As spatial statistics are essential to the geographical inquiry, accessible and flexible software offering relevant functionalities is highly desired. Python Spatial Analysis Library (PySAL) represents an endeavor towards this end. It is an open-source python library and ecosystem hosting a wide array of spatial statistical and visualization methods. Since its first public release in 2010, PySAL has been applied to address various research questions, used as teaching materials for pedagogical purposes in regular classes and conference workshops serving a wide audience, and integrated into general GIS software such as ArcGIS and QGIS. This entry first gives an overview of the history and new development with PySAL. This is followed by a discussion of PySAL’s new hierarchical structure, and two different modes of accessing PySAL’s functionalities to perform various spatial statistical tasks, including exploratory spatial data analysis, spatial regression, and geovisualization. Next, a discussion is provided on how to find and utilize useful materials for studying and using spatial statistical functions from PySAL and how to get involved with the PySAL community as a user and prospective developer. The entry ends with a brief discussion of future development with PySAL.
Computer programming and development are critical to the past, present, and future of geospatial systems and techniques. The increasing ubiquity and diversity of online, mobile, and desktop GIS platforms along with the inclusion of cyber-infrastructure components within the bounds of geographic information systems (e.g., supercomputing, wireless sensor networks) means that GIS researchers and professionals need to be fluent in multiple forms of programming, and the life-cycles of system and software development.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.
|Algorithm Design/Algorithmic Approaches||Programming Languages & Libraries|
|Real-time GIS Programming and Geocomputation||Python for GIS|
|Natural Language Processing in GIScience Applications||PySAL and Spatial Statistics Libraries|
|Machine Learning Programming for GIS||R for Geospatial Analysis & Mapping|
|GIS and Parallel Programming||SQL Languages for GIS|
|Object-oriented Programming in GIS Applications||GDAL/OGR and IO Libraries|
|Development Tools||Design, Development, Testing, and Deployment of GIS Applications|
|Visual Programming for GIS Applications||Verification & Validation of GIS Applications|
|SpatialMPI: Message Passage Interface for GIS Applications||Commercialization of GIS Applications|
|GIS APIs||Licensing of GIS Applications|
|Open Source Software Development|
|Platform Specific Programming|
|GIS and GPU Programming|
|Programming of Mobile GIS Applications|
|Web GIS Programming|