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After a tropical hurricane, what plants recover, and in what locations? How do cities and neighborhoods vary in their use of energy? Are registered sex offenders mobile, and if so, where do they move?
This diverse set of questions is only a small sample of the large variety that have been analyzed with support from an analytical toolbox known as Python Spatial Analysis Library (PySAL).
“We’re proud to announce that the newest release is available for download,” said Serge Rey, who directs the PySAL project within ASU’s Center for Geospatial Information Science (CGIS).
With the growing availability of geospatial data sources — whether from satellite imagery, GPS or a myriad of other sources — the world has also seen an increase in web-based visualization and mapping.
“While visualization is an inherently powerful lens, there is a critical need to couple these fascinating views with spatial statistics, spatial econometrics and geocomputational algorithms in order to fully examine and understand our world," said Rey. “PySAL is designed to provide this capability.”
PySAL can be thought of as a "library" because it consists of chunks of code that developers can adapt or combine as needed, depending on the analytical need. Built using the Python scripting language, it’s “open source,” meaning that its code is available for anyone to inspect, modify and enhance.
Given the scope and flexibility of the functions offered by PySAL, it has been deployed on a wide array of platforms, from the world's leading super computers, down to handheld devices for mobile and field-based data analysis. Since its initial release in 2010 it has been downloaded more than 100,000 times.
PySAL’s core development team consists of leading GIScientists from across the world as well as ASU graduate and undergraduate students.
Two major enhancements in the newest version of PySAL were developed by doctoral candidates in ASU’s School of Geographical Sciences and Urban Planning, Levi Wolf and Taylor Oshan, with both projects funded by Google’s Summer of Code. Wolf’s initiative got to the core of how PySAL works with data, making it easier for developers to integrate PySAL into their own Python-based projects.
Oshan’s project added more powerful ways to analyze flows from an origin to a destination either over physical space (such as migration) or through abstract space (like information flows in telecommunication). The tools he has developed allow for more sophisticated analysis of the demand and costs associated with moving between locations, as well as the investigation of spatial patterns within flow data.
“Initiatives like PySAL make our school a leader in developing innovative tools for turning geographic data into information that can support better decision making,” said Trisalyn Nelson, director of the School of Geographical Sciences and Urban Planning, which houses CGIS. “Our faculty and graduate students are creating analytical tools that can be utilized by anyone curious about data on a map.”
For more information about PySAL, visit http://pysal.org.