Python and Geography: Input Output Models and Graph Theory

Michalis Avraam — Wed, 20 May 2009 03:50:34 +0000

As may be evident from my other posts in the past, I have a tendency to use Python to solve a lot of the problems I face every day doing research in Geography. In that context, analyzing the 2002 Input Output Model for Washington State, I decided that Graph Theory needs to come into play (Economic Geograhy seems to need more Python(s) around).

In order to achieve this, I used the Excel Spreadsheet provided by the Washington State Office of Financial Management (OFM) and Python in the following way:

Using the excellent xlrd python module to read Excel data into Python
Create a multi-node directed graph from the data using NetworkX
With the amazing power of Python, calculate the Adjacency and Distance Matrices of the graph
Use xlwd to write back information in an Excel sheet

The results were stunning. The 50×50 matrix of the Washington I/O Model with 2,050 actual transactions takes a little more than a second to generate all the outputs given the Excel spreadsheet. Python to the rescue again, performing the work of geographers in a simple, fast way.

Here is a sample image, generating a directed grap (DiGraph) of the first 10 sectors reported in the Washington State Input Output Model for 2002 (the choice of 10 was to ensure a small, less complex graph so people can see the results better). All transactions are considered, with direction preserved.

The graph above is further analyzed using an adjacency matrix and a distance matrix, reported out as an Excel file.

I will be posting more results soon, and if anyone asks for it, I will clean up the script for release into the wild.

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The Essential Python Modules for GIS

Michalis Avraam » io model

Python and Geography: Input Output Models and Graph Theory