Python Geospatial Development Essentials
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Python Geospatial Development Essentials
This book provides you with the resources to successfully develop your own GIS application in Python. The book begins by walking you through the loading and saving of data structures before you start to build the look and feel of your application and create its interactive map window. You'll then move on to managing, editing, and analyzing spatial data from within the application and finish with instructions for packaging the application for distribution. By the end of the book, you should be able to use your GIS application as a template for further development, with the potential to expand and customize it to suit your needs.
Geospatial Development By Example with Python
Author: Pablo Carreira
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-01-30
Build your first interactive map and build location-aware applications using cutting-edge examples in Python About This Book Learn the full geo-processing workflow using Python with open source packages Create press-quality styled maps and data visualization with high-level and reusable code Process massive datasets efficiently using parallel processing Who This Book Is For Geospatial Development By Example with Python is intended for beginners or advanced developers in Python who want to work with geographic data. The book is suitable for professional developers who are new to geospatial development, for hobbyists, or for data scientists who want to move into some simple development. What You Will Learn Prepare a development environment with all the tools needed for geo-processing with Python Import point data and structure an application using Python's resources Combine point data from multiple sources, creating intuitive and functional representations of geographic objects Filter data by coordinates or attributes easily using pure Python Make press-quality and replicable maps from any data Download, transform, and use remote sensing data in your maps Make calculations to extract information from raster data and show the results on beautiful maps Handle massive amounts of data with advanced processing techniques Process huge satellite images in an efficient way Optimize geo-processing times with parallel processing In Detail From Python programming good practices to the advanced use of analysis packages, this book teaches you how to write applications that will perform complex geoprocessing tasks that can be replicated and reused. Much more than simple scripts, you will write functions to import data, create Python classes that represent your features, and learn how to combine and filter them. With pluggable mechanisms, you will learn how to visualize data and the results of analysis in beautiful maps that can be batch-generated and embedded into documents or web pages. Finally, you will learn how to consume and process an enormous amount of data very efficiently by using advanced tools and modern computers' parallel processing capabilities. Style and approach This easy-to-follow book is filled with hands-on examples that illustrate the construction of three sample applications of how to write reusable and interconnected Python code for geo-processing.
Python Geospatial Analysis Essentials
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.