This course introduces fundamental ideas underlying geo-spatial science, systems and services. These include spatial concepts and data models (e.g, field vs object based), spatial query languages, fundamental spatial algorithms (e.g., space filling curves, vornoi diagrams, etc.), spatial storage and indexing (e.g., Grid files, Quadtrees and R-trees), query processing (e.g, join strategies) and optimization, spatial networks (conceptual, logical and physical level design issues), spatial data mining (classification, association and clustering). Some future research trends in spatial computing would also be covered.
Required Work: This course would have four homeworks, two exams and a quiz. Homeworks may have both written and programming components. Programming component of the homework would include questions requiring SQL or a high-level language. Following is the distribution of weightage of these deliverables:
Group formation policies All homeworks should be done in a team. Following policies would be enforced with regards to the team configuration.
Makeup Exam or Quiz Policy: Makeup exams or quizzes will not be offered except in case of critical travel (e.g., paper presentation at a conference) that cannot be reschedules or medical emergency as documented by a doctor. Makeup exams or quizes would given only before the scheduled day of exam or quiz for the entire class. For example, if a quiz (or an exam) is scheuled on date X (which you cannot attend), then a makeup quiz (or an exam) would be given only before the date X. Post scheduled-day makeups would be given only in case of documented medical emergencies.
Late submission policy on homeworks: All homeworks must be submitted within the specified deadline. However, we understand that it may not always be possible on part of students to do so. In order to accomodate this, we would allow at grace period of 24 hours where the students are allowed to submit with a score reduction of 30%. Within 24 hours and 48 hours, there would be a score reduction of 60% byond which the submission would not receive any score.
Policies from TAs:
Note: Academic dishonesty polcies of IIIT Delhi apply. Visit this link for more information.
Auxiliary Information: Representing spatial information services include virtual globes (e.g. Google Earth, Bing Maps , World Wind ), location based services (e.g. Apple iPhone location services, Google Android location and maps, Location-based services , foursquare, mapquest ), enterprise consulting (e.g. IBM smarter planet). Representative application programming interfaces include HTML 5 Geolocation API , Google Maps API , Bing Maps API , Flickr location API , Twitter location API
Spatial computing systems include Geographic Information Systems (e.g. Open Source GRASS GIS , ESRI ArcGIS family , ), Database Management Systems (e.g. PostgreSQL PostGIS , Oracle Spatial & Graph , IBM DB2 Spatial Extender , MS SQL Server Spatial ), Spatial data mining platforms (e.g. R , and standards opengeospatial.org , ISO TC 211 etc.
Spatial computing includes relevant branches of computer sciences (e.g. spatial databases, spatial data mining, computational geometry, computational cartography), mathematics (e.g. topology, geometry, graph theory, spatial statistics), physical sciences (e.g. geodesy and geoPhysics), and social sciences (e.g spatial cognition), etc.
Resources research literature include Encyclopedia of GIS , Proceedings of the ACM SIG-Spatial Conf. on GIS , Proceedings of the Intl. Symposium on Spatial and Temporal Databases , IEEE Transactions on Knowledge and Data Eng. , and GeoInformatica: An International Journal on Advances in Computer Science for GIS.
Non-intuitive geo-spatial concepts include map projections , scale , auto-correlation , heterogeneity and non-stationarity etc. First two impact computation of spatial distance, area, direction, shortest paths etc. Spatial (and temporal) autocorrelation violates the omni-present independence assumption in traditional statistical and data mining methods. Non-stationarity violates assumptions underlying dynamic programming, a popular algorithm design paradigm in Computer Science.