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