Geospatial Development By Example with Python电子书

Geospatial Development By Example with Python

Table of Contents

Geospatial Development By Example with Python

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

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

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

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

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

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

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

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

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

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

10. Parallel Processing

Multiprocessing basics

Block iteration

Improving the image resolution

Image resampling

Pan sharpening

Summary

Index