About the content
This course introduces concepts, algorithms, programming, theory and design of spatial computing technologies such as global positioning systems (GPS), Google Maps, location-based services and geographic information systems. Learn how to collect, analyze, and visualize your own spatial datasets while avoiding common pitfalls and building better location-aware technologies.
Module 1 - Introduction
Course IntroductionModule 2 - Spatial Query Languages
Defining Spatial Computing
Interviews with Johannes Schöning, Loren Terveen and Martin Raubal
What is a Query? Query Language?Module 3 - Spatial Networks
An example database with 3 tables
SELECT statement with 1 table
Multi-table SELECT statement
Why spatial extensions are needed
1-table spatial queries
Motivation, Societal use casesModule 4 - Spatial Data Mining
Example spatial networks
Conceptual and mathematical models
Need for SQL extensions
Storage and data structures
Algorithms for connectivity query
Algorithms for shortest path
Interviews with Dev Oliver and Betsy George
Motivation, Spatial Pattern FamiliesModule 5 - Volunteered Geographic Information (VGI)
Spatial data types and relationships
Limitations of Traditional Statistics
Location Prediction model
Colocations and Co-occurrences
Summary: What is special about mining spatial data?
Introduction to Volunteered Geographic InformationModule 6 - Positioning
Pros and Cons of VGI
Interview with Michael Goodchild
Introduction to PositioningModule 7 - Cartography
Overview of GPS
Overview of Wifi and Cellular Positioning
Introduction to Content-based Positioning
Introduction to CartographyModule 8 - Future Directions
Overview of Maps and Mapping
Spatial Databases: Representative projects
Data Mining: Representative projects
Advances in Cartography
Advances in Positioning
Interviews with Vipin Kumar, Wan Bae, Mohammed Mokbel and Len Kne
- Shashi Shekhar - Computer Science and Engineering
- Brent Hecht - Computer Science and Engineering
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