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Learning Geospatial Analysis with Python
Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python: If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Profile Icon Joel Lawhead
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S$74.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (8 Ratings)
Paperback Oct 2013 364 pages 1st Edition
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Arrow left icon
Profile Icon Joel Lawhead
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S$74.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (8 Ratings)
Paperback Oct 2013 364 pages 1st Edition
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Learning Geospatial Analysis with Python

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...

Data structures

Despite dozens of formats, geospatial data have common traits. Understanding these traits can help you approach and understand unfamiliar data formats by identifying the ingredients common to nearly all spatial data. The structure of a given data format is usually driven by its intended use. Some data is optimized for efficient storage or compression; some is optimized for efficient access, some is designed to be lightweight and readable (web formats), while other data formats seek to contain as many different data types as possible.

Interestingly, some of the most popular formats today are also some of the simplest and even lack features found in more capable and sophisticated formats. Ease of use is extremely important to geospatial analysts, because so much time is spent integrating data into geographic information systems as well as exchanging data among analysts. Simple data formats facilitate these activities the best.

Common traits

Geospatial analysis is an approach...

Vector data

Vector data is by far the most common geospatial format because it is the most efficient way to store spatial information, and in general requires less computer resources to store and process than raster data. The Open Geospatial Consortium (OGC) has over 16 formats directly related to vector data. Vector data stores only geometric primitives including points, lines, and polygons. But only the points are stored for each type of shape. For example, in the case of a simple straight vector line shape, only the end points would be necessarily stored and defined as a line. Software displaying that data would read the shape type, and then connect the end points with a line dynamically.

Geospatial vector data is similar to the concept of vector computer graphics with some notable exceptions. Geospatial vector data contains positive and negative Earth-based coordinates, while vector graphics typically store computer screen coordinates. Geospatial vector data is also usually linked to...

Raster data

Raster data consists of rows and columns of cells or pixels, with each cell representing a single value. The easiest way to think of raster data is as images, which is how they are typically represented by software. But raster data sets are not necessarily stored as images. They can also be ASCII text files or Binary Large Objects (BLOBs) in databases.

Another difference between geospatial raster data and regular digital images is resolution. Digital images express resolution as dots-per-inch if printed at full size. Resolution can also be expressed or the total number of pixels in the image defined as megapixels. However, geospatial raster data uses the ground distance each cell represents. For example, a raster data set with two-foot resolution means that a single cell represents two feet on the ground, which also means only objects larger than two feet can be identified visually in the data set.

Raster data sets may contain multiple bands, meaning that different wavelengths...

Point cloud data

Point cloud data is any data collected as the (x,y,z) location of a surface point based on some sort of focused energy return. Point cloud data can be created using lasers, radar waves, acoustic soundings, or other waveform generation devices. The spacing between points is arbitrary and is dependent on the type and position of the sensor collecting the data. In this book we will primarily be concerned with LIDAR data and radar data. Radar point cloud data is typically collected on space missions while LIDAR is typically collected by terrestrial or airborne vehicles. But conceptually both types of data are similar.

LIDAR uses powerful laser range-finding systems to model the world with very high precision. The term LIDAR or LiDAR is a combination of the words light and radar. Some people claim it also stands for Light Detection and Ranging. LIDAR sensors can be mounted on aerial platforms including satellites, airplanes, or helicopters. They can also be mounted on vehicles...

Summary

You now have the background needed to work with common types of geospatial data. You also know the common traits of geospatial data sets which will allow you to evaluate unfamiliar types of data and identify key elements as follows:

  • Geo-location information
  • Subject information
  • Spatial indexing
  • Metadata
  • Basic file structure

In Chapter 3, The Geospatial Technology Landscape, we'll examine the modules and libraries available to work with these data sets. And as with all code in this book, whenever possible, pure Python and standard libraries will be used.

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Key benefits

  • Construct applications for GIS development by exploiting Python
  • Focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system ‚Äì no compiling of C libraries necessary
  • This is a practical, hands-on tutorial that teaches you all about Geospatial analysis in Python

Description

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.

Who is this book for?

This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually.This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python.

What you will learn

  • Automate Geospatial analysis workflows using Python
  • Code the simplest possible GIS in 60 lines of Python
  • Mold thematic maps with Python tools
  • Get a hold of the various forms the geospatial data comes in
  • Produce elevation contours using Python tools
  • Create flood inundation models
  • Learn Real-Time Data tracking and apply it in storm chasing
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Table of Contents

11 Chapters
1. Learning Geospatial Analysis with Python Chevron down icon Chevron up icon
2. Geospatial Data Chevron down icon Chevron up icon
3. The Geospatial Technology Landscape Chevron down icon Chevron up icon
4. Geospatial Python Toolbox Chevron down icon Chevron up icon
5. Python and Geographic Information Systems Chevron down icon Chevron up icon
6. Python and Remote Sensing Chevron down icon Chevron up icon
7. Python and Elevation Data Chevron down icon Chevron up icon
8. Advanced Geospatial Python Modelling Chevron down icon Chevron up icon
9. Real-Time Data Chevron down icon Chevron up icon
10. Putting It All Together Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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4 star 37.5%
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1 star 12.5%
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David Ferguson Oct 26, 2013
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The author draws from his experience in the geospatial industry to apply practical solutions using the Python programming language, A great resource for applying Python to perform GIS/Remote Sensing analysis.
Amazon Verified review Amazon
Jacob C. Ting Feb 10, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'd like to start this review by addressing a few quick questions before I add my own thoughts.First, who would be interested in this book?Really, anyone who has ever had an interest in simply learning what Geospatial Analysis is will benefit from this book, the first chapters go through an excellent introduction into what the field of Geospatial Analysis even is and the topics that cover its range, such as Geographic Information Systems and Remote Sensing. That being said, once you get into the specific topics the focus really shifts to using Python to implement examples of these topics, and an individual who is completely unfamiliar with Python wouldn't benefit as much as someone who has a fairly good Python or programming background. Nevertheless eve if you never ran a single line of code, simply reading the book from front to back should give the reader a good foundation on what Geospatial Analysis is and how it is performed.Second, I'd like to discuss if the book covers what it says it does in its description:The description states it will offer a background in Geosptial Analysis, which it does a great job of in the beginning chapters. Before even jumping into the many Python modules you'll use, readers will gain an understanding of the topic they'll be learning.The second big statement is that the book will teach you how do use Python to implement Geospatial Analysis, and I was pleasantly surprised that the author really strives to keep everything running in pure Python modules whenever possible. The most you'll stray from running pure Python modules is in the use of some file viewers. Another great addition is that the author really pushes the use of open source libraries, throughout the book I never felt like I needed to purchase any of the more intense Geographic Information System software packages that are on the market. I really felt like all I needed was my Python interpreter and an internet connection.The next aspect that I found most enjoyable was that I simply liked reading the book. I initially sat down and just read the book from front to back, without following the code tutorials. I really enjoyed this approach because after I had read through, I simply went back and followed the tutorials that I found to be the most interesting to get a better understanding of how they were used. Another thing I highly enjoyed was seeing the use of some Python packages I have seen used in so many other scientific realms, most notably NumPy, as well as some modules that I'd never seen before, most of which are directly related to Geospatial Analysis.The thing I learned the most from reading this book is that Geospatial Analysis is all about the data, and there is an extremely broad amount of data and data formats that can be used. A large portion of the text will cover what this data is, how it is used, and ways you can use Python to extract, alter, and use this data in creating some pretty cool Geospatial models. Even the very first map you draw depends on using this data. Thankfully you won't have to go searching for this, as the book provides plenty of data files for download.Overall I would highly recommend this book to anyone that has a love of Python and has a real interest in getting a starting background in understanding Geospatial Analysis. I would offer a slight recommendation for those users that are simply looking for a text book about Geospatial Analysis, as the real fun of the book comes from seeing the models and images being manipulated from the solid set of functions that the author gives you in Python. It is obvious that the author cares a great deal about the topic, and the enthusiasm is a definite benefit to the flow of the book. A fun, good read in my opinion as someone who initially knew very little about the topic.
Amazon Verified review Amazon
Anastasia Apr 05, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is sooooo useful for anybody who works with GIS! I used ideas from this book and was able to do projects with PURE Python, and not a single line of ArcPy !!!!
Amazon Verified review Amazon
moria2 Jan 03, 2015
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great book for those who know GIS and are interested in Python
Amazon Verified review Amazon
Mohammed Dec 26, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Great Book overall. the only problem I have is the data sources. all dataset links are not working. I will diffidently recommend it to my friends.
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