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

Summary

In this chapter, we briefly introduced the Python programming language and the main concepts behind geospatial development. We saw that Python is a very high-level language and that the availability of third-party libraries for working with geospatial data makes it eminently suited to the task of geospatial development. We learned that the term geospatial data refers to finding information that is located on the earth's surface using coordinates, and the term "geospatial development" refers to the process of writing computer programs that can access, manipulate, and display geospatial data.

We then looked at the types of questions that can be answered by analyzing geospatial data, saw how geospatial data can be used for visualization, and learned about geospatial mash-ups, which combine data (often geospatial data) in useful and interesting ways.

Next, we learned how Google Maps, Google Earth, and the development of cheap and portable GPS units have "democratized" geospatial development. We saw how the open source software movement has produced a number of high-quality, freely available tools for geospatial development and looked at how various standards organizations have defined formats and protocols for sharing and storing geospatial data.

Finally, we saw how geolocation is being used to capture and work with geospatial data in surprising and useful ways.

In the next chapter, we will look in more detail at traditional geographic information systems including a number of important concepts that you need to understand in order to work with geospatial data. Different geospatial formats will be examined, and we will finish by using Python to perform various calculations using geospatial data.