Geospatial Development By Example with Python

Packt Publishing Ltd, 30/01/2016 - 340 páginas

Build your first interactive map and build location-aware applications using cutting-edge examples in Python

About This BookLearn the full geo-processing workflow using Python with open source packagesCreate press-quality styled maps and data visualization with high-level and reusable codeProcess massive datasets efficiently using parallel processingWho 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 LearnPrepare a development environment with all the tools needed for geo-processing with PythonImport point data and structure an application using Python's resourcesCombine point data from multiple sources, creating intuitive and functional representations of geographic objectsFilter data by coordinates or attributes easily using pure PythonMake press-quality and replicable maps from any dataDownload, transform, and use remote sensing data in your mapsMake calculations to extract information from raster data and show the results on beautiful mapsHandle massive amounts of data with advanced processing techniquesProcess huge satellite images in an efficient wayOptimize geo-processing times with parallel processingIn 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.



Preparing the Work Environment
The Geocaching App
Combining Multiple Data Sources
Improving the App Search Capabilities
Making Maps
Working with Remote Sensing Images
Extract Information from Raster Data
Data Miner App
Processing Big Images
Parallel Processing
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Acerca do autor (2016)

Pablo Carreira is a Python programmer and a full stack developer living in Sao Paulo state, Brazil. He is now the lead developer of an advanced web platform for precision agriculture and actively uses Python as a backend solution for efficient geoprocessing. Born in 1980, Brazil, Pablo graduated as an agronomical engineer. Being a programming enthusiast and self-taught since childhood, he learned programming as a hobby and later honored his techniques in order to solve work tasks. Having 8 years of professional experience in geoprocessing, he uses Python along with geographic information systems in order to automate processes and solve problems related to precision agriculture, environmental analysis, and land division.

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