Geospatial Development By Example with Python

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

Geospatial Development By Example with Python

This ebook is included in a Mapt subscription
Pablo Carreira

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

ISBN 139781785282355
Paperback340 pages

Book Description

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.

Table of Contents

Chapter 1: Preparing the Work Environment
Installing Python
Python packages and package manager
Installing packages and required software
Installing NumPy
Installing GDAL and OGR
Installing Mapnik
Installing Shapely
Installing other packages directly from pip
Installing an IDE
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
Downloading geocaching data
Opening the file and getting its contents
Combining functions into an application
Setting your current location
Finding the closest point
Summary
Chapter 3: Combining Multiple Data Sources
Representing geographic data
Making data homogeneous
Importing geocaching data
Integrating new functionality into the application
Summary
Chapter 4: Improving the App Search Capabilities
Working with polygons
Using Shapely to handle geometries
Importing polygons
Getting the attributes' values
Importing lines
Converting the spatial reference system and units
Geometry relationships
Filtering by attributes and relations
Filtering by multiple attributes
Integrating with the app
Summary
Chapter 5: Making Maps
Knowing Mapnik
Creating utility functions to generate maps
Styling maps
Using Python objects as a source of data
Exporting geo objects
Creating the Map Maker app
Summary
Chapter 6: Working with Remote Sensing Images
Understanding how images are represented
Processing remote sensing images and data
Building an image processing pipeline
Summary
Chapter 7: Extract Information from Raster Data
Getting the basic statistics
Creating color classified images
Blending images
Showing statistics with colors
Summary
Chapter 8: Data Miner App
Measuring execution time
Code profiling
Storing information on a database
Importing massive amount of data
Searching for data and crossing information
Summary
Chapter 9: Processing Big Images
Working with satellite images
Memory and images
Processing images in chunks
Creating image compositions
Summary
Chapter 10: Parallel Processing
Multiprocessing basics
Block iteration
Improving the image resolution
Summary

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

Authors

Table of Contents

Chapter 1: Preparing the Work Environment
Installing Python
Python packages and package manager
Installing packages and required software
Installing NumPy
Installing GDAL and OGR
Installing Mapnik
Installing Shapely
Installing other packages directly from pip
Installing an IDE
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
Downloading geocaching data
Opening the file and getting its contents
Combining functions into an application
Setting your current location
Finding the closest point
Summary
Chapter 3: Combining Multiple Data Sources
Representing geographic data
Making data homogeneous
Importing geocaching data
Integrating new functionality into the application
Summary
Chapter 4: Improving the App Search Capabilities
Working with polygons
Using Shapely to handle geometries
Importing polygons
Getting the attributes' values
Importing lines
Converting the spatial reference system and units
Geometry relationships
Filtering by attributes and relations
Filtering by multiple attributes
Integrating with the app
Summary
Chapter 5: Making Maps
Knowing Mapnik
Creating utility functions to generate maps
Styling maps
Using Python objects as a source of data
Exporting geo objects
Creating the Map Maker app
Summary
Chapter 6: Working with Remote Sensing Images
Understanding how images are represented
Processing remote sensing images and data
Building an image processing pipeline
Summary
Chapter 7: Extract Information from Raster Data
Getting the basic statistics
Creating color classified images
Blending images
Showing statistics with colors
Summary
Chapter 8: Data Miner App
Measuring execution time
Code profiling
Storing information on a database
Importing massive amount of data
Searching for data and crossing information
Summary
Chapter 9: Processing Big Images
Working with satellite images
Memory and images
Processing images in chunks
Creating image compositions
Summary
Chapter 10: Parallel Processing
Multiprocessing basics
Block iteration
Improving the image resolution
Summary

Book Details

ISBN 139781785282355
Paperback340 pages
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