Geospatial development by example with Python : build your first interactive map and build location-aware applications using cutting-edge examples in Python / Pablo Carreira.

Build your first interactive map and build location-aware applications using cutting-edge examples in PythonAbout 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 processingWho This Book Is ForGeospatial 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 processingIn DetailFrom 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 approachThis 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.

Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Preparing the Work Environment -- Installing Python -- Windows -- Ubuntu Linux -- Python packages and package manager -- The repository of Python packages for Windows -- Installing packages and required software -- OpenCV -- Windows -- Ubuntu Linux -- Installing NumPy -- Windows -- Ubuntu Linux -- Installing GDAL and OGR -- Windows -- Ubuntu Linux -- Installing Mapnik -- Windows -- Ubuntu Linux -- Installing Shapely -- Windows -- Ubuntu Linux -- Installing other packages directly from pip -- Windows -- Ubuntu Linux -- Installing an IDE -- Windows -- Linux -- Creating the book project -- Programming and running your first example -- Transforming the coordinate system and calculating the area of all countries -- Sort the countries by area size -- Summary -- Chapter 2: The Geocaching App -- Building the basic application structure -- Creating the application tree structure -- Functions and methods -- Documenting your code -- Creating the application entry point -- Downloading geocaching data -- Geocaching data sources -- Fetching information from a REST API -- Downloading data from a URL -- Downloading data manually -- Opening the file and getting its contents -- Preparing the content for analysis -- Combining functions into an application -- Setting your current location -- Finding the closest point -- Summary -- Chapter 3: Combining Multiple Data Sources -- Representing geographic data -- Representing geometries -- Making data homogeneous -- The concept of abstraction -- Abstracting the geocache point -- Abstracting geocaching data -- Importing geocaching data -- Reading GPX attributes -- Returning the homogeneous data -- Converting the data into Geocache objects -- Merging multiple sources of data.

Integrating new functionality into the application -- Summary -- Chapter 4: Improving the App Search Capabilities -- Working with polygons -- Knowing well-known text -- Using Shapely to handle geometries -- Importing polygons -- Getting the attributes' values -- Importing lines -- Converting the spatial reference system and units -- Geometry relationships -- Touches -- Crosses -- Contains -- Within -- Equals or almost equals -- Intersects -- Disjoint -- Filtering by attributes and relations -- Filtering by multiple attributes -- Chaining filters -- Integrating with the app -- Summary -- Chapter 5: Making Maps -- Knowing Mapnik -- Making a map with pure Python -- Making a map with a style sheet -- Creating utility functions to generate maps -- Changing the data source at runtime -- Automatically previewing the map -- Styling maps -- Map style -- Polygon style -- Line styles -- Text styles -- Adding layers to the map -- Point styles -- Using Python objects as a source of data -- Exporting geo objects -- Creating the Map Maker app -- Using PythonDatasource -- Using the app with filtering -- Summary -- Chapter 6: Working with Remote Sensing Images -- Understanding how images are represented -- Opening images with OpenCV -- Knowing numerical types -- Processing remote sensing images and data -- Mosaicking images -- Adjusting the values of the images -- Cropping an image -- Creating a shaded relief image -- Building an image processing pipeline -- Creating a RasterData class -- Summary -- Chapter 7: Extract Information from Raster Data -- Getting the basic statistics -- Preparing the data -- Printing simple information -- Formatting the output information -- Calculating quartiles, histograms, and other statistics -- Making statistics a lazy property -- Creating color classified images -- Choosing the right colors for a map -- Blending images.

Showing statistics with colors -- Using the histogram to colorize the image -- Summary -- Chapter 8: Data Miner App -- Measuring execution time -- Code profiling -- Storing information on a database -- Creating an Object Relational Mapping -- Preparing the environment -- Changing our models -- Customizing a manager -- Generating the tables and importing data -- Filtering the data -- Importing massive amount of data -- Optimizing database inserts -- Optimizing data parsing -- Importing OpenStreetMap points of interest -- Removing the test data -- Populating the database with real data -- Searching for data and crossing information -- Filtering using boundaries -- Summary -- Chapter 9: Processing Big Images -- Working with satellite images -- Getting Landsat 8 images -- Memory and images -- Processing images in chunks -- Using GDAL to open images -- Iterating through the whole image -- Creating image compositions -- True color compositions -- Processing specific regions -- False color compositions -- Summary -- Chapter 10: Parallel Processing -- Multiprocessing basics -- Block iteration -- Improving the image resolution -- Image resampling -- Pan sharpening -- Summary -- Index.

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