Spatial Data Mining with PostGIS and Python
Duration: 5 Days
Course Background
The purpose of this course is to explore the use of Python data analysis and machine learning packages, as well as the use of Python interfaces with R packages 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 Python as well as 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 - available pacages and tools
- Python scripting for R
- Python scripting for PostgreSQL
- Migrating geometry from PostGIS to GRASS GIS
- Python scripting for GRASS
- Python scripting and QGIS
- Basic python numeric and scientific modules - Numpy, Scipy
- Pandas - Python Data Analysis Library
- Panda extension to handle geographic data - GeoPandas
- PyBrain - Python Machine Learning Library
- Scikit-Learn - Python Machine Learning Library
- Mixing it up - Python + R + PostGIS
- Making maps with Python
- Shapely + Fiona + Mapnik
- pymaps - Google maps for Python
- PostGIS and GRASS
- GRASS and Python
- Integrating PostGIS with GRASS and Python
- Spatial data mining with Python - 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