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

What is metadata?

As discussed in Chapter 1, Learning about Geospatial Analysis with Python, metadata is any data that describes the associated dataset. Common examples of metadata include basic elements such as the footprint of the dataset on Earth, as well as more detailed information such as spatial projection and information describing how the dataset was created.

Most data formats contain the footprint or bounding box of the data on Earth. Detailed metadata is typically stored in a separate location in a standard format, such as the US Federal Geographic Data Committee (FGDC), Content Standard for Digital Geospatial Metadata (CSDGM), ISO, or the newer European Union initiative, which includes metadata requirements, and is called the Infrastructure for Spatial Information in the European Community (INSPIRE).