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首页Python 3地理空间分析入门:第二版全面指南
Python 3地理空间分析入门:第二版全面指南
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更新于2024-07-20
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"《学习地理空间分析与Python(第二版)》是一本由Joel Lawhead编写的权威指南,专为那些希望利用Python 3进行地理信息系统(GIS)和遥感数据分析的读者设计。本书针对的是英文原版,适合于对地理信息科学和技术有兴趣的开发者和专业人员,无论他们是初学者还是进阶者。 该书详细介绍了如何通过Python进行空间数据处理、可视化、分析和建模,覆盖了从基础概念到高级应用的各种主题,包括地理坐标系统、数据获取(如遥感图像)、地图制作、地理编码与逆地理编码、空间查询和操作、以及基于开源库如GDAL、 Fiona、Pandas和matplotlib的实践应用。作者强调了Python在GIS领域的灵活性和实用性,使读者能够快速掌握这一工具并将其应用于实际项目。 第二版更新了Python 3的新特性和库,确保了内容的时效性。书中不仅提供理论知识,还包含丰富的实例和实战项目,帮助读者通过实践提升技能。版权方面,所有内容受Packt Publishing保护,未经许可不得复制或传播,除非用于学术引用。 尽管本书力求准确,但并不保证所有信息的绝对正确性,且销售时不附带任何明示或默示的保修。作者及出版商不对因使用本书导致的直接或间接损失承担责任。此外,书中提及的所有商标信息都经过精心标注,体现了Packt Publishing对知识产权的尊重。 《学习地理空间分析与Python(第二版)》是一本既实用又全面的教程,对于希望在地理信息技术领域深入探索的读者来说,是不可多得的学习资源。通过阅读和实践本书,读者将能够建立起坚实的Python地理分析基础,并在实际工作中发挥关键作用。"
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Table of Contents
[ vii ]
Chapter 10: Putting It All Together 333
A typical GPS report 334
Working with GPX-Reporter.py 334
Stepping through the program 335
The initial setup 336
Working with utility functions 338
Parsing the GPX 342
Getting the bounding box 343
Downloading map and elevation images 344
Creating the hillshade 346
Creating maps 347
Measuring the elevation 351
Measuring the distance 352
Retrieving weather data 353
Summary 358
Index 359
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[ ix ]
Preface
The book starts with a background on geospatial analysis, offers a ow of techniques
and technology used, and splits the eld into its component specialty areas, such as
Geographic Information Systems (GIS), remote sensing, elevation data, advanced
modeling, and real-time data. The focus of the book is to lay a strong foundation in
using the powerful Python language and framework in order to approach geospatial
analysis effectively. In doing so, we'll focus on using pure Python as well as certain
Python tools and APIs, and using generic algorithms. The readers will be able to
analyze various forms of geospatial data that comes in and learn real-time data
tracking and how to apply this to interesting scenarios.
While many third-party geospatial libraries are used throughout the examples, a
special effort will made by us to use pure Python, with no dependencies, whenever
possible. This focus on pure Python 3 examples is what will set this book apart from
nearly all other information in this eld. This book may be the only geospatial book
using Python 3 on the market currently. We will also go through some popular
libraries that weren't in the previous version of the book.
What this book covers
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
demonstrated using illustrations, basic formulas, pseudo code, and Python.
Chapter 2, Geospatial Data, explains the major categories of data as well as several
newer formats that are becoming more and more common. Geospatial data comes
in many forms. The most challenging part of geospatial analysis is acquiring the
data that you need and preparing it for analysis. Familiarity with these data types is
essential to understand geospatial analysis.
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Preface
[ x ]
Chapter 3, The Geospatial Technology Landscape, tells you about 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 understanding 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, introduces software and libraries that form the
basis of the book and are used throughout. Python's role in the geospatial industry
is elaborated: the 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 Using
Python, are also demonstrated in this chapter.
Chapter 5, Python and Geographic Information Systems, teaches you about 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 you 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 you and
demystify remote sensing using Python.
Chapter 7, Python and Elevation Data, demonstrates the most common uses of
elevation data and how to work with its unique properties. Elevation data deserves
a chapter all on its own. Elevation data can be contained in almost any geospatial
format but is used quite differently from other types of geospatial data.
Chapter 8, Advanced Geospatial Python Modeling, uses Python to teach you the true
power of geospatial technology. Geospatial data editing and processing help us
understand the world as it is. The true power of geospatial analysis is modeling.
Geospatial models help us predict the future, narrow a vast eld of choices down
to the best options, and visualize concepts that cannot be directly observed in the
natural world.
Chapter 9, Real-Time Data, examines the modern phenomena of geospatial analysis.
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 shatter this notion. Data sets are available over the Internet which are
up to the minute or even the second. This data fundamentally changes the way we
perform geospatial analysis.
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Preface
[ xi ]
Chapter 10, Putting It All Together, combines the skills from the previous chapters step
by step to build a generic corporate system to manage customer support requests
and eld support personnel that could be applied to virtually any organization.
What you need for this book
You will require Python (3.4 or higher), a minimum hardware requirement of a
300-MHz processor, 128 MB of RAM, 1.5 GB of available hard disk, and Windows,
Linux, or OS X operating systems.
Who this book is for
This book is for anyone who wants to understand digital mapping and analysis and
who uses Python or any other scripting language for the automation or crunching
of data manually. This book primarily targets Python developers, researchers, and
analysts who want to perform geospatial, modeling, and GIS analysis with Python.
Conventions
In this book, you will nd a number of text styles 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, lenames, le extensions,
pathnames, dummy URLs, user input, and Twitter handles are shown as follows:
"The
pycsw Python library implements the CSW standard."
A block of code is set as follows:
<?xml version="1.0" encoding="utf-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Placemark>
<name>Mockingbird Cafe</name>
<description>Coffee Shop</description>
<Point>
<coordinates>-89.329160,30.310964</coordinates>
</Point>
</Placemark>
</kml>
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