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UEP 294-22 Advanced Geographical Information Systems (GIS) Fall 2016

Location: GIS Lab, Tisch Library

Time: Wednesday 1:30-4:00PM

Instructor: Sumeeta Srinivasan, sumeeta.srinivasan@tufts.edu

Lab Hours: Wed 10-12PM and as arranged with individual students

Teaching Assistant: Sharon Ron, sharon.ron @tufts.edu

Prerequisites: A previous course in GIS or equivalent experience with ArcGIS or any other

GIS software is required. Students will be also expected to have competence in

microcomputer use and familiarity with Microsoft Windows environment

1.0 Course Objectives: The major goals are:

To learn more about the theoretical basis of GIS databases and analysis

To further explore topics in applied spatial analytical research and methodology

The learning objectives of this course are to:

Use appropriate spatial analysis methods in different contexts

Create and model databases for spatial analysis projects

Appraise spatial analysis in journal articles

Interpret and evaluate spatial statistics in journal articles

Implement an independent project that incorporates spatial analysis

2.0 Course Description: This course is intended to be students from any discipline with an interest in spatial data

analysis. It explores advanced topics in Geographic Information Systems (GIS) and their

applications in a specific context of interest to the students either in group or individual

projects. Every week, there will be a lecture and discussion as well as laboratory exercise

when students will be expected to work with a variety of spatial analysis methods

including spatial statistics, geostatistics and network analysis. The lab exercise will segue

into weekly assignment. The lab component will focus on the use of ArcGIS (Version 10.3)

and Geoda among other software in a Windows environment. Automation using Python

and Model builder will also be explored. The instructor will also provide support to

students who would like to use R.

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3.0 Grading: The final course grade will be based on:

Assignment 1 2%

Assignments 2-10 (best 7 scores) 35%

Final project (10% paper + 20% poster + 3% abstract) 33%

Presentation 15%

Participation 15%

4.0 Final Project The purpose of the final project is to provide additional experience in collecting, processing

and analyzing spatial data. The project can be relevant to your research interests or to your

thesis/ dissertation or for a joint project or final paper in another course. Students must

start thinking about project ideas early in the semester. The project should use spatial

analysis software to examine the spatial implications of a research problem. By November

30th, the student is expected to have scheduled and met the instructor for at least one

discussion about their project. The final project will require a paper describing the data and

a poster. The final project report is due Dec 19th. Group projects are encouraged but the

products of group work will be expected to scale-up corresponding to the number of

members in the group.

5.0 Textbook Lloyd, Christopher, D. 2010, Spatial Data Analysis, Oxford University Press.

The textbook is available from various booksellers both on and off-line. The book is also

available as an ebook from the Tisch Library at Tufts University.

6.0 Student Responsibilities for Meeting Course Objectives 1. Obtain and read the required textbook and supplemental material. Students will be

evaluated on knowledge and skills obtained from lecture, discussion, the required

textbook and supplemental reading materials.

2. Be prepared for class discussions and participation. Volunteer to both discuss

information and answer questions. Outcomes of this practice will be used by the

instructor as a means to subjectively evaluate students at the end of the semester.

3. Follow the student honor code and ethical standards. The academic code of conduct

can be accessed over the web at: https://students.tufts.edu/student-affairs/student-

life-policies/academic-integrity-policy

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4. Out-of-class assignment must be professionally prepared. This means the course

project and exercises will have to be legible and free of spelling errors, and poor

grammar. References must be cited properly. No late assignments will be accepted

under any circumstances.

5. If you need to communicate with the instructor, you may do so via e-mail, or by

making a personal appointment. It may take at least one workday for the instructor

to return a telephone or e-mail message. Please plan accordingly. If you need more

then 5-10 minutes of the instructor’s time, it may be best to schedule an

appointment.

6. Please do attend office hours not only when you have questions or concerns about

the material in class but also when you just need someone to brainstorm or have a

conversation.

7. Be prepared to spend many hours in the lab learning to work with the software and

data.

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7.0 Course Outline and Readings

Lecture Laboratory Assignment

Week 1

Sep 7

Course Introduction

Readings: Llloyd, Ch 1-2

Lab 0: Preliminary project abstract

Week 2

Sep 14

Geodatabases

Skim: support.esri.com/index.cfm?fa=downloads.dataModels.matrix

Lab 1: Geodatabases

Assignment 1 is due by 1PM

Week 3

Sep 21

Automating Analysis: Rasters, Vectors (again)

Readings: https://www.e-education.psu.edu/geog485/node/17

(Lesson 1-1.4.2)

Lab 2: Model Builder and Python in ArcGIS

Assignment 2 is due by 1PM

Week 4

Sep 28

Suitability analysis

Readings: Llloyd, Ch 5

Lab 3: Model Builder for suitability analysis

Assignment 3 is due by 1PM

Week 5

Oct 5

Raster analysis

Readings: Llloyd, Ch 10

Lab 4: DEM, cost path in ArcGIS

Assignment 4 is due by 1PM

Week 6

Oct 12

Network analysis

Readings: Llloyd, Ch 6

Skim: www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa03/cdrom/papers/433.pdf

Lab 5: Network Analysis in ArcGIS

Assignment 5 is due by 1PM

Week 7

Oct 19

Spatial Statistics

Readings: Llloyd, Ch 4.8, 7.1-7.3

Lab 6: Spatial statistics in Geoda

Assignment 6 is due by 1PM

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Lecture Laboratory Assignment

Week 8

Oct 26

Spatial Regressions

Readings: Llloyd, Ch 8

Lab 7: Spatial Regression in Geoda

Assignment 7 is due by 1PM

Week 9

Nov 2

Geostatistics

Readings: Llloyd, Ch 9

Lab 8: Geostatistical Analysis in ArcGIS

Assignment 8 is due by 1PM

Week 10

Nov 9

Spatio-temporal data

Readings: Delmelle et al, Methods for Space Time Analysis, 2013

Lab 9: Space Time GIS in CAST, ArcGIS and Geoda

Assignment 9 is due by 1PM

Week 11

Nov 16

GIS Project Discussion Assignment 10 is due by 1PM

Week 11

Nov 30

GIS Project

Project Abstracts are due

Week 13

Dec 7

Project Presentations

Week 14

Dec 14

Project help

Week 15

Dec 19

Final Poster and Paper due