CSE 5ISC: Introduction to Spatial Computing

Instructor: Dr. Viswanath Gunturi
Contact: gunturi@iiitd.ac.in, B-402 Academic Block
Credits: 4
Class Time and Place: Tu and Fri: 2:30pm - 4:00pm :: Room num: C24
Class Web page: http://faculty.iiitd.ac.in/~gunturi/courses/mon15/cse5isc/
To Register: Contact academic affairs office
Pre-requisites: Basic knowledge of data structures (e.g., CSE102 Data Structures and Algorithms) and databases (e.g., CSE202 Fundamentals of Database Systems), basic understanding of mathematics and programming skills.
Post-Condition: By the end of the course, the student can expect to develop
  • Understanding of spatial data, and how it is represented, and analysed.
  • Ability to solve basic spatial analysis problems.
  • Some understanding of the current challenges and trends in spatial computing

    • Textbooks: GIS: A Computing Perspective , M. Worboys et al., CRC Press, 2004. ( amazon link ), Spatial Databases: A Tour, Shashi Shekhar et al., Prentice Hall, 2003. ( amazon link )
    Other resources MOOC course on Spatial Computing
    Important Links Schedule, Teams, Instructor Announcements, Readings

    Background: Spatial computing encompasses a set of ideas, solutions, technologies, and systems that transform our lives and society by creating a new understanding of locations; how we know, communicate, and visualize our relationship to locations; and how we navigate through those locations. Spatial Computing has had a transformative impact. Large organizations (e.g., logisitics companies) use spatial computing for site selection, asset tracking, facility management, navigation and logistics. Scientists use Gobal Positioning systems to track endangered species and better understand animal behavior, while farmers use these technologies for precision agriculture to increase crop yields and reduce costs. Government agencies also use spatial computing technologies for a wide range of applications which include transportation and urban planning, disease monitoring and epidemiology (e.g., public health), census blocks, weather modelling, mapping terrains etc.

    Topics: 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: For this course, the students would be expected to work on 3 homework assignments (11% each) (to be done in a team of 2 students), 2 exams (15% each) and a course project (37%). The course project should be done in groups of 2-3 (ideally). Course projects would be considered under following three tracks: (a) Literature review: Comprehensive literature review of a broad topic complete with gap analysis, open research challenges and possible new research avenues, (b) Research Problem: should contain a formal problem definition, description of challenges (should be computational in nature), limitations of related work, an approach and a preliminary evaluation, (c) Comparison: Choose a known problem and extensively compare 3-4 different known approaches to solve the problem. The comparison strategies should involve both analytical and experimental aspects. For this track, a characterization of the dominance zone of each of the known approaches would be expected.

    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.