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Geospatial Development By Example with Python

Geospatial Development By Example with Python

By : Pablo Carreira
5 (4)
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Geospatial Development By Example with Python

Geospatial Development By Example with Python

5 (4)
By: Pablo Carreira

Overview of this book

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 (12 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Preparing the Work Environment
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
2
2. The Geocaching App
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
3
3. Combining Multiple Data Sources
3. Combining Multiple Data Sources
Representing geographic data
Making data homogeneous
Importing geocaching data
Integrating new functionality into the application
Summary
4
4. Improving the App Search Capabilities
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
5
5. Making Maps
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
6
6. Working with Remote Sensing Images
6. Working with Remote Sensing Images
Understanding how images are represented
Processing remote sensing images and data
Building an image processing pipeline
Summary
7
7. Extract Information from Raster Data
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7. Extract Information from Raster Data
Getting the basic statistics
Creating color classified images
Blending images
Showing statistics with colors
Summary
8
8. Data Miner App
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
9
9. Processing Big Images
9. Processing Big Images
Working with satellite images
Memory and images
Processing images in chunks
Creating image compositions
Summary
10
10. Parallel Processing
10. Parallel Processing
Multiprocessing basics
Block iteration
Improving the image resolution
Summary
11
Index
Index

Getting the basic statistics

As we have previously seen, images or raster data are arrays containing numerical values representing a given real world space. So, they are by consequence statistical samples and they can be used in statistical analysis.

When we import the data, it is converted into NumPy arrays. These arrays are packed with methods for basic statistical calculations. In this topic, we are going to obtain the results from these calculations and save them in a file.

At the end of the previous chapter, we made an image processing pipeline by combining steps that can be saved on the disk. Here, we will follow the same pattern. The statistical computation will be added as another step. Maintaining the same kind of organization allows the user to generate statistics at any point on the processing pipeline. It will be possible to save statistics from all the substeps if needed.

Let's start by organizing our code:

  1. As we do at the beginning of every chapter, we will copy the code from the...

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