Book Image

Geospatial Development By Example with Python

By : Pablo Carreira
5 (1)
Book Image

Geospatial Development By Example with Python

5 (1)
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.

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Geospatial Development By Example with Python
Pablo Carreira
Jan, 2016 | 340 pages
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Table of Contents (17 chapters)
Geospatial Development By Example with Python
Geospatial Development By Example with Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Preparing the Work Environment
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
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
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
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
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
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
Extract Information from Raster Data
Getting the basic statistics
Creating color classified images
Blending images
Showing statistics with colors
Summary
8
Data Miner App
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
Processing Big Images
Working with satellite images
Memory and images
Processing images in chunks
Creating image compositions
Summary
10
Parallel Processing
Parallel Processing
Multiprocessing basics
Block iteration
Improving the image resolution
Summary
Index
Index

Importing geocaching data

In the previous chapter, we generalized our import function by adding the capability to import more types of data supported by OGR.

Now, we will improve it again, make it handle some errors, make it compatible with our objects, and add two new capabilities. We will also convert the data in order to produce uniform objects.

To achieve our goals, we will analyze what kind of information is stored in the files that we want to open. We will use OGR to inspect the files and return some information that may help us with the data conversion.

First, let's alter our open_vector_file function, allowing it to handle incorrect paths and filenames, which is a very a common error. Perform the following steps:

  1. Go to the utils folder and open the geo_functions.py file.

  2. Add the following import statements at the beginning of the file:

    # coding=utf-8

import ogr
import osr
import gdal
import os
from pprint import pprint

  3. Now, edit the open_vector_file function via the following code:

    def...
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