values without a true zero (e.g., temperature in °C).
      • Ratio Data – Numeric values with a true zero (e.g., population count, rainfall amount).
    • Example: A river feature may have attributes like:
      River NameLength (km)Flow Rate (m³/s)Water Quality
      Ganges252516000Moderate

  3. Time (Temporal Component)

    • Captures how spatial features change over time, crucial in monitoring and trend analysis.
    • Types of temporal data:
      • Static Data – Data recorded at a single point in time (e.g., a 2020 census map).
      • Dynamic Data – Data that updates over time (e.g., satellite images showing land cover change).
    • Example: Tracking deforestation from 2000 to 2020 using Landsat satellite imagery.

  4. Spatial Relation (Topology)

    • Defines how spatial objects relate to each other in space.
    • Key topological relationships:
      • Adjacency – Whether two features share a boundary (e.g., two neighboring districts).
      • Intersection – Whether two features overlap (e.g., a river crossing a road).
      • Containment – Whether one feature is fully inside another (e.g., a lake within a park).
      • Connectivity – Whether features are linked (e.g., a railway network).
    • Example:
      • A road network where roads are connected at intersections.
      • A forest boundary that contains multiple lakes within it.

Basic Spatial Entities

Spatial features are represented using three primary geometric types:

  1. Point (0-Dimensional)

    • Represents a single location in space with no length, width, or area.
    • Example:
      • A weather station (lat: 12.9716° N, lon: 77.5946° E).
      • ATM locations in a city.

  2. Line (1-Dimensional)

    • Represents linear features with length but no width.
    • Example:
      • Roads, rivers, pipelines on a map.
      • A railway track connecting two cities.

  3. Area (Polygon) (2-Dimensional)

    • Represents features with an enclosed boundary and area.
    • Example:
      • Forest areas, land parcels, administrative boundaries.
      • A lake represented as a polygon instead of a point.

Dimensions of Spatial Data

  1. Spatial Dimension (Geographic Space)

    • Defines the actual location of objects in a coordinate system.
    • Example:
      • A city's location on a world map.
      • A satellite image's pixel coordinates in a raster grid.

  2. Thematic Dimension (Attribute Information)

    • Stores descriptive information related to a spatial feature.
    • Example:
      • A land cover map showing forest, agriculture, and urban areas.
      • A population density map with data about different regions.

  3. Temporal Dimension (Time-Based Changes)

    • Helps in studying changes over time.
    • Example:
      • A flood risk map showing changes in flood-prone areas over the last 20 years.
      • A land-use change model predicting urban expansion from 2000 to 2050.

Spatial Perspectives

  1. Location

    • Identifies the exact position of an object on Earth's surface.
    • Example:
      • The location of Mumbai is 19.0760° N, 72.8777° E.

  2. Direction

    • Refers to the relative position of one object in relation to another.
    • Example:
      • "New York is northwest of Washington, D.C."
      • "The Himalayas are north of India."

  3. Distance

    • Measures the spatial separation between two objects.
    • Types of distance measurement:
      • Euclidean Distance (straight-line distance)
      • Manhattan Distance (distance along a grid-like path)
    • Example:
      • The distance between Delhi and Chennai is about 2,200 km.

  4. Region

    • Groups areas based on common characteristics (e.g., cultural, economic, or environmental factors).
    • Types of regions:
      • Formal Regions – Defined by official boundaries (e.g., states, countries).
      • Functional Regions – Defined by a common function (e.g., a metropolitan area).
      • Perceptual Regions – Based on human perception (e.g., "The Silicon Valley").
    • Example:
      • Amazon Rainforest is a biogeographical region with high biodiversity.

  5. Association

    • Examines how different spatial features relate to each other.
    • Example:
      • High rainfall areas are often associated with dense vegetation.
      • Urban areas are associated with higher temperatures due to the heat island effect.

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