CS 350: Algorithms for Geographic Information Systems

Spring 2009

T, Th 2:30 - 3:55 in Searles 224

Geographic Information Systems (GIS) are (computer) systems for storing, displaying and analyzing geographically referenced, or geospatial data. GIS started many years ago with primitive systems of visualizing maps. In the past twenty years, with the growth of computer technology and the explosion of digital geographic data, GIS has seen a tremendous growth and has established as a field of its own. GIS has not only changed the way people approach research in earth-related sciences (and others), but has become part of our every day life: It is normal to check maps online and get driving directions, or travel virtually with GoogleEarth.

For the disciplines that use GIS, the study of GIS usually starts with the study of the functionality provided by the standard GIS packages to manage and analyze data and model specific problems. For example, a city planner may want to find the best location to host an ugly construction. How can this problem be modeled with a GIS; What data is available; How can all the layers of data be integrated; Is there a way to model and query the impact of a site.

This class gives a computing perspective of GIS. For Computer Scientists, GIS is a rich source of problems, spanning from theory, to data bases, networks and systems and algorithm engineering. The class introduces the basic problems encountered in designing GIS: data models, data representation and basic algorithms.

The class was developed with the support of NSF award no. 0728780.

Prerequisites: csci210 and {csci189 or math200} (or permission of instructor). Note: the class will move based on the background of the students.

Instructor: Laura Toma
Office: 219 Searles

Office hours: T, Th after class. For quick questions, any time I am in the office. Or, send me an email to setup a (different) time.

Class Email: csci350 at bowdoin.edu

Class webpage: http://www.bowdoin.edu/~ltoma/teaching/cs350/spring09

Course syllabus:

• Fundamental database concepts.
• Fundamental spatial concepts.
• Geographic data models (raster, vector).
• Data representation (raster compression, winged-edge, quad-edge).
• Intro to geometric algorithms.
• Finding the closest pair of points.
• Horizontal-vertical segment intersection.
• The sweep method: finding the intersections of a general set of segments.
• Polygon triangulation.
• Delaunay triangulation. Storing, accesssing and traversing a triangulation. Incremental construction.
• The closest-pair problem: Voronoi diagrams.
• Line simplification (Douglas-Peucker and Imai-Iri).
• Terrain simplification.
• Intro to meshing.
• Access methods (grid structures, quadtrees, Morton orderings).
• Terrain processing: flow modeling.
• Visibility.
• Very large datasets. Motivation. Scalability of algorithms to large datasets. Models of computation. The I/O-model.
• External memory algorithms. External memory sorting. External memory priority queue.

Good reference books:

• The C programming Language. Kernighan and Ritchie. Second edition.
• GIS: A computing perspective. Worboys and Dukham. Second edition.
• Algorithmic Foundations of geographic Information Systems.. Kreveld, Nievergelt, Roos, Widmayer Editors. LNCS tutorial.
• Computational geometry. M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkoph.
• Computational geometry in C. J. O'Rourke.
• Programming pearls. J. Bentley.

Grading policy

• Class discussions and presentations
• Programming projects