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Integrating Open-Source Statistical Packages with ArcGIS

Explore spatial data analysis in ArcGIS, harness Python and R for enhanced analytics, collaborate motivation in GIS, and leverage PySAL library for advanced spatial techniques.

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Integrating Open-Source Statistical Packages with ArcGIS

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  1. 7-25-12 Integrating Open-Source Statistical Packages with ArcGIS Mark V. Janikas, Ph. D. Xing Kang

  2. Outline • Introduction to Spatial Data Analysis in ArcGIS • Spatial Statistics, Geostatistics and Spatial Analyst • Python: Directly and Indirectly Extendable • Collaborative Motivation • PySAL (Python Spatial Analysis Library) • Direct • R • Indirect • Conclusions and Future Directions

  3. Spatial Analytics in ArcGIS: Past and Present • Traditional Spatial Analysis • Core tools continue to evolve • Spatial Analyst • Raster • Map Algebra • Geostatistics • Raster and Vector • Continuous Data • Spatial Statistics • Vector • Exhaustive Data • Python

  4. Spatial Analytics in ArcGIS: Moving Forward • Python • Spatial Analyst • Raster NumPy • SciPy • Spatial Statistics and Geostatistics • Data Access Module • Vector NumPy • Spatial Statistics Data Object and Utilities • matplotlib

  5. The Great and Extendable Python • Direct • Numeric/Scientific Python Modules • http://wiki.python.org/moin/NumericAndScientific • +50 Modules Listed • Check Compatibility… Then Plug and Play • Indirect • Alternative Languages • No Python Hooks or Module • Compatibility Fails • Python Serves as Active Script and OS • Out of Process

  6. Collaborative Motivation • ArcGIS Users • Substantive Interest • Methodological Choice • Quantitative State of Mind • Enhancement Requests • User Conference • Resource Center and Forums • Email… You know who you are… • What we build and why • What is left? • PySAL and R

  7. Collaborative Motivation: 2nd Side of the Coin • Value Added in the Other Direction • Direct or indirect linkage should focus on the strengths of each • A Simple (Humorous?) Example • The R module “Midas” • Method • Inputs: X, Y, Z Coordinates, Image Reading at Location • Output: Probability that Gold Exists at each location • I’m rich!... But what if “they” get there first? • ArcGIS • Network Analyst: How do I get there? Best route? • Spatial Statistics: Hot-Spots to identify an area to focus on • Geostatistics: Kriging to predict and unknown locations

  8. Introduction to PySAL • Open Source Python Library for Spatial Analytical Functions • ASU GeoDa Center for Geospatial Analysis and Computation • Luc Anselin • PySpace • Sergio Rey • STARS • BSD License

  9. Anatomy of PySAL

  10. Collaborative Advantages: PySAL and ArcGIS • PySAL • Advance Spatial Analysis code base with novel functions • E.g. Regionalization, Spatial Econometrics • Do not have to “reinvent the wheel” • ArcGIS • GIS User Interface • ~800 GP Tools • Easy-to-use Script Tool Framework • Enriched functionalities from ArcGIS Python scripts • arcpy, SSDataObject, SSUtilities, SSReport etc. • Multiple input/output data format • Error messages • Pyharness framework for robust testing

  11. Basic PySAL – ArcGIS Interaction Model NumPy Input Data SSDataObject SSUtilities Spatial Weights Environment Settings Projections Field Qualification Z/M Values Bad Records Error/Warning Messages Localization Feature Accounting PySAL Analytical Function Output Data NumPy

  12. PySAL – ArcGIS Toolbox Demonstration:Regional Income Distributions

  13. What is R? Why should I use it? • R (The R Project for Statistical Computing) • Over 60 CRAN sites across 30+ countries • Its Free • GNU GENERAL PUBLIC LICENSE • Base is powerful • Statistics, Linear Algebra, Visualization , etc… • Its extendible • 1800+ Contributed Extensions • splancs, spatstat, spdep, rgdal, maptools, shapefiles

  14. Indirect Integration Strategy • Python and R: “Decoupled” • Used as the core script tool • Hooks into the Operating System to call R • Post-Processor • “Out of Process” • RPy/RPy2 • Compatibility • win32com • Windows only • Works for other programs as well

  15. Basic R – ArcGIS Interaction Model Input Params ArcGIS Python Retrieves Parameters Organizes into R command Executes R command Post-Processing Apply Symbology Apply Projections Report R Enhanced Output Data Output Data

  16. R – ArcGIS Toolbox Demonstration:Regional Income Distributions

  17. Conclusions • PySAL • Advanced spatial analytic techniques • Combined with SSDataObject and Utilities • Directly compatible • Python Harness Implementation • Spatial Econometrics and Spatial Weights Conversion • ESDA, Clustering, Spatial Dynamics etc. • BSD

  18. Conclusions • R • Contains “cutting edge” data analysis techniques from a wide body of academic and applied fields • Extendible • Indirectly compatible • Direct via RPy/RPy2 and win32com • GNU • Revolution • esri continues to focus on improving the interaction in the future

  19. Software Links • PySAL • https://geodacenter.asu.edu/pysal • http://code.google.com/p/pysal/ • NumPy and SciPy • http://www.numpy.org/ • R • http://www.r-project.org/index.html

  20. http://www.arcgis.com/home/group.html?owner=ArcGISTeamAnalysis&title=Analysis%20and%20Geoprocessing%20Tool%20Galleryhttp://www.arcgis.com/home/group.html?owner=ArcGISTeamAnalysis&title=Analysis%20and%20Geoprocessing%20Tool%20Gallery • Using R in ArcGIS (Version 10) • R Point Clustering Tools for ArcGIS (Version 9.3) • Using R in ArcGIS (Version 11) – Coming Soon! • Using PySAL in ArcGIS (Version 11) • Either here or at the GeoDa Center

  21. Related Sessions • Search the Online Agenda • http://events.esri.com/uc/2012/infoWeb/OnlineAgenda/ • Keywords • Python • Spatial Statistics • Geostatistics • Spatial Analyst • Business Analyst

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