Mastering Geospatial Analysis with Python

Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

Paul Crickard , Eric van Rees , Silas Toms 지음

Packt(GCO Science) 2018년 04월 30일 출간

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▶Book Description
Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis.

You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.

▶What You Will Learn
? Manage code libraries and abstract geospatial analysis techniques using Python 3.
? Explore popular code libraries that perform specific tasks for geospatial analysis.
?Utilize code libraries for data conversion, data management, web maps, and REST API creation.
?Learn techniques related to processing geospatial data in the cloud.
?Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and SpatiaLite.

▶Key Features
? Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas
? Leverage new ArcGIS API to process geospatial data for the cloud.
? Explore various Python geospatial web and machine learning frameworks.

▶Who This Book Is For
The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3.

▶What this book covers
? Chapter 1, Package Installation and Management, explains how to install and manage the code libraries used in the book.
? Chapter 2, Introduction to Geospatial Code Libraries, covers the major code libraries used to process and analyze geospatial data.
? Chapter 3, Introduction to Geospatial Databases, introduces the geospatial databases used for data storage and analysis.
? Chapter 4, Data Types, Storage, and Conversion, focuses on the many different data types (both vector and raster) that exist within GIS.
? Chapter 5, Vector Data Analysis, covers Python libraries such as Shapely, OGR, and GeoPandas. which are used for analyzing and processing vector data.
? Chapter 6, Raster Data Processing, explores using GDAL and Rasterio to process raster datasets in order to perform geospatial analysis.
? Chapter 7, Geoprocessing with Geodatabases, shows the readers how to use Spatial SQL to perform geoprocessing with database tables containing a spatial column.
? Chapter 8, Automating QGIS Analysis, teaches the readers how to use PyQGIS to automate analysis within the QGIS mapping suite.
? Chapter 9, ArcGIS API for Python and ArcGIS Online, introduces the ArcGIS API for Python, which enables users to interact with Esri's cloud platform, ArcGIS Online, using Python 3.
? Chapter 10, Geoprocessing with a GPU Database, covers using Python tools to interact with cloud-based data to search and process data.
? Chapter 11, Flask and GeoAlchemy2, describes how to use the Flask Python web framework and the GeoAlchemy ORM to perform spatial data queries.
? Chapter 12, GeoDjango, covers using the Django Python web framework and the GeoDjango ORM to perform spatial data queries.
? Chapter 13, Geospatial REST API, teaches the readers how to create a REST API for geospatial data.
? Chapter 14, Cloud G

목차

▶TABLE of CONTENTS
1: PACKAGE INSTALLATION AND MANAGEMENT
2: INTRODUCTION TO GEOSPATIAL CODE LIBRARIES
3: INTRODUCTION TO GEOSPATIAL DATABASES
4: DATA TYPES, STORAGE, AND CONVERSION
5: VECTOR DATA ANALYSIS
6: RASTER DATA PROCESSING
7: GEOPROCESSING WITH GEODATABASES
8: AUTOMATING QGIS ANALYSIS
9: ARCGIS API FOR PYTHON AND ARCGIS ONLINE
10: GEOPROCESSING WITH A GPU DATABASE
11: FLASK AND GEOALCHEMY2
12: GEODJANGO
13: GEOSPATIAL REST API
14: CLOUD GEODATABASE ANALYSIS AND VISUALIZATION
15: AUTOMATING CLOUD CARTOGRAPHY
16: PYTHON GEOPROCESSING WITH HADOOP

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▶Editorial Review
Over time, Python has become the programming language of choice for spatial analysis, resulting in many packages that read, convert, analyze, and visualize spatial data. With so many packages available, it made sense to create a reference book for students and experienced professionals containing essential geospatial Python libraries for Python 3.

This book also comes at an exciting moment: new technology is transforming how people work with geospatial data ？. IoT, machine learning, and data science are areas where geospatial data is used constantly. This explains the inclusion of new Python libraries, such as CARTOframes and MapboxGL, and Jupyter is included as well, to explore these new trends. At the same time, web and cloud-based GIS is increasingly becoming the new standard. This is reflected in the chapters of the second part of this book, where interactive geospatial web maps and REST APIs are introduced.

These newer libraries are combined with a number of older ones that have become essential over the years, and are still very popular to this day, such as Shapely, Rasterio, and GeoPandas. Readers who are new to this field will be given a proper introduction to popular libraries, putting them into perspective and comparing their syntax through code examples using real-world data.

Finally, this books marks the transition from Python 2 to 3.x. All of the libraries covered in this book were written in Python 3.x so that the readers can access all of them using Jupyter Notebook, which is also the recommended Python coding environment for this book.

작가정보

저자(글) Paul Crickard

Paul Crickard III has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD, and built web-based GIS applications to display and modify architectural data. He has given presentations to the New Mexico Public School Facilities Authority on BIM and GIS integration and on the use of GIS for Facility Planning, and the BIM505 Users Group on GIS as an interactive frontend to BIM and editing BIM data via web applications. Currently, Paul works as a programmer analyst in Albuquerque, specializing in the design, maintenance, and the implementation of geospatial applications. He has written plugins and extensions for ArcMap and ArcGIS Explorer Desktop to utilize NoSQL databases and send data using the Advanced Message Queuing Protocol (AMQP). Paul has built applications using OpenLayers and Leaflet.js and is currently utilizing the ESRI JavaScript API in production. Paul tries to incorporate Python in geospatial development wherever possible. From building plugins, toolboxes, and the Field Calculator functions in ArcMap to coding standalone desktop and web applications, pyshp is his favorite library for geospatial Python applications. When he is not coding, Paul enjoys relaxing with his wife and son, cooking, and brewing beer.

저자(글) Eric van Rees

Eric van Rees was first introduced to Geographical Information Systems (GIS) when studying Human Geography in the Netherlands. For 9 years, he was the editor-in-chief of GeoInformatics, an international GIS, surveying, and mapping publication and a contributing editor of GIS Magazine. During that tenure, he visited many geospatial user conferences, trade fairs, and industry meetings. He focuses on producing technical content, such as software tutorials, tech blogs, and innovative new use cases in the mapping industry.

저자(글) Silas Toms

Silas Toms is a certified GIS Professional and the author of the first edition of ArcPy and ArcGIS. President and founder of Loki Intelligent Corporation, a location information firm located in San Francisco, California, he is an expert in real-time geographic information systems and analysis automation. Along with Dara O'Beirne and Arini Geographics, he developed the real-time common operational picture used at Super Bowl 50 and all other events at Levi's Stadium in Santa Clara, California. This dynamic system was recognized by the White House and ESRI President, Jack Dangermond, as a unique and powerful application of GIS, allowing the federal, state, and local government to coordinate and communicate in real time, for the first time ever.As the President of Loki Intelligent, Silas is focused on unique applications of GIS that will power the future of location information. The sheer amount of data collected through sensors and mobile reporting demands automation and data processing improvements to turn the raw input into location intelligence. He believes that correct application of geospatial analysis, web mapping, and mobile data collection will improve the decision-making processes within the government and business. Loki is location information, and information is power