Instructor: Steve Signell - firstname.lastname@example.org
TA: Robert Poirier - email@example.com
Meeting times: Monday and Thursday morning 10:00am - 11:50am
Office Hours: By appointment
Class Listing: ERTH 4750 (38031)
Class Location DCC 232
Table of Contents
Introduction to relational analysis and interpretation of spatial data and their presentation on static and interactive maps using PostGIS, qGIS, Leaflet.js and Geoserver. Geographic spatial data concepts covered are map projections, vectors & geoprocessing, raster analsysis, interpolation, collaborative mapping, GIS on the cloud and web mapping. Database concepts of building and manipulating a spatial database, SQL, spatial queries, and integration of graphic and tabular data are also covered. During each class we will discuss topics and do examples. Related take-home exercises will be assigned. Depending on class size, students may be asked to present assignments to the rest of the class. Each student will do a semester-long project on some topic of particular interest to them, but also of relevance to the class. These projects will be presented to the class during the last week. 4 credit hours.
Each Topic will meet for 3 to 4 hours per week, comprising 1.5 hours of instruction and approximately 2 hours of lab.
Syllabus/ Calendar (Tentative)
Jan. 23 (Thursday) Lecture: Introduction to Geographic Information Systems: Week 1 Thursday Slides [Download]
Jan. 27 (Monday) Lecture: GIS I: Projections & vector data: Week 2 Monday Slides [Download]
- Install QGIS on your laptop
Jan. 30 (Thursday) Lab: Viewing and analyzing vector data with QGISWeek 1 Exercise Data [Download]
- NAIP link:http://raster.nationalmap.gov/ArcGIS/services/Orthoimagery/USGS_EDC_Ortho_NAIP/ImageServer/WMSServer?
- Install QGIS on your laptop!!!
Feb. 3 (Monday) Lecture: GIS II: Raster AnalysisWeek 3 Monday Slides [Download]
Homework: Finish modules from Thursday Lab:
Feb. 6 (Thursday) Lab: Viewing and analyzing raster data with QGIS
Homework: Terrain Analysis [Download]: Will not be graded but complete before lab on Feb. 6.
Feb 10 (Monday) Lecture: GeoData I: Scrounging 101, tracking down geodataWeek 4 Monday Slides [Download]
Homework for Monday:
- Work with your group to make a list of what kinds of data you'd like to acquire for your portion of our class RPI mapping project.
- Make a list of data you'd like to acquire for personal project.
Feb. 13 (Thursday) Lab: GeoData Scrounging 101
Homework for Thursday:
- Work with your group to find and download at least 5 datasets for your group project.
- Find and download at least 5 datasets for your personal project.
Graded Assignment #1: Due Thursday, Feb. 2O:
Feb. 18 (**TUESDAY!**) Lecture: GeoData II: Mobile data collection: Guest Lecturer Bryan McBride (Fulcrum)
Readings for Tuesday:
Feb. 13 (Thursday) Lab: Mobile data collection with Fulcrum
Homework for Thursday:
- Complete Graded Assignment #1-- due at beginning of class on Thursday.
Feb. 24 Monday Lecture: Introduction to Spatial Databases
Readings for Monday:
Feb. 27 (Thursday) Lab: Setting up a PostgreSQL/PostGIS Database
DATA:Week 6 Lab Data [Download]
Homework for Thursday:
Mar. 3 Monday Lecture: Spatial Queries in PostGIS: Week 7 Monday Slides [Download]
Mar. 6 (Thursday) Lab: Spatial Queries in PostGIS
Homework to complete before Thursday:
Week 8 (Mar. 10/Mar. 13: no classes - spring break)
Week 9 (Mar. 17/Mar. 20)
- Monday-lecture: Collaborative GIScience I: Literate Programming & GitHub
- Thursday-lab: Git & GitHub
Week 10 (Mar. 24/Mar. 27)
- Monday-lecture: Visualizing & Sharing Geodata on the Web I
- Thursday-lab: Geoserver, Google, CartoDB
Week 11 (Mar 31/Apr. 3
- Monday-lecture: Visualizing & Sharing Geodata on the Web II
- Thursday-lab: Leaflet.js & D3.js
Week 12 (Apr. 7/Apr. 10
- Monday-lecture: Collaborative GIScience II: Web Map Mashup
- Thursday-lab: Group Mash-up: work on group project
Week 13 (Apr. 14/Apr. 17)
- Monday-lecture: Multidimensional data I: Guest Lecturer Robert Poirier (RPI): Ocean Data View
- Thursday-lab: Ocean Data View
Week 14 (Apr. 21/Apr.24)
- Monday-lecture: Multidimensional data II: 3D Visualization
- Thursday-lab: Guest Lab Leader Aashish Chaudhary (KitWare): WebGL
Week 15 (Apr. 28/May 1)
- Monday-lecture: Wrap up: Review & the future of GIS
- Thursday-lab: Work on group & individual projects.
Week 16 (May 5): Monday: Final project presentations
GIScience Applications: Getting data files and software for THIS CLASS
Goals of the Course
- To provide students an opportunity to learn geospatial applications and tools.
- To introduce relational analysis and interpretation of spatial data and presentation on maps.
- Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
- To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
- To gain experience in an end-to-end GIS application via a term project.
Course Learning Objectives
Through class lectures, practical sessions, written and oral presentation assignments and projects, students should be able to:
- Demonstrate proficiency in using geospatial applications and tools (commercial and open-source).
- Present verbally relational analysis and interpretation of a variety of spatial data on maps.
- Demonstrate skill in applying database concepts to build and manipulate a spatial database, SQL, spatial queries, and integration of graphic and tabular data.
- Demonstrate intermediate knowledge of geospatial analysis methods and their applications.
- Via written assignments addressing each learning objective with specific percentage of grade allocation provided for each assignment and question
- Via projects and presentations
- Via participation in class (not to exceed 10% of total)
- Late submission policy: first time with valid reason – no penalty, otherwise 20% of score deducted each late day
Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts, which violate this trust, undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty. If found in violation of the academic dishonesty policy, students may be subject to two types of penalties. The instructor administers an academic (grade) penalty, and the student may also enter the Institute judicial process and be subject to such additional sanctions as: warning, probation, suspension, expulsion, and alternative actions as defined in the current Handbook of Student Rights and Responsibilities. of an academic grade penalty or . If you have any question concerning this policy before submitting an assignment, please ask for clarification.
- Knowledge such that gained in geography, cartography.
- or permission of the instructor
Enrolled students may miss at most one class without permission of the instructor. Missed classes will contribute to class participation assessments.