Python Geospatial Analysis Essentials : Process, Analyze, and Display Geospatial Data Using Python Libraries and Related Tools

Process, analyze, and display geospatial data using Python libraries and related tools About This Book Learn to build a complete geospatial application from scratch using Python Create good-looking maps based on the results of your analysis This is a fast-paced guide to help you explore the key concepts of geospatial to obtain high quality spatial data Who This Book Is For If you are an experienced Python developer and wish to get up-to-speed with geospatial programming, then this book is for you. While familiarity with installing third-party Python libraries would be an advantage, no prior knowledge of geospatial programming is required. What You Will Learn Understand the key geospatial concepts and techniques needed to analyze and work with geospatial data Learn how to read and write geospatial data from within your Python code Use PostGIS to store spatial data and perform spatial queries Use Python libraries to analyze and manipulate geospatial data Generate maps based on your spatial data Implement complete geospatial analysis systems using Python Use the Shapely and NetworkX libraries to solve problems such as distance-area calculations, finding the shortest path between two points, buffering polygons, and much more In Detail Python is a highly expressive language that makes it easy to write sophisticated programs. Combining high-quality geospatial data with Python geospatial libraries will give you a powerful toolkit for solving a range of geospatial programming tasks. The book begins with an introduction to geospatial analysis and programming and explains the ideas behind geospatial data. You will explore Python libraries for building your own geospatial applications. You will learn to create a geospatial database for your application using PostGIS and the psycopg2 library, and see how the Mapnik library can be used to create attractive and useful maps. Finally, you will learn to use the Shapely and NetworkX libraries to create, analyze, and manipulate complex geometric objects, before implementing a system to match GPS recordings against a database of roads to produce a heatmap of the most frequently used roads. Cover 1 Copyright 1 Credits 1 About the Author 1 About the Reviewers 1 www.PacktPub.com 1 Table of Contents 1 Preface 1 Chapter 1: Geospatial Analysis and Techniques 1 About geospatial analysis 1 Understanding geospatial data 1 Setting up your Python installation 24 Installing GDAL 25 Installing Shapely 26 Obtaining some geospatial data 28 Unlocking the shapefile 29 Analyzing the data 31 A program to identify neighboring countries 33 Summary 36 Chapter 2: Geospatial Data 38 Geospatial data quality 39 Types of geospatial data 1 Shapefiles 1 Well-known text 1 Well-known binary 1 Spatial databases 1 Geospatial microformats 1 GeoJSON 1 GML 1 Digital elevation models 48 Raster basemaps 49 Multiband raster files 50 Sources of freely available geospatial data 51 Natural Earth Data 52 OpenStreetMap 1 US Census Bureau 55 World Borders Dataset 55 GLOBE 55 National Elevation Dataset 56 Reading and writing geospatial data using Python 56 Reading vector data 56 Writing vector data 58 Reading raster data 60 Writing raster data 62 Dealing with spatial reference systems 64 WGS84 66 Universal Transverse Mercator 67 Describing spatial reference systems 68 Transforming coordinates 69 Calculating lengths and areas 69 Geospatial data errors and how to fix them 71 Points 71 LineStrings 72 Linear Rings 72 Polygons 73 MultiPolygons 73 Fixing invalid geometries 73 Summary 76 Chapter 3: Spatial Databases 78 Spatial database concepts 79 Installing a spatial database 83 Installing PostgreSQL 83 Installing PostGIS 84 Installing psycopg2 85 Accessing PostGIS from Python 86 Setting up a spatial database 86 Importing spatial data 89 Querying spatial data 92 Manipulating spatial data 93 Exporting spatial data 97 Summary 97 Chapter 4: Creating Maps 98 Introducing Mapnik 98 Installing Mapnik 99 A taste of Mapnik 99 Building a map 103 Styling a map 104 Learning Mapnik 106 Datasources 106 Symbolizers 108 PointSymbolizer 109 LineSymbolizer 110 PolygonSymbolizer 111 TextSymbolizer 112 RasterSymbolizer 115 Map rendering 116 A working example 117 Next steps 121 Summary 122 Chapter 5: Analyzing Geospatial Data 124 Libraries for spatial analysis 125 PyProj 125 NetworkX 130 Spatial analysis recipes 132 Calculating and comparing coordinates 132 Calculating lengths 135 Calculating areas 139 Calculating shortest paths 142 Summary 150 Chapter 6: Building a Complete Geospatial Analysis System 152 Matching GPS data against a map 153 An overview of the GPS Heatmap system 154 Obtaining the necessary data 155 Obtaining GPS data 156 Downloading the road data 156 Implementing the GPS Heatmap system 158 Initializing the database 159 Importing the road data 160 Splitting the road data into segments 162 Constructing a network of directed road segments 165 Implementing the map-matching algorithm 170 Generating the GPS Heatmap 184 Further improvements 188 Summary 189 Index 192

About This Book

• Learn how to design and deploy an OpenStack private cloud using automation tools and best practices
• Gain valuable insight into OpenStack components and new services
• Explore the opportunities to build a scalable OpenStack infrastructure with this comprehensive guide

Who This Book Is For

This book is intended for system administrators, cloud engineers, and system architects who want to deploy a cloud based on OpenStack in a mid- to large-sized IT infrastructure. If you have a fundamental understanding of cloud computing and OpenStack and want to expand your knowledge, then this book is an excellent checkpoint to move forward.

What You Will Learn

• Explore the main architecture design of OpenStack components, core-by-core services, and how they work together
• Learn how to distribute OpenStack services among cluster setup
• Compare different storage solutions and driver extensions
• Design different high availability scenarios and how to plan for a no Single Point Of Failure environment
• Set up a multinode environment in production using orchestration tools
• Boost OpenStack performance with advanced configuration
• Establish a distributed monitoring solution and keep track of resource consumption

In Detail

This comprehensive guide will help you to choose the right practical option and make strategic decisions about the OpenStack cloud environment to fit your infrastructure in production.

At the start, this book will explain the OpenStack core architecture. You will soon be shown how to create your own OpenStack private cloud.

Next, you will move on to cover the key security layer and network troubleshooting skills, along with some advanced networking features. Finally, you will gain experience of centralizing and logging OpenStack. The book will show you how to carry out performance tuning based on OpenStack service logs.

By the end of this book, you will be ready to take steps to deploy and manage an OpenStack cloud with the latest open source technologies.

Key FeaturesBook DescriptionWhat you will learnUnderstand the key geospatial concepts and techniques needed to analyze and work with geospatial dataLearn how to read and write geospatial data from within your Python codeUse PostGIS to store spatial data and perform spatial queriesUse Python libraries to analyze and manipulate geospatial dataGenerate maps based on your spatial dataImplement complete geospatial analysis systems using PythonUse the Shapely and NetworkX libraries to solve problems such as distancearea calculations, finding the shortest path between two points, buffering polygons, and much moreWho this book is forIf you are an experienced Python developer and wish to get up-to-speed with geospatial programming, then this book is for you. While familiarity with installing third-party Python libraries would be an advantage, no prior knowledge of geospatial programming is required.