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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

By : Diener
4.4 (5)
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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

4.4 (5)
By: Diener

Overview of this book

Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Table of Contents (15 chapters)
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12
A. Other Geospatial Python Libraries
13
B. Mapping Icon Libraries
14
Index

Installing pyshp, geojson, and pandas

These specific libraries are for specific formats that make our life easier and simpler than using GDAL for some projects. pyshp will work with shapefiles, geojson with GeoJSON, and pandas with all other textual data types in a structured manner.

pyshp is pure Python and is used to import and export shapefiles; you can find the source code for pyshp here at https://github.com/GeospatialPython/pyshp. The pyshp library's sole purpose is to work with shapefiles. GDAL will be used to do most of our data's in/out needs, but sometimes, a pure Python library is simpler when working with shapefiles.

geojson is the name of a Python library and also a format, making it a little confusing to understand. The GeoJSON format (http://geojson.org) is becoming ever more popular and to this extent, we use the Python geojson library to handle its creation. You will find it on Python Package Index (PyPI) if you search for geojson. As you would expect, this will help us create all the different geometry types supported in the GeoJSON specifications.

pandas (http://pandas.pydata.org) is a data analysis library that structures your data in a spreadsheet-like manner for further computations. Since our geospatial data comes from a broad set of sources and formats, such as CSV, pandas helps work with the data with minimal effort.

Getting ready

Enter your virtual environment using the following command:

$ workon pygeoan_cb

Your prompt should now look like this:

(pygeoan_cb)mdiener@mdiener-VirtualBox:~$

How to do it...

The three installations are as follows:

  1. Pyshp will first be installed by simply using pip as follows:
    $ pip install pyshp
    
  2. Next, the geojson library will be installed using pip:
    $ pip install geojson
    
  3. Finally, pip will install pandas:
    $ pip install pandas
    

To test your installation of pyshp, use the import shapefile type. The output should look like what is shown in the following output:

(pygeoan_cb)mdiener@mdiener-VirtualBox:~/venv$ python
Python 2.7.3 (default,  Feb 27 2014, 19:58:35)
[GCC 4.6.3] on linux2
Type “help”,  “copyright”, “credits”, or  “license” for more information.
>> import shapefile
>> import geojson
>> import pandas

Note

The import shapefile statement imports the pyshp library; unlike the other libraries, the import name is not the same as the installation name.

How it works...

As seen in the other modules, we've used the standard installation pip package to execute installations. There are no other dependencies to worry about, making for fast progress.

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