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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

By : Diener
4.4 (5)
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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

4.4 (5)
By: Diener

Overview of this book

Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Table of Contents (15 chapters)
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12
A. Other Geospatial Python Libraries
13
B. Mapping Icon Libraries
14
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:

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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