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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

By : Joel Lawhead
4.1 (8)
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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

4.1 (8)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (12 chapters)
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11
Index

Chapter 2. Geospatial Data

The most challenging aspect of geospatial analysis is the data. Geospatial data includes dozens of file formats and database structures already and continues to evolve and grow to include new types of data and standards. Additionally almost any file format can technically contain geospatial information simply by adding a location. As a geospatial analyst you may frequently encounter the following general data types:

  • Spreadsheets and comma or tab-delimited files (CSV files)
  • Geo-tagged photos
  • Lightweight binary points, lines, and polygons
  • Multigigabyte satellite or aerial images
  • Elevation data such as grids, point clouds, or integer-based images
  • XML files
  • JSON files
  • Databases (both servers and file databases)

Each format contains its own challenges for access and processing. When you perform analysis on data, usually you have to do some form of preprocessing first. You might clip a satellite image of a large area down to just your area of interest. Or you might...

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