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GIS for Science 2018

Instructor: Steve Signell - signes@rpi.edu
TA: XXX - @rpi.edu
Meeting times: Tuesday and Friday morning 08:00am - 09:50am
Office Hours: By appointment
Class Listing: ERTH 4750 (55699)
Class Location J-ROWL 3W13

Table of Contents

Description

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)

 

Week 1

Jan. 23 (Thursday) Lecture: Introduction to Geographic Information Systems: Week 1 Thursday Slides [Download]

 

Week 2

Jan. 27 (Monday) Lecture: GIS I: Projections & vector data: Week 2 Monday Slides [Download]

Reading:

Other:

  • Install QGIS on your laptop

Jan. 30 (Thursday) Lab: Viewing and analyzing vector data with QGISWeek 1 Exercise Data [Download]

Reading:

Other:

  • NAIP link:http://raster.nationalmap.gov/ArcGIS/services/Orthoimagery/USGS_EDC_Ortho_NAIP/ImageServer/WMSServer?
  • Install QGIS on your laptop!!!

 

Week 3

Feb. 3 (Monday) Lecture: GIS II: Raster AnalysisWeek 3 Monday Slides [Download]

Homework: Finish modules from Thursday Lab:

Reading:

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.

 

Week 4

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.

Reading:

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:

 

Week 5

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.

 

Week 6

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:

 

Week 7

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:

  • 1-page proposal for your individual project due Thursday. Not Graded
  • Complete sections 1-9 in the Boundless Introduction to PostGIS. Exercise data can be downloaded from here.

    You may skip Sections 2 & 3 if you already have a spatial database created.

 

Week 8 (Mar. 10/Mar. 13: no classes - spring break)

 

Week 9

Mar. 17 Monday Lecture: Literate Programming and the Process of GIS Week 9 Monday Slides [Download]

Mar. 20 (Thursday) Lab: The Process of GIS
Homework to complete before Thursday:

  • Complete sections 10-13, and 18-19 in the Boundless Introduction to PostGIS. Exercise data can be downloaded here. Please run your queries and save your outputs in the giscience.tw.rpi.edu PosgreSQL database so I can check your work and help troubleshoot any problems.

 

Week 10

Mar. 24 (Monday) Lecture: Visualizing & Sharing Geodata on the Web I

Homework due Monday:

  • Updated map and 1-page writeup for group projects. One submission per group, submitted electronically (via email).

Mar. 27 (Thursday) Lab: Geoserver, CartoDB
Homework for Thursday:
Sign up for a free CartoDB account and complete the following tutorials found on this page:

Assignment 3 due Monday March 31GIScienceAssignment3 [Download]

 

Week 11

Mar. 31 (Monday) Lecture/Lab: Visualizing & Sharing Geodata on the Web II: Leaflet.js: Week 11 Monday Slides [Download]

Assignment 3 due (electronically) by the start of class Monday.

Download Leaflet Template:giscience_leaflet_template [Download]

Apr. 3 (Thursday) Lecture/Lab: Visualizing & Sharing Geodata on the Web III: Leaflet.jsWeek 11 Thursday Slides [Download]

Download files for class:giscience_leaflet_files2 [Download]

 

Week 12

Apr. 7 (Monday) Lecture/Lab: Visualizing & Sharing Geodata on the Web III: Geoserver:
Download Leaflet Template for RPI:giscience_leaflet_template_rpi [Download]

Apr. 10 (Thursday) Lecture/Lab: Webmapping Wrapup & Geoserver reboot:

Lab12Geoserver2.docx [Download]
Download data for todays exercise:LabData4.10.2014.zip [Download]

 

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 (Monday) Lecture/Lab: Web Map Troubleshooting & Multidimensional data II: 3D Visualization Week 14 Monday Slides [Download]

Thursday-lab: Field Trip to KitWare

 

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

  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. 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.

Assessment Criteria

  • 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

Academic Integrity

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.

Suggested Prerequisites

  • Knowledge such that gained in geography, cartography.
  • or permission of the instructor

Attendance Policy

Enrolled students may miss at most one class without permission of the instructor. Missed classes will contribute to class participation assessments.

==Additional Information==
 


Topics: Geoinformatics, Geoscience, Geographic Information System
Course Numbers:
  • 55699
Description:
  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. To gain experience in an end-to-end GIS application via a term project.

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 and 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.

Goal:

  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. To gain experience in an end-to-end GIS application via a term project.

Learning Objective:

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.
Assessment Criteria:
  • 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
Academic Integrity:
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.

Course: GIScience

Date: to