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 Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
5 (7)
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 Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

5 (7)
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)
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1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 3:Practical Geospatial Processing Techniques

Summary

Elevation data can often provide a complete dataset for analysis and derivative products without any other data. In this chapter, you learned how to read and write ASCII Grids using only NumPy. You also learned how to create shaded reliefs, slope grids, and aspect grids. We created elevation contours using a little-known feature called contour in the GDAL library, which is available for Python.

Next, we transformed LiDAR data into an easy-to-manipulate ASCII Grid. We experimented with different ways to visualize the LiDAR data with PIL. Finally, we created a 3D surface or TIN by turning a LiDAR point cloud into a 3D shapefile of polygons. Then we colorized LiDAR using aerial images to create an almost photo-realistic 3D model. We also classified LiDAR so it can be an input to more sophisticated analysis models. And we saw that ocean seafloor data can be processed in much the same way as terrestrial data. These are the tools of terrain analysis that are used for transportation...

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Programming languages
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