Instructor: Xiaogang (Marshall) Ma - max7 at rpi dot edu
TA: Anastasia Rodzianko - rodzia at rpi dot edu
Meeting times: Tuesday and Friday afternoon 4:00pm - 5:50pm
Office Hours: Thursday 1:00pm - 2:00pm in Winslow 2132 (or by appointment)
Class Listing: ERTH 4750 (38031)
Class Location JRSC 2C25
Table of Contents
Introduction to relational analysis and interpretation of spatial data and their presentation on maps (using MapInfo and R). Geographic spatial data concepts covered are map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, extrapolation, and kriging. Database concepts of building and manipulating a spatial database, SQL, spatial queries, and integration of graphic and tabular data are 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 close to 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)
Refer to Reading/ Assignment/ Reference list for each week (see below).
- Week 1 (Jan. 22/25): Tuesday-lecture: Introduction to Geographic Information Systems Week 1 Tue slides [Download], Friday-lab: MapInfo Professional: Getting started Week 1 Fri slides [Download]
- Week 2 (Jan. 29/Feb. 1): Tuesday-lecture: Geographic information and spatial data types Week 2 Tue slides [Download], Friday-lab: MapInfo Professional: Viewing and analyzing data Week 2 Fri slides [Download]
- Week 3 (Feb. 5/8): Tuesday-lecture: Spatial referencing Week 3 Tue slides [Download], Friday-lab: MapInfo Professional: Buffering, projection and printing Week 3 Fri slides [Download]
- Week 4 (Feb 12/15): Tuesday-lecture: Geostatistical computing Week 4 Tue slides [Download], Friday-lab: Using the R environment for statistical computing [Download]
- Week 5 (Feb. 19/22): Tuesday-lecture: Exploring and visualizing spatial data Week 5 Tue slides [Download], Friday-lab: Spatial visualization with R [Download]
- Week 6 (Feb. 26/Mar. 1): Tuesday-lecture: Modeling spatial structure from point samples Week 6 Tue slides [Download], Friday-lab: Modeling spatial structure from point samples with R [Download]
- Week 7 (Mar. 5/8): Tuesday-lecture: Spatial prediction from point samples (Part 1) Week 7 Tue slides [Download], Friday-lab: Spatial prediction from point samples with R (Part 1) [Download]
- Week 8 (Mar. 12/15: no classes - spring break)
- Week 9 (Mar. 19/22): Tuesday-lecture: Spatial prediction from point samples (Part 2) Week 9 Tue slides [Download], Friday-lab: Spatial prediction from point samples with R (Part 2) [Download], E_Z_Kriging program and manual [Download], OK_Explained.xls spreadsheet [Download]
- Week 10 (Mar. 26/29): Tuesday-lecture: Assessing the quality of spatial predictions Week 10 Tue slides [Download], Friday-lab: Assessing the quality of spatial predictions with R [Download]
- Week 11 (Apr. 2/5): Tuesday-lecture: Interfacing R with GIS Week 11 Tue slides [Download], Friday-lab: Interfacing R with GIS. [Download] -- Note that you need to install packages 'rgdal', 'maptools', and 'gpclib' to run the example scripts in this lab. Also in the lower part of page 35 the PNG file name in the script should be 'MeuseZn.png' instead of 'MeuseZnInterpolation.png'. Otherwise you will not see the result as shown in Figure 4 in the lab document.
- Week 12 (Apr. 9/12: no class - Grand Marshall week)
- Week 13 (Apr. 16/19): Tuesday-lecture: Efficient and effective result presentation with GIS Week 13 Tue slides [Download], Friday-lab: Lecture: Dr. Gavin Schmidt, " What are climate models good for?" When: Friday, April 19, 2013 4:00 PM - 5:00 PM; Where: EMPAC Concert Hall
- Week 14 (Apr. 23/26): Tuesday: Guest Lecture: Dr. D.G. Rossiter, Point-pattern Analysis, Friday-lab: Time of this lab is for the term assignment.
- Week 15 (Apr. 30): Tuesday: Short final project presentations
Reading/ Assignment/ Reference List
Week 1 Reading Assignment:
Week 2 Reading Assignment:
Week 3 Reading / Written Assignments:
Week 4 Reading Assignment:
Week 5 Reading Assignment:
- Read the lecture slides for Tuesday Feb 19 (See link in the Syllabus). We have no class that day according to the RPI academic calendar.
Week 6 Reading Assignment:
- Read the lecture slides for Tuesday Feb 26 and the lab document for Friday March 01 (See link in the Syllabus). For this Friday lab you are required to submit a short document with answers to the self-test in the lab document.
Week 7 Reading Assignment:
Week 9 Reading Assignment:
- No reading assignment for this week
Term Written Assignment (individual)
- Term Written Assignment Document [Download]
Week 10 Reading Assignment:
- Data Quality in GIS
- Hard copies of handouts from the lecture (digital copies available from instructor and TA via email request)
Week 11 Reading Assignment:
- Chapter 9 Selecting and Querying Data. In: MapInfo Professional 11.0 User Guide
- Handout: Chapter 4 Spatial Data Import and Export. In: Bivand, R.S., Pebesma, E.J., Gómez-Rubio, V., 2008. Applied Spatial Data Analysis with R. Springer, New York, NY, 374pp. (digital copies available from instructor and TA via email request)
Week 13 Reading Assignment:
- Chapter 12 Stylizing your map for presentations and publishing. In: MapInfo Professional 11.0 User Guide
- Chapter 16 Working with data from a web service. In: MapInfo Professional 11.0 User Guide
- Huisman, O., de By, R.A. (eds.), 2009. Principles of Geographic Information Systems. ITC Press, Enschede, The Netherlands (A nice text book on GIS. It is a big pdf document - right click and save as a local file.)
- Rossiter, D. G., Distance education course: Geostatistics & Open-source statistical computing (Thanks to Dr. Rossiter for sharing his lecture and lab documents, a part of which is used in this course)
- MapInfo Professional 11.0 User Guide (Big pdf document - right click and save as a local file.)
- MapInfo Professional Video Tutorials
- List of geographic information systems software
- Friedl, L., Yuen, K., et al., 2012. Eart as Art. NASA. (Viewing enjoyment of remote sensing images)
- Flat Earth
- BBC Animated History of European Map making
- Olea, R.A., 2009, A practical primer on geostatistics: U.S. Geological Survey Open- File Report 2009-1103, 346 p.
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.