CSCI 5715: Csci 5715

Credits: 3
Class Time and Place: 09:45 P.M. - 11:00 A.M., Tu and Th KHKH 3-111.
Class Webpage: http://www.spatial.cs.umn.edu/Courses/Fall17/5715/
Moodle Page: https://ay17.moodle.umn.edu/course/view.php?id=5277
Pre-requisites: Course work in Computer Science or Geographic Information Science.
Text: Spatial Databases: A Tour, S. Shekhar and S. Chawla, Prentice Hall, 2003, ISBN 013-017480-7 .
Supplementary Material: Encyclopedia of GIS and selected articles.
Links: Schedule, Homeworks , TA Announcements, Instructor Announcements, Teams

Motivation: Spatial Computing is a set of ideas and technologies to address major limitations of traditional computing models in context of societally important geographic problems. The transformational potential of Spatial Computing is already evident. From Uber and Google Maps to consumer GPS devices, our society has benefitted immensely from spatial technology. We've reached the point where a hiker in Yellowstone, a schoolgirl in DC, a drone over a Minnesota farm, and a Uber driver in Manhattan know precisely where they are, nearby points of interest, and how to reach their destinations. Groups of friends can form impromptu events via "check-in" models used by Facebook and foursquare. Scientists use GPS to track endangered species to better understand behavior and farmers use GPS for precision agriculture to increase crop yields while reducing costs. Google Earth is being used in classrooms to teach children about their neighborhoods and the world in a fun and interactive way. Augmented reality applications (e.g., Pokemon Go, range lines on images from car-backup-camera) are providing real-time place labeling in the physical world and providing people detailed information about major landmarks nearby.

Topics: This course introduces the fundamental ideas underlying the geo-spatial services, systems, and sciences. These include spatial concepts and data models, spatial query languages, spatial storage and indexing, query processing and optimization, spatial networks, spatial data mining, GPS and positioning, GIS and geo-visualization, geodesy, virtual globe and trends in spatial computing. We will also explore spatial ideas and questions in the other computing areas. One may use Pre-course survey to identify a few new ideas to be learned in this course

Note: UCGIS GIST Body of Knowledge domains of "Analytical Methods", "Data Modeling", "Conceptual Foundations", "Cartography and Visualization", "Geospatial Data" and "GIS&T and Society" are explored including the subdomains of AM2: Query Operations and query languages, AM3: Geometric Measures, AM7: Spatial Statistics, AM10: Data Mining, AM11: Network Analysis, DM1: Basic storage and retrieval structures, DM2: Database Management Systems,and, DM4: Vector and object data models, CF4: Elements of geographic information, CF5: Relationships, CF6: Imperfection in geographic information, CV1: History and trends, CV3: Principles of map design, CV4: Graphic representation techniques, CV5: Map production, CV6: Map use and evaluation, GD3: Georeferencing systems, GD4: Datums, GD5: Map projections, GS1: Legal aspects.

Required Work: Course has a set of four assignments and two examinations. The weighting scheme used for grading is: Exam 1 - 30%, Exam 2 - 30%, Assignments - 30%, and Class participation - 10%. Examinations will emphasize problem solving and critical thinking. Assignments will include pen-and-paper problems and computer based laboratory experiments/projects to reinforce concepts uncovered in the classroom. Class participation includes short presentation and active group learning. Participants will take turn to review an article from encyclopedia of GIS or current spatial news and present selected news items in the class. During active learning, participants will work in small groups on exercises provided in the class meeting. After this, a randomly chosen group will be invited to summarize the discussion in his/her group. Other groups in the class may critique constructively. Assignments and class-presentation will be completed in teams of two students.

Late Submission Policy: Assignments must be handed in at the beginning of the class on the specified due date (always a Tuesday). Late homeworks should be submitted to KHKH 5-202. DO NOT submit assignments in KHKH 5-203. A penalty of 30% will be deducted from score for the first 24-hour period your assignment is late. A penalty of 70% will be deducted from score beyond a 24-hour period.

Honor Code: Getting help from services like general debugging service (GDS), web-sites (e.g. cheaters.com), copying someone else's assignment, or the common solution of written or programming assignments will be considered cheating. Interaction for the purpose of understanding a problem is not considered cheating and will be encouraged. However, the actual solution to problems must be team's own. Each homework should include a description of contribution (e.g., solution to a problem, review of solution) by each team member.

Career Opportunities: Major computer science employers looking for geospatial knowledge and skills include ESRI, Facebook, Google, Apple, Uber, IBM, Microsoft, Oracle, and many government agencies related to public health, public safety, transportation, etc. As per a recent article in the Nature magazine “ the US Department of Labor identified geotechnology as one of the three most important emerging and evolving fields, along with nanotechnology and biotechnology. Job opportunities are growing and diversifying as geospatial technologies prove their value in ever more areas. ”

Auxiliary Information: Representing geo-spatial information services include virtual globes (e.g. Google Earth, Google Earth Engine, Bing Maps , NASA World Wind , MapServer, Timelapse), location based services (e.g. Uber Ride with Uber, Apple iPhone location services, Google Android location and maps, Foursquare, MapQuest, Pokemon Go), enterprise consulting (e.g. IBM smarter planet). Representative application programming interfaces include HTML 5 Geolocation API , Google Maps API , Bing Maps API , Flickr Flickr Maps , Twitter location API.

Geo-spatial systems include GIS (e.g. Open Source GIS , ESRI ArcGIS family , ), Database Management Systems (e.g. Oracle Spatial & Locator , IBM Spatial Offerings , MS SQL Server Spatial , PostGres PostGIS ), spatial data mining platforms (e.g. SatScan , R , Splus , Crimestat), and standards opengeospatial.org , ISO TC 211 etc.

Geo-spatial information science 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. Web-based resources include Encyclopedia of GIS 2nd Edition , 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. This course will also explore these concepts particularly in context of the gap between traditional Computer Science (CS) paradigms and the computational needs of spatial domains. We will examine current approaches to address these new challenges possibly via talks from prominent geospatial thinkers at our university.