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  • Book Overview & Buying Learning Geospatial Analysis with Python
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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

5 (7)
By: Joel Lawhead
Buy this Book

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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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Conventions used
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Get in touch
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Share your thoughts
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Download a free PDF copy of this book
1
Part 1:The History and the Present of the Industry
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Part 1:The History and the Present of the Industry
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2
Chapter 1: Learning about Geospatial Analysis with Python
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Chapter 1: Learning about Geospatial Analysis with Python
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Technical requirements
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Geospatial analysis and our world
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History of geospatial analysis
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Evolution of Geographic Information Systems (GISs)
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Remote sensing
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Point cloud data
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Computer-aided drafting
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Geospatial analysis and computer programming
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The importance of geospatial analysis
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GIS concepts
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Remote sensing concepts
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Common vector GIS concepts
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Common raster data concepts
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Creating the simplest possible Python GIS
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Summary
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Questions
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Further reading
3
Chapter 2: Learning about Geospatial Data
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Chapter 2: Learning about Geospatial Data
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Technical requirements
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Overview of common data formats
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Understanding data structures
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Understanding spatial indexing
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What are overviews?
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What is metadata?
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Understanding the file structure
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Knowing about the most widely used vector data types
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Understanding raster data types
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What is point cloud data?
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More realistic geospatial models with 3D data
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What are web services?
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Understanding geospatial databases
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Sharing data with interchange formats
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Introducing spatiotemporal data
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Summary
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Questions
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Further reading
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Chapter 3: The Geospatial Technology Landscape
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Chapter 3: The Geospatial Technology Landscape
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Technical requirements
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Understanding data access
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Understanding computational geometry
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Understanding desktop tools (including visualization)
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Understanding metadata management
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A quick look at artificial intelligence
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Summary
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Questions
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Further reading
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Part 2:Geospatial Analysis Concepts
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Part 2:Geospatial Analysis Concepts
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Chapter 4: Geospatial Python Toolbox
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Chapter 4: Geospatial Python Toolbox
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Technical requirements
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Using QGIS
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Installing third-party Python modules
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Jupyter
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Python networking libraries for acquiring data
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Bundling and compressing files
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Python markup and tag-based parsers
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Well-Known Text (WKT)
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Python JSON libraries
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OGR
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PyShp
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Shapely
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Fiona
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GDAL
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NumPy
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PIL
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PNGCanvas
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GeoPandas
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PyFPDF
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PyMySQL
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Rasterio
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OSMnx
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Folium
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Summary
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Questions
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Further reading
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Chapter 5: Python and Geospatial Algorithms
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Chapter 5: Python and Geospatial Algorithms
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Technical requirements
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Measuring distance
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Calculating line direction
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Understanding coordinate conversion
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Understanding reprojection
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Understanding coordinate format conversion
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Calculating the area of a polygon
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Using ChatGPT to measure a polygon perimeter
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Summary
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Questions
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Further reading
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Chapter 6: Creating and Editing GIS Data
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Chapter 6: Creating and Editing GIS Data
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Technical requirements
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Editing shapefiles
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Creating images for visualization
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Using GPS data
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Turning addresses into points with geocoding
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Performing GIS analysis faster with multiprocessing
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Summary
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Questions
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Further reading
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Chapter 7: Python and Remote Sensing
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Chapter 7: Python and Remote Sensing
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Technical requirements
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Examining raster data properties
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Swapping image bands
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Creating image histograms
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Clipping images
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Classifying images
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Extracting features from images
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Understanding change detection
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Extracting image footprints using ChatGPT
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Summary
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Questions
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Further reading
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Chapter 8: Python and Elevation Data
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Chapter 8: Python and Elevation Data
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Technical requirements
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Accessing ASCII Grid files
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Creating a shaded relief
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Creating elevation contours
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Working with LiDAR data
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Working with bathymetry
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Summary
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Questions
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Further reading
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Part 3:Practical Geospatial Processing Techniques
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Part 3:Practical Geospatial Processing Techniques
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Chapter 9: Advanced Geospatial Modeling
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Chapter 9: Advanced Geospatial Modeling
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Technical requirements
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Creating a normalized difference vegetation index (NDVI)
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Creating a flood inundation model
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Creating a color hillshade
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Performing least cost path analysis
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Converting the route to a shapefile
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Routing along streets
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Geolocating photos
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Calculating satellite image cloud cover
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Summary
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Questions
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Further reading
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Chapter 10: Working with Real-Time Data
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Chapter 10: Working with Real-Time Data
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Technical requirements
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Limitations of real-time data
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Using real-time data
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Tracking vehicles
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Gathering reports from the field
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Summary
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Questions
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Further reading
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Chapter 11: Putting It All Together
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Chapter 11: Putting It All Together
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Technical requirements
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Understanding a typical GPS report
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Building a GPS reporting tool
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Summary
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Questions
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Further reading
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Assessments
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Chapter 1 – Learning about Geospatial Analysis with Python
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Chapter 2 – Learning about Geospatial Data
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Chapter 3 – The Geospatial Technology Landscape
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Chapter 4 – Geospatial Python Toolbox
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Chapter 5 – Python and Geospatial Algorithms
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Chapter 6 – Creating and Editing GIS Data
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Chapter 7 – Python and Remote Sensing
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Chapter 8 – Python and Elevation Data
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Chapter 9 – Advanced Geospatial Modeling
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Chapter 10 – Working with Real-Time Data
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Chapter 11 – Putting It All Together
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Index
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Index
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Why subscribe?
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Other Books You May Enjoy
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Other Books You May Enjoy
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Packt is searching for authors like you
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Understanding spatial indexing

Geospatial datasets are often very large files, easily reaching hundreds of megabytes or even several gigabytes in size. Geospatial software can be quite slow in trying to repeatedly access large files when performing analysis.

As discussed briefly in Chapter 1, Learning about Geospatial Analysis with Python, spatial indexing creates a guide, which allows the software to quickly locate query results without examining every single feature in the dataset. Spatial indexes allow the software to eliminate possibilities and perform more detailed searches or comparisons on a much smaller subset of the data.

Spatial indexing algorithms

Many spatial indexing algorithms are derivatives of well-established algorithms that have been used on non-spatial information for decades. The two most common spatial indexing algorithms are quadtree index and R-tree index. There is a three-dimensional counterpart to the Quadtree index called an octree data structure...

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