Algorithms for GIS csci3225 - Bowdoin College
Transcript of Algorithms for GIS csci3225 - Bowdoin College
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Laura Toma
Bowdoin College
Algorithms for GIScsci3225
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Spatial analysis: the beginnings
1848, London, John Snow
Finding: nb. cholera deaths aer spatially clustered around the Broad St pump
Claim: Cholera is a water-transmitted disease
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GIS (Geographic Information Systems)
• Systems for storing, visualizing and analyzing geospatial data • Started in 1970s as an extension of traditional cartography • First use: Mapping and visualization
• Display different types of data, all on same location (layers), turn layers on and off, zoom in/out, etc
• Create beautiful, interactive maps • Combine data from many sources
ESRI maps
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Polygons (properties), lines (streets), points (trees) and raster images (air photo) are integrated into one map.
http://researchguides.library.syr.edu/c.php?g=258118&p=1723814
GIS: Vizualization
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• Spatial operations • e.g. What lies within 5 miles of a dump site? • e.g. What other crimes have occurred in this selected region? • e.g. What is the total length of the river network? • e.g. Find shortest routes, connectivity
GIS: Spatial analysis
GRASS: module of the day v.overlay: overlays two vector maps, offering clip, intersection, difference and union operators
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GIS: Terrain analysis
e.g. modeling flow
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GIS: Terrain analysis
e.g. modeling visibility
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GIS: Terrain analysis
e.g. modeling flooding
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• Access existing data collection (e.g. Landsat, Modis) • Visualization and analysis
GIS: Satellite imagery
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Real-time GIS
• Put real-time sensor data on interactive maps • Track dynamic assets such as vehicles, aircrafts and vessels
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GIS
• Used by a growing number of disciplines • earth, atmospheric and oceanographic sciences • environmental studies • digital humanities, …
• Also used by city planners, government, …
Explosion of digital data ==> GIS has seen tremendous growth
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GIS software
• ArcGIS• developed by ESRI • probably most comprehensive system • complex interface • available in Bowdoin labs ; IT/ES offer tutorials
• https://grass.osgeo.org/# Open source systems • e.g. GRASS GIS, QGIS
• Other proprietary modules, with specialized functions • e.g. LAStools
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e.g. GRASS screenshots
Raster map operations Vector map operations
LiDAR data Processing
3D Visualization
Cartography
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GIS and geospatial analytics
• Rich source of problems in CS • algorithms, databases, interfaces, visualization, cloud computing,
systems
• LOTS of geospatial data available • What can one do with it?
geospatial “data science”
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Descartes Maps
They build global composites using their cloud-based parallel computing infrastructure
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Logistics
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Class overview
• Explore selected GIS applications and the algorithms/data structures involved.
Dealing with large data• Parallel programming with OpenMP • cache- and I/O-efficiency • Space-filling curves
Some basic GIS topics• Data models (raster, vector, TIN) and representations • Shortest paths and least-cost path surfaces • Flow
• river network, watersheds, flooding, sea-level rise • Visibility
• viewshed, total viewshed, guarding, and approximation.
• Simplification • 2D (line simplification) and 3D (terrain
simplification). • Spatial data structures: B-trees and quadtrees. • LiDAR data
Visualization • programming with OpenGL
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Focus: the interplay between theory and practice and scalability to large data
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Class info
• Pre-requisites • 1101, 2101 (data structures) and 2200 (algorithms)
• CS curriculum • Satisfies the “projects” requirement • Satisfies the “theory” requirement
• No textbook • Papers, slides, and other online materials
• Discussion forum • Piazza
• TAs • Jason Nawrocki
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Work
• The work for the class consists of • programming assignments & project • research papers reviews and class discussions • presentations (such as project proposal, updates, and final presentation) • final project report, final project demo and presentation
• date in polaris: Dec 18, 2pm • class participation