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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

Vector data analysis is used in many, many application areas, starting from measuring the distance from point A to point B all the way through to complex routing algorithms. The first GIS systems were built on vector data and vector analysis, and then later expanded into the raster domain. In this chapter, we will start with simple vector operations, then work our way into a more complex model, chaining the various vector methods together to deliver new data that answers our spatial questions.

This process of data analysis is broken down into a couple of steps starting with an input dataset, performing a spatial operation on the data such as a buffer analysis, and, finally, we'll have some output in the form of a new dataset. The following diagram shows the flow of analysis in the simplest model form:

Introduction

Converting simple questions into spatial operation methods and models takes experience and is not a simple task. For example, you may come across a simple task such as, &quot...

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