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Spatial Data Mining with PostGIS and R

Duration: 5 Days

Course Background

The purpose of this course is to explore the use of R's data analysis and machine learning modules in the exploration and mining of data held in a PostGIS database.

Course Prerequisites and Target Audience

Attendees are assumed to have a good working knowledge of PostGIS and a basic knowledge of R.

Course Outline

  • KDD - Knowledge Discovery in Databases
  • Spatial data mining - the Process
    • Visual interpretation and analysis
    • Attribute query and selection
    • Generalisation and classification
    • Detection of spatial and non-spatial association rules
    • Clustering analysis
    • Spatial regression
  • Integration of GIS and Spatial Data Mining
    • Visualisation through GIS
    • Discovering association rules and minimum confidence thresholds
    • Mining for multi-level associations
    • Detection of spatial and non-spatial association rules
  • Data mining via scripts
    • Survey of CRAN packages for analysis of geographic and geometric data
    • Survey of CRAN packages for general spatial statistics analysis
    • OSGeo projects and R
    • QGIS and R
    • QGIS manageR plugin
    • R with PostGIS via RpostgreSQL and via rgdal
    • PL/R - R procedural language for PostgreSQL
  • Making maps with R
    • R webmaps package
    • RgoogleMaps
    • PostGIS and GRASS
    • GRASS and R
    • Integrating PostGIS with GRASS and R
  • Spatial data mining with R - tools, strategies and techniques
    • Data exploration
    • Decision trees and random forest
    • Regression
    • Clustering
    • Outlier detection
    • Time-series analysis
    • Association rules
    • Combining social network analysis with spatial data analysis