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Home > Programming > Geospatial Analysis > Geospatial Development By Example with Python
Geospatial Development By Example with Python
Geospatial Development By Example with Python

Geospatial Development By Example with Python: Build your first interactive map and build location-aware applications using cutting-edge examples in Python

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Profile Icon Pablo Carreira
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eBook Jan 2016 340 pages 1st Edition
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eBook Jan 2016 340 pages 1st Edition
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Geospatial Development By Example with Python

Chapter 2. The Geocaching App

In this chapter, we will build a geocaching app that will initially get geocache points from the Internet and return the coordinates and information on the point closest to a user's location.

We will go through some of the most important steps in every geoprocessing application: we will discuss opening files, reading information, preparing data for analysis, and performing calculations with each object in your data. To achieve this, you will learn how to organize your code with Python and use the resources provided by the language to write consistent applications.

In this chapter, we will start to make use of classes, methods, functions, decorators, and exception handling, which will help us build an application with reusable components and clean code. Don't worry if these terms are new to you; they will be explained in the examples. In short, here's what we will cover:

  • Programming the basic application structures
  • Downloading geocaching...

Building the basic application structure

There are two main reasons to define a good basic structure for our application:

  • It keeps our code organized
  • It allows us to reuse pieces of code in later applications

Python is a flexible language in terms of code organization, and although users are allowed to write the whole application in a single file, it's preferable to separate the functionalities into modules and packages.

Modules are Python files that contain classes and functions that can be imported into another file with the import statement. Packages are special directories (folders) that contain modules. This leads to organized and well-structured code that is less prone to having bugs and is easier to maintain.

The proposed structure is to have a folder for each chapter. Inside it, we can create packages or files for each application; we will create a package for a common utility code that can be imported and reused and a directory to perform experiments.

Creating the application tree...

Downloading geocaching data

We now have the basic application structure with an entry point; next, we will start writing modules that execute the tasks that the application needs to produce the desired results.

The first thing that we need is to obtain some geocaching data from the Internet, and we want our application to do this for us. There are two common ways of doing this, and they are not restricted only to geocaching data. Many geographical data repositories can be accessed by these methods:

  • Direct download: This is a download similar to what you do in a browser. There is a link, a request is made to this link, and the download starts.
  • REST API: Many services offer this kind of data access. REST (Representational State Transfer) is a way of serving data where a client makes requests with a series of constraints, and the server responds with the result. It's particularly useful because it allows the user to customize the data of interest.

Geocaching data sources

There are many sources...

Opening the file and getting its contents

Now, we will open the downloaded file and prepare it for processing. This is something that we already did in Chapter 1, Preparing the Work Environment, so we will copy our function and improve it so that we can reuse it in this application and the ones to come. Here are the steps that we will perform:

  1. Create a new file named geo_functions.py inside the utils directory.
  2. Open the world_areas.py file from Chapter 1, Preparing the Work Environment, and copy the open_shapefile function. Then, paste it into the created file.
  3. Now, change the name of the function to open_vector_file so that it makes more sense as we will use this function to open many kinds of file. The geocaching file isn't a shapefile—it's a GPX, and to open it, we don't need to change anything. OGR will handle this for us.
  4. Now, to keep the code well documented, change the docstring to reflect the function's capabilities. Change it to something similar to &quot...

Combining functions into an application

So far, we looked at very useful utility functions that perform specific tasks; however, to form an application, we need to combine these functions by calling them in an ordered manner to achieve our objectives. We need code that orchestrates the calls and results—one that will make the application run.

For this, we will dive into one of the most beautiful and powerful parts of Python programming: classes and methods.

Python is an object-oriented programming language (but it is not strict). If you are not familiar with the concept of object-oriented programming, don't worry; the best way to understand what this is about is by examples, so I won't go into theories now but teach by example instead. Perform the following steps now:

  1. Remember the application's entry point? It's in the Chapter2 folder, in the geochaching_app.py file. Open it for editing, and you should have this:
    # coding=utf-8    

def main():print "Hello geocaching...

Setting your current location

So far, the application can open a file. The next step is to define your location so that we can find the closest geocache. To do this, we will change the GeocachingApp class so that it can keep track of the current location through a property. We will also create methods to change the location (similar to the geometries), transform its coordinates, and prepare it for processing.

Here are the steps that need to be performed:

  1. Edit the GeocachingApp class init method using the following code:
    #..
    def __init__(self, data_file=None, my_location=None):
        """Application class.

        :param data_file: An OGR compatible file
         with geocaching points.
        """
        self._datasource = None
        self._transformed_geoms = None
        self._my_location = None
        self.distances = None

        if data_file:
            self.open_file(data_file)

        if my_location:
            self.my_location = my_location...

Building the basic application structure

There are two main reasons to define a good basic structure for our application:

  • It keeps our code organized

  • It allows us to reuse pieces of code in later applications

Python is a flexible language in terms of code organization, and although users are allowed to write the whole application in a single file, it's preferable to separate the functionalities into modules and packages.

Modules are Python files that contain classes and functions that can be imported into another file with the import statement. Packages are special directories (folders) that contain modules. This leads to organized and well-structured code that is less prone to having bugs and is easier to maintain.

The proposed structure is to have a folder for each chapter. Inside it, we can create packages or files for each application; we will create a package for a common utility code that can be imported and reused and a directory to perform experiments.

Creating the application tree structure...

Downloading geocaching data

We now have the basic application structure with an entry point; next, we will start writing modules that execute the tasks that the application needs to produce the desired results.

The first thing that we need is to obtain some geocaching data from the Internet, and we want our application to do this for us. There are two common ways of doing this, and they are not restricted only to geocaching data. Many geographical data repositories can be accessed by these methods:

  • Direct download: This is a download similar to what you do in a browser. There is a link, a request is made to this link, and the download starts.

  • REST API: Many services offer this kind of data access. REST (Representational State Transfer) is a way of serving data where a client makes requests with a series of constraints, and the server responds with the result. It's particularly useful because it allows the user to customize the data of interest.

Geocaching data sources

There are many sources...

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Page 1 of 8

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Key benefits

  • • 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 processing

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.

Who is this book 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 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 30, 2016
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781785288340
Category :
Programming
Languages :
Python
Concepts :
Geospatial Analysis
Tools :
NumPy

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Publication date : Jan 30, 2016
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781785288340
Category :
Programming
Languages :
Python
Concepts :
Geospatial Analysis
Tools :
NumPy

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Table of Contents

11 Chapters
1. Preparing the Work Environment Chevron down icon Chevron up icon
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. The Geocaching App Chevron down icon Chevron up icon
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. Combining Multiple Data Sources Chevron down icon Chevron up icon
3. Combining Multiple Data Sources
Representing geographic data
Making data homogeneous
Importing geocaching data
Integrating new functionality into the application
Summary
4. Improving the App Search Capabilities Chevron down icon Chevron up icon
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. Making Maps Chevron down icon Chevron up icon
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. Working with Remote Sensing Images Chevron down icon Chevron up icon
6. Working with Remote Sensing Images
Understanding how images are represented
Processing remote sensing images and data
Building an image processing pipeline
Summary
7. Extract Information from Raster Data Chevron down icon Chevron up icon
7. Extract Information from Raster Data
Getting the basic statistics
Creating color classified images
Blending images
Showing statistics with colors
Summary
8. Data Miner App Chevron down icon Chevron up icon
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. Processing Big Images Chevron down icon Chevron up icon
9. Processing Big Images
Working with satellite images
Memory and images
Processing images in chunks
Creating image compositions
Summary
10. Parallel Processing Chevron down icon Chevron up icon
10. Parallel Processing
Multiprocessing basics
Block iteration
Improving the image resolution
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(4 Ratings)
5 star 100%
4 star 0%
3 star 0%
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1 star 0%
Winston Apr 04, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book!!! This book takes the reader down a well structured path using Python to develop geospatial app. First the author has the reader download and install various Python packages like NumPy and Mapnik combined with multiple datasources to create fully-functional apps. Must read.
Amazon Verified review Amazon
ruben Apr 06, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is an excellent book for programming with Python very interesting book for users who want to get involve in the languange programming.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.
Amazon Verified review Amazon
Bill Jones Apr 08, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Awesome book, well written, and structured nicely. I'm new to Python but in that regard I didn't care if it was 3 or 2.7 until I started to want to use other modules or play around with some libraries. So one of the reviews did mention that but I didn't have any issues with the content in this book. I would highly recommend this book if you want to do some Geospatial Development with Python.
Amazon Verified review Amazon
Tim Crothers Apr 07, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really enjoyed this book. Prior I had a lot of experience with Python but only limited experience with various geospatial packages. By the end of the book I had a solid foundation for how to use several libraries I was unfamiliar with and how to apply them to various uses related to geospatial applications. Be aware the book is written in Python 2.7 so if you are a Python 3 developer you'll need to translate and many of the libraries aren't available yet in Python 3 so the value might be a bit less
Amazon Verified review Amazon

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About the author

Profile icon Pablo Carreira
Pablo Carreira
Pablo Carreira is a Python programmer and a full stack developer living in So 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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