Book Image

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python - Fourth Edition

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.
Table of Contents (18 chapters)
1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 3:Practical Geospatial Processing Techniques

Examining raster data properties

As a geospatial analyst, understanding the metadata and properties of these raster images is crucial as they provide insights into the image’s spatial reference, resolution, number of bands, data type, and other essential attributes. This information is vital for ensuring that the raster data is compatible with other datasets, aligns correctly within a spatial analysis, and is suitable for the intended analytical methods.

The following script serves as a practical tool for examining the metadata and properties of a raster image using the GDAL library in Python. By running this code, you can quickly assess the characteristics of a raster file, such as its projection, size, band properties, and more. This information is not only valuable for initial data exploration but also plays a critical role in preprocessing and quality control. Whether you’re integrating raster data with other spatial datasets, preparing it for analysis, or troubleshooting...