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Python Geospatial Analysis Cookbook

You're reading from   Python Geospatial Analysis Cookbook Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python

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

Table of Contents (15) Chapters Close

Preface 1. Setting Up Your Geospatial Python Environment FREE CHAPTER 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 A. Other Geospatial Python Libraries
B. Mapping Icon Libraries
Index

Introduction

Geospatial data comes in hundreds of formats and massaging this data from one format to another is a simple task. The ability to convert between data types, such as rasters or vectors, belongs to data wrangling tasks and can be used for better geospatial analysis. Here is an example of a raster and vector dataset so that you can take a look at what I am talking about:

Introduction

The best practice methodology is to run analysis functions or models over data stored in a common format, such as a PostgreSQL PostGIS database, or a set of Shapefiles in a common coordinate system. For example, running an analysis on input data stored in multiple formats is also possible, but you can expect to find the devil in the detail if something goes wrong or your results are not as you expected them to be.

This chapter looks at some common data formats and demonstrates how to move these from one format to another with the help of the most common tools.

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