Chapter 16

Geographical Information Systems in Agriculture

GIS Data Management

Database management in GIS refers to the organized handling, storage, and retrieval of spatial data. It plays a crucial role in ensuring geographic data—such as maps, coordinates, and attributes—is structured for effective analysis, visualization, and decision-making. This process involves managing large datasets, applying data models, maintaining data integrity, and optimizing performance for querying geographic features. In GIS, databases store various types of spatial data (e.g., field location, roads) and link them with non-spatial attributes (e.g., names, statistics). Database management is essential for GIS as it ensures that vast amounts of spatial and non-spatial data are accurately stored, organized, and easily accessible.

Data Types

GIS databases store various data types, categorized broadly into spatial data and attribute data. Spatial data describes the geographic location and shape of features, while attribute data provides non-spatial information about those features.

Metadata

Information about the spatial dataset itself, including details about its scale, accuracy, projection/datum, data source, and manipulations. In GIS, metadata files contain items such as general descriptions about the contents of the file, definitions for the various terms used to identify records (rows) and fields, the range of values for fields, the quality or reliability of the data, and measurements, how the data were collected when the data were collected, and who collected the data.

Relational Versus Geospatial Databases

Relational and geospatial databases are both database management systems, but they differ in their primary focus. Relational databases store structured data in tables, while geospatial databases are specialized to efficiently manage location-based data (points, lines, polygons) alongside non-spatial information.

Relational Database

Relational databases store data in tables, with rows (records) and columns (fields), and can establish relationships between tables using primary and foreign keys, making them ideal for managing structured data, such as crop types, soil properties, and economic data associated with farms. Data is typically structured across multiple tables, which can be joined together via a primary key or a foreign key.

Geospatial Database

Geospatial (or spatial) databases include the functionality of a database management system (DBMS) but are also designed for storing and querying data that represents vector data (points, lines, polygons), raster data (imagery, gridded data), and their associated attributes. Integrating this geographic information with the tabular attribute data of a classical DBMS provides users with powerful tools to visualize and answer the spatially explicit questions that arise in an increasingly technological society. They are optimized for managing spatial data and performing queries based on location or proximity.

Why Use Relational Databases?

While spatial databases are optimized for handling geographic data within GIS, relational databases are still frequently used due to their versatility, robust features, and established ecosystem. Relational databases offer advantages in managing large volumes of attribute data, ensuring data integrity, and facilitating integration with various applications and tools.

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