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Python Geospatial Analysis Cookbook

You're reading from  Python Geospatial Analysis Cookbook

Product type Book
Published in Nov 2015
Publisher
ISBN-13 9781783555079
Pages 310 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Python Geospatial Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Setting Up Your Geospatial Python Environment 2. Working with Projections 3. Moving Spatial Data from One Format to Another 4. Working with PostGIS 5. Vector Analysis 6. Overlay Analysis 7. Raster Analysis 8. Network Routing Analysis 9. Topology Checking and Data Validation 10. Visualizing Your Analysis 11. Web Analysis with GeoDjango Other Geospatial Python Libraries
Mapping Icon Libraries
Index

Discovering projection(s) of a Shapefile or GeoJSON dataset


Remember that all data is stored in a coordinate system, no matter what the data source is. It is your job to figure this out using a simple approach outlined in this section. We will take a look at two different data storage types: a Shapefile and a GeoJSON file. These two formats contain geometries, such as points, lines, or polygons, and their associated attributes. For example, a tree would be stored as a point geometry with attributes, such as height, age, and species, Each of these data types store their projection data differently and, therefore, require different methods to discover their projection information.

Now a quick introduction to what a Shapefile is: a Shapefile is not a single file but a minimum of three files, such as .shp, .shx, and, .dbf, all of which have the same name. For example, world_borders.shp, world_borders.shx and world_borders.dbf make up one file. The .shp file stores geometry, .dbf stores a table...

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