MEV 024: Unit 08 – Introduction to GIS

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 UNIT 8: INTRODUCTION TO GIS

8.1 Introduction

Geographic Information Systems (GIS) have revolutionized the way spatial data is collected, analyzed, visualized, and interpreted. A GIS integrates hardware, software, data, and human resources to collect, manage, analyze, and display geographically referenced information. It is used widely in urban planning, environmental management, disaster risk reduction, agriculture, transportation, and various research disciplines.

This unit introduces the foundational concepts of GIS, its components, data models, and analytical capabilities. The evolution of GIS, from basic cartographic tools to modern spatial data infrastructures, is also explored.

8.2 Objectives

After studying this unit, you will be able to:

  • Define GIS and understand its purpose.
  • Identify the main components and history of GIS.
  • Differentiate between raster and vector data models.
  • Understand data analysis techniques in GIS.
  • Recognize the wide-ranging applications of GIS across fields.

8.3 Definition of GIS

A Geographic Information System (GIS) is a computer-based tool that allows users to capture, store, manipulate, analyze, manage, and present spatial or geographic data. It is concerned with mapping and analyzing things that exist and events that happen on Earth.

Key characteristics:

  • Spatial referencing (latitude and longitude, coordinates)
  • Integration of spatial (maps) and non-spatial (attribute) data
  • Ability to layer, query, and analyze data for decision-making

8.4 Components of GIS

GIS is composed of five key components:

  1. Hardware: Computers, GPS devices, scanners, and servers.
  2. Software: Programs like ArcGIS, QGIS, GRASS GIS that support spatial analysis.
  3. Data: Spatial (location-based) and attribute (descriptive) data.
  4. People: GIS professionals, users, and decision-makers who use the system.
  5. Methods: Models, processes, and procedures for data handling and analysis.

8.5 History of GIS

  • 1960s: The first true GIS was developed in Canada as the Canada Geographic Information System (CGIS).
  • 1970s–80s: GIS evolved with better computers and software.
  • 1990s: The emergence of desktop GIS and widespread adoption.
  • 2000s onwards: Integration with GPS, remote sensing, and web-based GIS.

GIS has now become a mainstream tool used in both government and private sectors for spatial decision-making.

8.6 Data Models in GIS

GIS uses two primary data models to represent geographic features: vector and raster.

8.6.1 Vector Data Model

  • Represents features as discrete geometries:
    • Points: e.g., wells, cities
    • Lines: e.g., rivers, roads
    • Polygons: e.g., land parcels, lakes
  • Each feature has an associated attribute table.
  • Ideal for representing linear and boundary-based data.

8.6.2 Raster Data Model

  • Represents data as a grid of cells (pixels), each with a value.
  • Used for continuous data like:
    • Elevation
    • Temperature
    • Land cover
  • Cell size determines the resolution (smaller = higher resolution).

8.7 Vector Data Analysis

8.7.1 Data Acquisition

Vector data can be acquired through:

  • Digitization from maps
  • GPS field data collection
  • Downloading from spatial data repositories

8.7.2 Data Query

Querying helps retrieve specific information using:

  • Attribute queries (e.g., all roads with speed > 60 km/h)
  • Spatial queries (e.g., find schools within 1 km of a hospital)

8.7.3 Geoprocessing of Vector Data

Geoprocessing involves operations like:

  • Buffering: Creating zones around features
  • Overlay analysis: Combining layers to find intersections or unions
  • Clipping: Cutting a layer using boundaries of another

These tools help in answering spatial questions and making decisions.

8.8 Raster-Based Analysis

Raster data analysis focuses on continuous surface phenomena.

8.8.1 Single Layer Analysis

Includes:

  • Reclassification: Assigning new values based on categories (e.g., land cover types)
  • Zonal statistics: Summarizing values within a zone (e.g., mean elevation in a district)

8.8.2 Multi-layer Operation

Includes:

  • Map algebra: Mathematical operations between layers (e.g., slope + rainfall = erosion risk)
  • Overlay analysis: Combining multiple raster layers for modeling (e.g., suitability analysis)

Raster analysis is commonly used in environmental modeling and terrain analysis.

8.9 Applications of GIS

GIS applications span across multiple disciplines, such as:

  • Urban Planning: Zoning, infrastructure mapping, land use management.
  • Environmental Monitoring: Deforestation tracking, wildlife habitat mapping, pollution analysis.
  • Disaster Management: Hazard mapping, risk zones identification, evacuation planning.
  • Agriculture: Precision farming, soil and crop mapping, irrigation planning.
  • Health: Disease mapping, healthcare accessibility analysis.
  • Transport: Route optimization, traffic flow analysis, logistics management.
  • Public Utilities: Managing water, electricity, and telecommunication networks.

8.10 Let Us Sum Up

Geographic Information Systems (GIS) are powerful tools that integrate spatial and attribute data for visualization, analysis, and decision-making. With roots in digital cartography, GIS has evolved into a robust technology supported by hardware, software, and analytical techniques. The two main data models—vector and raster—support diverse analysis types suited to various applications. Whether in city planning, agriculture, disaster risk reduction, or health services, GIS plays an essential role in understanding and solving spatial problems.

8.11 Key Words

  • GIS: A system for managing and analyzing spatial data.
  • Vector Data: Point, line, or polygon representations of discrete features.
  • Raster Data: Grid-based representation of continuous data.
  • Spatial Analysis: Techniques to derive meaningful insights from spatial relationships.
  • Buffering: Creating zones around spatial features.
  • Overlay: Combining layers to find common areas or intersections.
  • Zonal Statistics: Statistical analysis of raster data within defined areas.

 

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