Table of Contents for
Python Geospatial Development - Third Edition

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Python Geospatial Development - Third Edition by Erik Westra Published by Packt Publishing, 2016
  1. Cover
  2. Table of Contents
  3. Python Geospatial Development Third Edition
  4. Python Geospatial Development Third Edition
  5. Credits
  6. About the Author
  7. About the Reviewer
  8. www.PacktPub.com
  9. Preface
  10. What you need for this book
  11. Who this book is for
  12. Conventions
  13. Reader feedback
  14. Customer support
  15. 1. Geospatial Development Using Python
  16. Geospatial development
  17. Applications of geospatial development
  18. Recent developments
  19. Summary
  20. 2. GIS
  21. GIS data formats
  22. Working with GIS data manually
  23. Summary
  24. 3. Python Libraries for Geospatial Development
  25. Dealing with projections
  26. Analyzing and manipulating Geospatial data
  27. Visualizing geospatial data
  28. Summary
  29. 4. Sources of Geospatial Data
  30. Sources of geospatial data in raster format
  31. Sources of other types of geospatial data
  32. Choosing your geospatial data source
  33. Summary
  34. 5. Working with Geospatial Data in Python
  35. Working with geospatial data
  36. Changing datums and projections
  37. Performing geospatial calculations
  38. Converting and standardizing units of geometry and distance
  39. Exercises
  40. Summary
  41. 6. Spatial Databases
  42. Spatial indexes
  43. Introducing PostGIS
  44. Setting up a database
  45. Using PostGIS
  46. Recommended best practices
  47. Summary
  48. 7. Using Python and Mapnik to Generate Maps
  49. Creating an example map
  50. Mapnik concepts
  51. Summary
  52. 8. Working with Spatial Data
  53. Designing and building the database
  54. Downloading and importing the data
  55. Implementing the DISTAL application
  56. Using DISTAL
  57. Summary
  58. 9. Improving the DISTAL Application
  59. Dealing with the scale problem
  60. Performance
  61. Summary
  62. 10. Tools for Web-based Geospatial Development
  63. A closer look at three specific tools and techniques
  64. Summary
  65. 11. Putting It All Together – a Complete Mapping System
  66. Designing the ShapeEditor
  67. Prerequisites
  68. Setting up the database
  69. Setting up the ShapeEditor project
  70. Defining the ShapeEditor's applications
  71. Creating the shared application
  72. Defining the data models
  73. Playing with the admin system
  74. Summary
  75. 12. ShapeEditor – Importing and Exporting Shapefiles
  76. Importing shapefiles
  77. Exporting shapefiles
  78. Summary
  79. 13. ShapeEditor – Selecting and Editing Features
  80. Editing features
  81. Adding features
  82. Deleting features
  83. Deleting shapefiles
  84. Using the ShapeEditor
  85. Further improvements and enhancements
  86. Summary
  87. Index

Chapter 1. Geospatial Development Using Python

This chapter provides an overview of the Python programming language and geospatial development. Please note that this is not a tutorial on how to use the Python language; Python is easy to learn, but the details are beyond the scope of this book.

In this chapter, we will see:

  • What the Python programming language is and how it differs from other languages
  • How the Python Standard Library and the Python Package Index make Python even more powerful
  • What the terms geospatial data and geospatial development refer to
  • An overview of the process of accessing, manipulating, and displaying geospatial data. How geospatial data can be accessed, manipulated, and displayed.
  • Some of the major applications of geospatial development
  • Some of the recent trends in the field of geospatial development

Python

Python (http://python.org) is a modern, high-level language suitable for a wide variety of programming tasks. It is often used as a scripting language, automating and simplifying tasks at the operating system level, but it is equally suitable for building large and complex programs. Python has been used to write web-based systems, desktop applications, games, scientific programs, and even utilities and other higher-level parts of various operating systems.

Python supports a wide range of programming idioms, from straightforward procedural programming to object-oriented programming and functional programming.

Python is sometimes criticized for being an interpreted language, and can be slow compared to compiled languages such as C. However, the use of bytecode compilation and the fact that much of the heavy lifting is done by library code means that Python's performance is often surprisingly good—and there are many things you can do to improve the performance of your programs if you need to.

Open source versions of the Python interpreter are freely available for all major operating systems. Python is eminently suitable for all sorts of programming, from quick one-off scripts to building huge and complex systems. It can even be run in interactive (command-line) mode, allowing you to type in one-off commands and short programs and immediately see the results. This is ideal for doing quick calculations or figuring out how a particular library works.

One of the first things a developer notices about Python compared with other languages such as Java or C++ is how expressive the language is: what may take 20 or 30 lines of code in Java can often be written in half a dozen lines of code in Python. For example, imagine that you wanted to print a sorted list of the words that occur in a given piece of text. In Python, this is easy:

words = set(text.split())
for word in sorted(words):
    print(word)

Implementing this kind of task in other languages is often surprisingly difficult.

While the Python language itself makes programming quick and easy, allowing you to focus on the task at hand, the Python Standard Library makes programming even more efficient. This library makes it easy to do things such as converting date and time values, manipulating strings, downloading data from web sites, performing complex maths, working with e-mail messages, encoding and decoding data, XML parsing, data encryption, file manipulation, compressing and decompressing files, working with databases—the list goes on. What you can do with the Python Standard Library is truly amazing.

As well as the built-in modules in the Python Standard Library, it is easy to download and install custom modules, which could be written either in Python or C. The Python Package Index (http://pypi.python.org) provides thousands of additional modules that you can download and install. And if this isn't enough, many other systems provide Python bindings to allow you to access them directly from within your programs. We will be making heavy use of Python bindings in this book.

Python is in many ways an ideal programming language. Once you are familiar with the language and have used it a few times, you'll find it incredibly easy to write programs to solve various tasks. Rather than getting buried in a morass of type definitions and low-level string manipulation, you can simply concentrate on what you want to achieve. You almost end up thinking directly in Python code. Programming in Python is straightforward, efficient, and, dare I say it, fun.

Python 3

There are two main flavors of Python in use today: the Python 2.x series has been around for many years and is still widely used today, while Python 3.x isn't backward compatible with Python 2 and is becoming more and more popular as it is seen as the main version of Python going forward.

One of the main things holding back the adoption of Python 3 is the lack of support for third-party libraries. This has been particularly acute for Python libraries used for geospatial development, which are often dependent on individual developers or have requirements that were not compatible with Python 3 for quite a long time. However, all the major libraries used in this book can now be run using Python 3, and so all the code examples in this book have been converted to use Python 3 syntax.

If your computer runs Linux or Mac OS X, then you can use Python 3 with all these libraries directly. If, however, your computer runs MS Windows, then Python 3 compatibility is more problematic. In this case, you have two options: you can attempt to compile the libraries yourself to work with Python 3 or you can revert to using Python 2 and make adjustments to the example code as required. Fortunately, the syntax differences between Python 2 and Python 3 are quite straightforward, so not many changes will be required if you do choose to use Python 2.x rather than Python 3.x.