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Python Geospatial Development

You're reading from   Python Geospatial Development Develop sophisticated mapping applications from scratch using Python 3 tools for geospatial development

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Product type Paperback
Published in May 2016
Publisher
ISBN-13 9781785288937
Length 446 pages
Edition 3rd Edition
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Author (1):
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Erik Westra Erik Westra
Author Profile Icon Erik Westra
Erik Westra
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Table of Contents (15) Chapters Close

Preface 1. Geospatial Development Using Python FREE CHAPTER 2. GIS 3. Python Libraries for Geospatial Development 4. Sources of Geospatial Data 5. Working with Geospatial Data in Python 6. Spatial Databases 7. Using Python and Mapnik to Generate Maps 8. Working with Spatial Data 9. Improving the DISTAL Application 10. Tools for Web-based Geospatial Development 11. Putting It All Together – a Complete Mapping System 12. ShapeEditor – Importing and Exporting Shapefiles 13. ShapeEditor – Selecting and Editing Features Index

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...
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