8.1 Introduction
A Geographic Information System (GIS) is a multidisciplinary field and technological framework that merges geography, computer science, and data analysis to capture, store, manage, analyze, and display spatial or geographically referenced data. It acts as a bridge between the physical world and digital analysis by representing real-world features in a computer environment.
This process involves collecting data from diverse sources—such as satellite imagery, GPS measurements, and surveys—then integrating, processing, and analyzing this data for decision-making. The strength of GIS lies in its ability to reveal spatial relationships, patterns, and trends that would be difficult to discern otherwise. Since the 1970s, its applications have expanded into agriculture, urban development, environmental monitoring, transportation planning, disaster management, and climate studies. The technology is underpinned by two main data representation models: vector and raster, each with unique advantages for specific types of analysis.
8.2 Objectives
By completing this unit, you will be able to:
- Identify and describe the five foundational components of GIS.
- Understand the principles and differences between vector and raster data models.
- Conduct and interpret both vector-based and raster-based data analyses.
- Appreciate the historical progression of GIS from early mapping methods to modern digital systems.
- Recognize the wide scope of GIS applications across multiple disciplines.
8.3 Definition of GIS
GIS, or Geographic Information System, is defined by the National Center for Geographic Information and Analysis (NCGIA, 1990) as a combination of hardware, software, and procedures designed to manage, analyze, model, and display geo-referenced data to solve complex resource management and planning problems.
Geographic specifies spatial location, Information refers to descriptive attributes of that location, and System emphasizes integration and interconnectivity. In essence, GIS can be envisioned as a system that organizes the world into thematic layers—such as elevation, land use, vegetation, and infrastructure—that are linked through shared spatial references.
8.4 Components of GIS
A functioning GIS integrates:
- Hardware: Physical devices including computers, servers, high-resolution monitors, GPS units, and scanners.
- Software: Programs that enable spatial analysis, map creation, and database management (e.g., ArcGIS, QGIS, GRASS, MapInfo).
- Data: Spatial (maps, coordinates, satellite images) and non-spatial (attribute tables, survey records) data stored and processed within a DBMS.
- Methods: Standardized workflows and analytical procedures for capturing, managing, and interpreting spatial information.
- People: GIS specialists, analysts, technicians, and developers who design, maintain, and apply the technology.
8.5 History of GIS
While GIS in its current digital form originated in the 1960s—pioneered by Roger Tomlinson—its conceptual roots date back to 19th-century thematic maps used for epidemiological studies, such as John Snow’s cholera map in 1854. The development of GIS can be divided into three phases:
- 1960s–mid-1980s: Experimental stage focusing on theory and basic tools.
- Mid-1980s–early 1990s: Commercial growth, rapid software advances, and sector-specific applications.
- Mid-1990s–present: Integration with GPS and remote sensing, global accessibility, and advanced analytical tools.
8.6 Data Models in GIS
Two principal models define how real-world features are represented digitally:
- Vector Model
- Uses points, lines, and polygons to represent features like wells, roads, and land parcels with precise coordinates.
- Raster Model
- Represents features as a grid of uniformly sized pixels, each holding a value that represents specific attributes (e.g., elevation, vegetation density, soil class).
8.7 Vector Data Analysis
Vector analysis typically follows these steps:
- Data Acquisition: Digitizing paper maps, capturing GPS coordinates in the field, or importing tabulated coordinate datasets.
- Data Queries: Using SQL for attribute-based searches and performing spatial queries like intersection, containment, and proximity analysis.
- Geoprocessing: Transforming vector datasets via operations like union, intersection, clip, dissolve, merge, and buffering.
8.8 Raster-Based Analysis
Raster data analysis is commonly grouped into:
- Single Layer Analysis: Reclassification to categorize continuous data (for example, converting elevation values into low, medium, and high classes).
- Multi-layer Analysis: Combining multiple raster layers using mathematical operations for comparative or change detection studies (e.g., vegetation change across years).
8.9 Applications of GIS
- Land Use/Land Cover Mapping: Monitoring changes in urban expansion, deforestation, or agricultural patterns.
- Disaster Management: Predicting risk zones, planning evacuations, and allocating emergency resources.
- Natural Resource Management: Managing forest health, tracking wildlife habitats, and assessing water resources.
- Irrigation Planning: Evaluating water availability, optimizing irrigation scheduling, and monitoring usage.
8.10 Summary
GIS unites advanced computing, data science, and geography into a cohesive system that informs planning and decision-making. Its dual reliance on vector and raster data models allows for diverse, multi-scale analyses applicable to countless fields.
GIS in Diagnosis and Management of Problem Soils
GIS is invaluable for detecting, mapping, monitoring, and rehabilitating problematic soils such as saline, sodic, acidic, and waterlogged types. By combining spatial datasets with field survey data and laboratory results, it provides a comprehensive diagnostic and management tool.
- Detailed Soil Mapping: Integrates satellite imagery, aerial photos, and field sampling to produce accurate soil classification maps.
- Problem Identification: Uses layers on pH, electrical conductivity, organic matter, and nutrient levels to pinpoint degraded zones.
- Temporal Monitoring: Time-series analyses track changes in soil health, enabling evaluation of past remediation efforts.
- Decision Support: Merges soil condition data with climate, hydrology, and crop requirements to design precise interventions.
- Site-Specific Management: Recommends targeted measures such as gypsum for sodic soils, leaching for saline soils, and drainage for waterlogged areas.
- Remote Sensing Integration: Enhances accuracy and coverage by pairing GIS with real-time remote sensing data.
- Long-Term Planning: Supports sustainable land use strategies that preserve soil productivity and prevent further degradation.
- Stakeholder Collaboration: Provides visual and analytical tools for farmers, researchers, and policymakers to collaborate effectively.
By leveraging GIS, soil management becomes more data-driven, cost-effective, and environmentally responsible, ultimately safeguarding agricultural yields and ecological health.
