CSCI 5715 -- New Course

Mon Apr 22 12:38:10 2013

Approvals Received:
Department
on 04-19-13
by Mary Freppert
(freppert@umn.edu)
Approvals Pending: College/Dean  > Provost > Catalog
Effective Status: Active
Effective Term: 1143 - Spring 2014
Course: CSCI 5715
Institution:
Campus:		 UMNTC - Twin Cities
UMNTC - Twin Cities
Career: UGRD
College: TIOT - College of Science and Engineering
Department: 11108 - Computer Science & Eng
General
Course Title Short: Spatial Computing
Course Title Long: From GPS and Virtual Globes to Spatial Computing
Max-Min Credits
for Course:		 3.0 to 3.0 credit(s)
Catalog
Description:		 This course introduces mathematical concepts (e.g., topology), geo-information, representations (e.g., tessellation), algorithms (e.g., Euclidean, graph based), data-structures and access methods (e.g., R-tree), analysis (e.g., spatial data mining), architectures, interfaces (e.g., Geo-visualization), reasoning, and time (e.g., processes).
Print in Catalog?: Yes
CCE Catalog
Description:		 <no text provided>
Grading Basis: Stdnt Opt
Topics Course: No
Honors Course: No
Online Course: No
Instructor
Contact Hours:		 3.0 hours per week
Years most
frequently offered:		 Odd years only
Term(s) most
frequently offered:		 Spring
Component 1: LEC (with final exam)
Auto-Enroll
Course:		 No
Graded
Component:		 LEC
Academic
Progress Units:		 Not allowed to bypass limits.
3.0 credit(s)
Financial Aid
Progress Units:		 Not allowed to bypass limits.
3.0 credit(s)
Repetition of
Course:		 Repetition not allowed.
Course
Prerequisites
for Catalog:		 Familiarity with Java, C++, or Python.
Course
Equivalency:		 No course equivalencies
Consent
Requirement:		 No required consent
Enforced
Prerequisites:
(course-based or
non-course-based)		 001186 - Exclude fr or soph 5000 level courses
Editor Comments: <no text provided>
Proposal Changes: <no text provided>
History Information: <no text provided>
Faculty
Sponsor Name:		 Shashi Shekhar
Faculty
Sponsor E-mail Address:		 shekhar@cs.umn.edu
Student Learning Outcomes
Student Learning Outcomes: * Student in the course:

- Can identify, define, and solve problems

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

Spatial computing introduces new challenges to familiar computer science ideas. This course will use labs, homework assignments, and exams to ask the students to solve various computer science problems with a spatial twist. For example, students will need to identify which spatial concepts (e.g., auto-correlation) might be useful for a specific problem, construct a solution, design appropriate algorithms that incorporate spatial components, and verify the correctness of the solution.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

This SLO will be assessed through labs, homework, quizzes, in-class exercises, and a semester project. Each of these types of students' work is problem-based; solving open-ended spatial computing problems where the students must first identify, define, and/or clarify what the problem is before solving it, and explain the solution they propose.

Liberal Education
Requirement
this course fulfills:		 None
Other requirement
this course fulfills:		 None
Criteria for
Core Courses:		 Describe how the course meets the specific bullet points for the proposed core requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or method.

Core courses must meet the following requirements:

  • They explicitly help students understand what liberal education is, how the content and the substance of this course enhance a liberal education, and what this means for them as students and as citizens.
  • They employ teaching and learning strategies that engage students with doing the work of the field, not just reading about it.
  • They include small group experiences (such as discussion sections or labs) and use writing as appropriate to the discipline to help students learn and reflect on their learning.
  • They do not (except in rare and clearly justified cases) have prerequisites beyond the University's entrance requirements.
  • They are offered on a regular schedule.
  • They are taught by regular faculty or under exceptional circumstances by instructors on continuing appointments. Departments proposing instructors other than regular faculty must provide documentation of how such instructors will be trained and supervised to ensure consistency and continuity in courses.

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Criteria for
Theme Courses:		 Describe how the course meets the specific bullet points for the proposed theme requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or methods.

Theme courses have the common goal of cultivating in students a number of habits of mind:
  • thinking ethically about important challenges facing our society and world;
  • reflecting on the shared sense of responsibility required to build and maintain community;
  • connecting knowledge and practice;
  • fostering a stronger sense of our roles as historical agents.


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LE Recertification-Reflection Statement:
(for LE courses being re-certified only) 		<no text provided>
Writing Intensive
Propose this course
as Writing Intensive
curriculum:		 No
Question 1 (see CWB Requirement 1): How do writing assignments and writing instruction further the learning objectives of this course and how is writing integrated into the course? Note that the syllabus must reflect the critical role that writing plays in the course.

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Question 2 (see CWB Requirement 2): What types of writing (e.g., research papers, problem sets, presentations, technical documents, lab reports, essays, journaling etc.) will be assigned? Explain how these assignments meet the requirement that writing be a significant part of the course work, including details about multi-authored assignments, if any. Include the required length for each writing assignment and demonstrate how the minimum word count (or its equivalent) for finished writing will be met.

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Question 3 (see CWB Requirement 3): How will students' final course grade depend on their writing performance? What percentage of the course grade will depend on the quality and level of the student's writing compared to the percentage of the grade that depends on the course content? Note that this information must also be on the syllabus.

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Question 4 (see CWB Requirement 4): Indicate which assignment(s) students will be required to revise and resubmit after feedback from the instructor. Indicate who will be providing the feedback. Include an example of the assignment instructions you are likely to use for this assignment or assignments.

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Question 5 (see CWB Requirement 5): What types of writing instruction will be experienced by students? How much class time will be devoted to explicit writing instruction and at what points in the semester? What types of writing support and resources will be provided to students?

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Question 6 (see CWB Requirement 6): If teaching assistants will participate in writing assessment and writing instruction, explain how will they be trained (e.g. in how to review, grade and respond to student writing) and how will they be supervised. If the course is taught in multiple sections with multiple faculty (e.g. a capstone directed studies course), explain how every faculty mentor will ensure that their students will receive a writing intensive experience.

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Course Syllabus
Course Syllabus: For new courses and courses in which changes in content and/or description and/or credits are proposed, please provide a syllabus that includes the following information: course goals and description; format;structure of the course (proposed number of instructor contact hours per week, student workload effort per week, etc.); topics to be covered; scope and nature of assigned readings (text, authors, frequency, amount per week); required course assignments; nature of any student projects; and how students will be evaluated. The University "Syllabi Policy" can be found here

The University policy on credits is found under Section 4A of "Standards for Semester Conversion" found here. Course syllabus information will be retained in this system until new syllabus information is entered with the next major course modification. This course syllabus information may not correspond to the course as offered in a particular semester.

(Please limit text to about 12 pages. Text copied and pasted from other sources will not retain formatting and special characters might not copy properly.)


 Motivation: Spatial Computing is a set of ideas and technologies that transform our lives by understanding the physical world, knowing and communicating our relation to places in that world, and navigating through those places. The transformational potential of Spatial Computing is already evident. From 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 biker in Minneapolis, and a taxi 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 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 mathematical concepts (e.g. Euclidean space, topology of space, network space), geo-information models (e.g. field-based, object-based), representations (e.g. discretized, spaghetti, tessellation, vornoi diagram), algorithms (e.g. metric and Euclidean, topological, set-based, triangulation, graph-based), data-structures and access methods (e.g. space filling curves, quad-trees, R-tree), analysis (e.g. spatial query languages, spatial statistics, spatial data mining), architectures (e.g. location sensor, location based services), interfaces (e.g. cartography, Geo-visualization), reasoning (e.g. data quality, approaches to uncertainty), and time (e.g. valid time, events and processes). We will also explore spatial ideas and questions in other computing areas.

Required Work: Course has a set of four assignments and two examinations. The weighting scheme used for grading is: Midterm exam. - 25%, Final exam. - 25%, Assignments including a project - 40%, 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 spatial-news presenting and active group learning. Participants will take turn to review 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.

Schedule
Week Topic
1 Introduction to Spatial Computing
2 Database Concepts
3 Spatial Concepts
4 Models of Geospatial Information
5 Representations of Spatial Data (e.g., raster, vector, graph)
6 Algorithms (e.g., geometric, topological, graph-based)
7 Data Structures (e.g., spaghetti, node-arc-area)
8 Midterms
9 Access Methods (e.g., Quad-tree, R-Tree)
10 Architectures (e.g., location-based services)
11 Interfaces (e.g., cartography, geo-visualization)
12 Spatial Reasoning, Uncertainty
13 Spatio-temporal
14 Spatial Data Mining
15 Finals

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 , 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) auto-correlation 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.
Strategic Objectives & Consultation
Name of Department Chair
Approver:		 <no text provided>
Strategic Objectives -
Curricular Objectives:		 How does adding this course improve the overall curricular objectives ofthe unit?

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Strategic Objectives - Core
Curriculum:		 Does the unit consider this course to be part of its core curriculum?

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Strategic Objectives -
Consultation with Other
Units:		 In order to prevent course overlap and to inform other departments of new curriculum, circulate proposal to chairs in relevant units and follow-up with direct consultation. Please summarize response from units consulted and include correspondence. By consultation with other units, the information about a new course is more widely disseminated and can have a positive impact on enrollments. The consultation can be as simple as an email to the department chair informing them of the course and asking for any feedback from the faculty.

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