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Geospatial Development By Example with Python

By : Pablo Carreira

5 (4)

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.

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. 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. 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. 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. 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. 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. 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. Extract Information from Raster Data

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

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

9. Processing Big Images

Working with satellite images

Memory and images

Processing images in chunks

Creating image compositions

Summary

10. Parallel Processing

10. Parallel Processing

Multiprocessing basics

Block iteration

Improving the image resolution

Summary

Index

Index

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Searching for data and crossing information

Now that we have our database populated with some data, it's time to get some information from it; let's explore what kind of information all those POIs hold. We know that we downloaded points that contain at least one of the amenity or store keys.

Amenities are described by OSM as any type of community facilities. As an exercise, let's see a list of amenity types that we got from the points:

Edit your geodata_app.py file's if __name__ == '__main__': block:
```
if __name__ == '__main__':
    amenity_values = Tag.objects.filter(
        key='amenity').distinct('value').values_list('value')
    for item in amenity_values:
        print(item[0])
```
Here we take the Tag model, access its manager (objects), then filter the tags whose key='amenity'. Then we separate only distinct values (exclude repeated values from the query). The final part—values_list('value')—tells Django that we don't want it to create Tag models, we only want a list of values.
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