CIVILEN 7432
Transcript Abbreviation:
Adv Spatial Data
Course Description:
Introduction to spatial algorithms; spatial data generation; 3-D spatial modeling; spatial indexing; spatial relational operators; normalization and confirmation; and spatial applications.
Course Levels:
Graduate
Designation:
Elective
Required
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
4.00
Max Credit Hours:
4.00
Select if Repeatable:
Off
Maximum Repeatable Credits
(N/A)
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 5001, 5421, 6431, or permission of instructor.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 787 or GeodSci 787.
Course Goals / Objectives:
Know how and when to generate spatial data sets
Write and program algorithms for managing spatial data sets
Build advanced mapping/GIS related spatial database systems
Use databases for GIS applications
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Introduction and fundamental knowledge | 4.0 | 0.0 | 3.0 | 0 |
| Automated Spatial Data Acquisition:Direct measuring techniques (GPS, total station based measurements; Indirect measuring techniques (mobile mapping, airborne and satellite remote sensing); Automatic spatial map feature generation | 9.0 | 0.0 | 6.0 | 0 |
| Spatial Object Modeling and Database Generation: 2-D Spatial object modeling; 3-D Spatial object modeling; Spatial database design and generation | 9.0 | 0.0 | 6.0 | 0 |
| Spatial Relations and Algebra in Mapping and GIS: Review of relational operators; Relational algebra; Application in mapping and GIS; Peano relations for raster map data; Normalization and conformation of spatial databases | 8.0 | 0.0 | 6.0 | 0 |
| Map Related Spatial Data Access, Quality and Applications:; Spatial indexing; Integrity constraints; Spatial topology and consistency; Distributed map related spatial databases; Octree and subsurface modeling; Spatial data mining | 8.0 | 0.0 | 9.0 | 0 |
| Sensors used in spatial data collection (satellite, airborne, and marine) | 4.0 | 0.0 | 9.0 | 0 |
| Total | 42 | 0 | 39 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Lab
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Course seminar | 10% |
| Laboratory assignments | 30% |
| Midterm | 20% |
| Final exam | 30% |
| Additional credit | 10% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Fundamentals of Spatial Information Systems | Laurini, R. and D. Thompson | |
| An Introduction to Database Systems | Date, C.J. | |
| The Design and Analysis of Spatial Data Structures | Samet, H. | |
| Three Dimensional Applications in Geographical Information Systems. | Raper, J. | |
| Algorithmic Foundation of Multi-Scale Spatial Representation | Li, Zhilin |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes
(N/A)
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)