The best books change the way you look at the world. They take your mind to a different place than where you started. The transformation we experience from a good book is the reason books have survived for centuries as a way to share the breadth of human experience.
This book is about geospatial analysis. Geospatial analysis is the combination of statistical analysis, computational geometry, and image processing applied to data which is tied to the Earth (or even other planets). But that technical definition falls short of what geospatial analysis truly is. Similar to a good book, geospatial analysis tells a story about our world. This story is told through thematic maps, processed satellite images, and tables of information.
These stories quite literally change your worldview by revealing patterns about human behavior and natural processes that are otherwise difficult to discern or are even invisible to us. The increased awareness of our world and our place in it allows us to make better decisions about everything from agriculture to politics to disaster management.
This book will teach you geospatial analysis using the Python programming language. Python is a very popular and easy to learn language used in nearly every field. Python was invented in the late 1980s by Guido van Rossum and is based on the language "ABC" designed to teach programming to kids. The clean and intuitive syntax allows you to think about the problem you are trying to solve and not the language you are using. It also interfaces well with nearly every geospatial library available.
Learning Geospatial Analysis with Python supplements the library of Packt Publishing with a third book on geospatial technology and Python. The series offered by Packt Publishing covers the most complete range of published knowledge in this domain. In order to understand the scope of this book and its benefits, it helps to be familiar with the other offerings by Packt Publishing.
Python Geospatial Development by Erik Westra covers building desktop and web applications using Python and leading open source geospatial libraries. The focus of the book is capturing well-defined geospatial processes as requirements and then developing applications allowing users to interactively execute that process again and again.
Programming ArcGIS 10.1 with Python Cookbook by Eric Pimpler teaches readers how to automate ArcGIS 10.1, the leading Geographic Information System (GIS) software package by Esri. ArcGIS contains a Python environment called ArcPy that provides an interface to nearly the entire package. The book shows how to use Python to script the ArcGIS for a variety of geoprocessing tasks.
Geospatial analysis will allow you to look at the world in a whole new way and with new understanding. And Python will facilitate the journey and even make it fun! This book will serve as both a guide and future reference as you move deeper into this exciting field.
Chapter 1, Learning Geospatial Analysis with Python, introduces geospatial analysis as a way of answering questions about our world. The differences between GIS and remote sensing are explained. Common geospatial analysis processes are illustrated and a code for a simple geographic information system in Python is introduced.
Chapter 2, Geospatial Data, discusses geospatial data, and explains the forms geospatial data comes in. The most challenging part of geospatial analysis is acquiring the data you need and preparing it for analysis. This chapter explains the two major categories of data as well as several newer formats that are becoming more and more common. Familiarity with these data types is essential to understand geospatial analysis.
Chapter 3, The Geospatial Technology Landscape, covers the geospatial technology ecosystem that consists of thousands of software libraries and packages. This vast array of choices is overwhelming for newcomers to geospatial analysis. The secret to learning geospatial analysis quickly is to understand the handful of libraries and packages that really matter. Most other software is derived from these critical packages. Understanding the hierarchy of geospatial software and how it's used allows you to quickly comprehend and evaluate any geospatial tool.
Chapter 4, Geospatial Python Toolbox, explains the software and libraries introduced which forms the basis of the book and are used throughout. In this chapter, Python's role within the geospatial industry is elaborated: GIS scripting language, mash-up glue language, and full-blown programming language. Code examples are used to teach data editing concepts, and many of the basic geospatial concepts in Chapter 1, Learning Geospatial Analysis with Python, are also demonstrated in Python.
Chapter 5, Python and Geographic Information Systems, teaches the simple yet practical python GIS geospatial products using processes which can be applied to a variety of problems.
Chapter 6, Python and Remote Sensing, shows readers how to work with remote sensing geospatial data. Remote sensing includes some of the most complex and least documented geospatial operations. This chapter will build a solid core for the reader and demystify remote sensing using Python.
Chapter 7, Python and Elevation Data, demonstrates the most common uses of elevation data, which can be contained in almost any geospatial format but is used quite differently from other types of geospatial data, and will show you how to work with its unique properties.
Chapter 8, Advanced Geospatial Python Modeling, discusses how geospatial data editing and processing help us understand the world as it is. But the true power of geospatial analysis is modeling. Geospatial models help us predict the future, narrow vast fields of choices down to the best options, and visualize concepts which cannot be directly observed in the natural world. This chapter uses Python to teach the reader the true power of geospatial technology.
Chapter 9, Real-Time Data, introduces real-time data and examines a modern phenomenon. A wise geospatial analyst once said, "As soon as a map is created it is obsolete." Until recently, by the time you collected data about the earth, processed it, and created a geospatial product, the world it represented had already changed. But modern geospatial data shatters this notion. Data sets are available over the Internet which are up to the minute or even the second. These data sets fundamentally change the way we perform geospatial analysis.
Chapter 10, Putting It All Together, combines the skills from previous chapters step-by-step to build a simple, automated geospatial analysis system which produces a report.
To follow through the various examples, you will need to download and install the following software:
Python Version 2.x (minimum Version 2.5)
GDAL/OGR Version 1.7.1 or later
GEOS Version 3.2.2 or later
PyShp 1.1.6 or later
Shapely Version 1.2 or later
Proj Version 4.7 or later
PyProj Version 1.8.6 or later
NumPy
PNGCanvas
Python Imaging Library (PIL)
This book assumes at least a basic working knowledge of Python and a familiarity with geospatial analysis. Procedures for unloading and installing these tools are covered in the relevant chapters of this book as needed.
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.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We created a shapefile Reader object instance and set it to the variable r".
A block of code is set as follows:
>>> import math >>> x1 = 456456.23123582301 >>> y1 = 1279721.064356426 >>> x2 = 576628.34295886324 >>> y2 = 1071740.3328161312 >>> x_dist = x1 - x2 >>> y_dist = y1 - y2 >>> dist_sq = x_dist**2 + y_dist**2 >>> distance = math.sqrt(dist_sq) >>> distance 240202.6667795573
Any command-line input or output is written as follows:
C:\>python Python 2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes, for example, appear in the text like this: "In the Properties window, select the Advanced tab".
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