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 6. Spatial Databases

In this chapter, we will look at how you can use a PostGIS database to store and work with spatial data. In particular, we will cover:

  • The concept of a spatially enabled database
  • Spatial indexes and how they work
  • How PostGIS acts as an extension to the PostgreSQL relational database
  • How to install PostgreSQL, PostGIS, and the psycopg2 Python database adapter onto your computer
  • How to set up and configure a spatial database using PostGIS
  • How to use the psycopg2 database adapter to access a spatial database from your Python code
  • How to create, import, and query against spatial data using Python
  • Recommended best practices for storing spatial data in a database

This chapter is intended to be an introduction to using databases in a geospatial application. Chapter 8, Working with Spatial Data, will build on this to perform powerful spatial queries not possible using shapefiles and other geospatial data files.

Spatially-enabled databases

In a sense, almost any database can be used to store geospatial data: simply convert a geometry to WKT format and store the results in a text column. But while this would allow you to store geospatial data in a database, it wouldn't let you query it in any useful way. All you could do is retrieve the raw WKT text and convert it back to a geometry object, one record at a time.

A spatially-enabled database, on the other hand, is aware of the notion of space, and allows you to work with spatial objects and concepts directly. In particular, a spatially-enabled database allows you to:

  • Store spatial data types (points, lines, polygons, and so on) directly in the database in the form of a geometry column
  • Perform spatial queries on your data, for example, select all landmarks within 10 km of the city named "San Francisco"
  • Perform spatial joins on your data, for example, select all cities and their associated countries by joining cities and countries on (city inside country)
  • Create new spatial objects using various spatial functions, for example, set "danger_zone" to the intersection of the "flooded_area" and "urban_area" polygons

As you can imagine, a spatially-enabled database is an extremely powerful tool for working with geospatial data. By using spatial indexes and other optimizations, spatial databases can quickly perform these types of operations and can scale to support vast amounts of data simply not feasible using other data-storage schemes.