Book Image

Learning Geospatial Analysis with Python - Third Edition

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python - Third Edition

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. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
Table of Contents (15 chapters)
Free Chapter
Section 1: The History and the Present of the Industry
Section 2: Geospatial Analysis Concepts
Section 3: Practical Geospatial Processing Techniques

Getting an overview of common data formats

As a geospatial analyst, you may frequently encounter the following general data types:

  • Spreadsheets and comma-separated values (CSV files) or tab-separated values (TSV files)
  • Geotagged photos
  • Lightweight binary points, lines, and polygons
  • Multi-gigabyte satellite or aerial images
  • Elevation data such as grids, point clouds, or integer-based images
  • XML files
  • JSON files
  • Databases (both servers and file databases)
  • Web services
  • Geodatabases

Each format contains its own challenges for access and processing. When you perform analysis on data, you usually have to do some form of preprocessing first. You might clip or subset a satellite image of a large area down to just your area of interest, or you might reduce the number of points in a collection to just the ones meeting certain criteria in your data model. A good example of this type of...