Mills College 2009
SOC128 Geographic Information Systems and Sociological Geography

Professor Dan Ryan

Lecture Tues-Thurs 14.30 - 15.45
Lab alternating Wednesdays 13.00 - 15.00

Course Overview

Course Objectives

The course provides students with the opportunity to attain beginner to intermediate level competence with ESRI's ArcGIS Desktop along with experience applying GIS to one or more fields of interest. In addition to hands-on skills, the course also introduces students to cartography, spatial data analysis, data visualization, and the use of images to communicate.   Along the way, students will have the opportunity to learn some real geography, a bit of math and physics, a little bit of computer science, and to develop deeper expertise in specific areas if so motivated.


  1. Ormsby, et al.  2008.  Getting to Know ArcGIS Desktop.  Redlands: ESRI. (Second Edition (2004) OK too)
  2. Maantay & Ziegler.  2006.  GIS for the Urban Environment.  Redlands: ESRI.
  3. GTKAGI errrata (Downloadable PDF)

How to Learn Computer Applications

There are three variables that will determine how well you learn to fly the software introduced in this course.  Most important is simply the number of hours you log in the pilot's seat (I would estimate 6-8 solid, separate hours per week for at least the first several weeks).  Second most important is the frequency with which you do so -- more than once per day is ideal; once per week is a recipe for falling behind and/or failing.  Third most important is repetition.  It is generally better to move forward at a reasonable clip and repeat exercises until they are second nature than to proceed with slow, deliberate care taking notes on each step.  You are developing intuition here, not studying for an exam.

A Note on Working With Computers

You will often follow the instructions in an exercise and not get the results the instructions indicate.  Most people's first inclination is to think either (1) something is wrong with the computer or software, or (2) the instructions are wrong.  Both are possible, but both are much less common events than misreading the instructions.  Computers are, at the end of the day, damnably consistent.  One of the joys (if you see it in the right light) of working with them is that if you remember that you can learn a lot about how to master them and make productive use of them.  It does, however, require of many folks a certain attitude adjustment: you will repeatedly make little mistakes and the damn machine will catch you EVERY time.  If you can come to think of this as a virtue, you will enjoy this course and learn a lot.  If you allow it to frustrate, anger, intimidate, or shame you or undermine your confidence, you will neither enjoy the experience nor learn much.  Nor will you be much fun to work with.

Mastering the Art of Thinking Slowly

Finally, there seems to be a tendency when around computers to feel compelled to show how fast one can think.  This is also manifest, sometimes, in a compulsion to read very quickly, skipping words, or whole steps.  Success requires the exact opposite of these.  The computer will think (and act) quickly, repeating tasks over and over again without complaining.  Our job is to go slowly and systematically so we can be sure that the instructions we ask the computer to carry out millions of times at lightning speed make sense and get done the job we want to do.  Slow, step by step, cognitive plodding is the thing you want to cultivate in this class (and it's not a bad tool to have in other areas in life either).


Since this is largely a skill-based course, the assessment is mostly based on demonstrating that you have learned the skills.  Evaluation divided among 6 areas (they total to 105% -- a bonus is added for staying on schedule with exercises).

  1. Attend class and labs. [10%]
For 100% credit, miss no more than one class, never miss a lab.  Deduct 10% for each additional class missed, 20% for each lab.*
  1. Work through exercises at pace and learn the content thereof. [5% -- bascially a bonus since the impact of this will be seen elsewhere]
You'll be asked to submit progress reports during the semester.  Three levels: 5-on track all the way; 3-on track most of the time; 0-not on track much of the time.
  1. Attend, carryout, and submit lab work [10%]
Some labs will be "do this in lab and print it out when you  are done" and some will be "take what learned here and go home and do something with it."  In either case, there will be something to turn in after each lab.  Six labs graded "done and submitted by due date" (3) or not (0).
  1. Do one "Case Study Presentation" (see below). [15%]
Each member of class will make a presentation of the material in one of the case studies in the GIS for Urban Environment textbook.  Examples and instructions for presentations will be provided.  Grading scale to be distributed with instructions.
  1. Sit for two "Lab Practicals" (see below). [25%]
These will be short "oral" or "manual" exams.  Class members will be provided with a list of skills to master.  Each will have a short (10-15 minute) appointment with instructor during which, by roll of dice you will be asked to demonstrate 3 GIS skills.  Each is graded as "3-aced," "2-OK," or "0-didn't really know how to do it" plus 1 for showing up for 10 total possible points.
  1. Propose, carry out, and present one mapping project. [40%]
25% for proposal, 25% for data, 25% for mapping, 25% presentation.

* For those who do this sort of math, missing a class reduces semester grade by 4% of a letter grade, a lab by 8%.

Other Policies

Customary academic standards academic integrity (including proper bibliographic citation) apply.  It is your responsibility to know what these are and to follow them.  Collaborative learning is encouraged, but work that is submitted under your name as a demonstration of your skills and competence must represent YOUR work.  Plagiarism, as defined under the Mills College Honor Code, will be cause for, at a minimum, a failing grade in this course. Please consult with instructor if you have any questions, or even the slightest doubt, about how to follow these requirements.


Every effort will be made to make this class accessible for students regardless of disability. Students with needs for accomodation should contact for Students with Disabilities (Cowell Building, x2130) and inform the instructor in order for access to be arranged adequately and promptly.

Assessment and Student Learning

What will this course contribute to the educational outcomes articulated by the department of sociology for its courses?  Successful completion of this course should provide you with the following educational commodities
  1. You will learn some things that we know about the world.
    1. Substantive knowledge about the world. Students will acquire knowledge of some empirical facts about the world through exposure to maps of actual data.    They will likely internalize a sense of things like basic demographics of the world and the US, basic geography, and perhaps a little history too.
  2. You will develop attitudes and skills of empirical research.  This course is primarily about a set of methods used to process the results of empirical research.  What students will walk away with can be packaged as follows:
    1. Empiricism as value.   This course should enhance students' orientation toward rigorous and methodical “finding out” as a way to answer questions about human affairs.
    2. Methods of Empirical Research. Students should acquire a specific set of skills that can be used to analyze and report on empirical data.
    3. Finding what is already known. Students will leave the course with an enhanced capacity to find and use existing data.
  3. You will expand your communication repertoire and skill.
    1. Attitude of valuing clear communication.  The study of map-making is at its heart the study of clear communication and so students will be repeated exhorted to value clear, coherent, stylistically correct communication although more of this will be visual and oral than writing.
    2. Knowledge of the conventions of scholarly communication.  Students will leave the course with familiarity with a new style of scholarly communication -- cartography --  and will be exposed to the differences between casual, journalistic, and scholarly data visualization.
    3. Capacity to speak well.  Students will receive instruction in, have two opportunities to practice, and will be evaluated on, public presentation of scientific findings.

Schedule of Class Meetings and Labs

Th 22 Jan

Course Overview and Introduction

Situating this course in comparison to others with GIS in the title.  Lectures, self-study, and labs.  How we'll use the two textbooks.  Loading software on your computer. Some ideas about data organization on your computer.  What the interface looks like.  What you'll learn.  How to study in a course like this.  How you will be evaluated.

Know much about geography? Try these interactive quizzes
  1. This weekend you should install the software and exercise data on the machine you will usually use (if you will be using a machine other than those in the lab). NOTE: for GIS for the Urban Evironment, just load the data for the exercises.

  2. Download and/or printout a work calendar (pdf|xls).  For the first few weeks, record your ArcGIS sessions -- start and finish, what you worked on, what questions came up.

  3. Watch the Flash videos "What is GIS?"  "GIS Show" and "About the Software" that are on the CD/DVD that came with your textbook or try clicking here and here and here

  4. Look at the next item on the syllabus: you have reading for Tuesday and can (should) get started playing with the software right away.

For some current-event-relevant examples, you might want to have a look at these Dynamic Maps of Nonprime Mortgage Conditions in the US at New York Fed

Tu 27 Jan

Maps, GIS, and the Real World

What is a map?  How long have humans been mapping?  Basic map reading.  What is GIS?  Layers.  Data. Who uses GIS for what?  Start absorbing vocabulary like toolbars, display, table of contents, docked/floating toolbars, context menus, dialog boxes, maps, layers, features, attributes, polygons, lines, points, vector, raster, zoom, scale, thematic maps.

NOTE: you should start working on lab I (see below) as soon as you can.  Do NOT wait until the morning of the lab to prepare for it. (see suggested exercise schedule)

Read* GISftUE chapter 1.
GTK chapters 1 and 2.  Skim chapter 3.
GTK pp 357-8.  Don't worry if it does not make complete sense.  The point is to plant some mental seeds.

* All reading assignments are to be completed PRIOR to the class date for which they are assigned.

We 28 Jan

Jump In, See What GIS Can Do and How

Sometimes the best way to learn to swim is just to jump in the pool.  This won't be quite that abrupt since we introduced some ideas and the look and feel of ArcGIS in class and since you'll have read a bit about it before you start and since you've been working on the exercises in GTK.  After this lab session (building on the prep you are expected to do before hand and bolstered the followup you are expected to do after) you will have a feel for the ArcGIS interface, how to move back and forth between its "views," how to find map files, how maps are built up out of layers and so on.

Before lab.  You should already have attentively read GTK pp. 1-18.  Then skim through 19-46 before you sit down at the computer.  Then, sit down at the computer and start working through exercises 3a to 4c.  Keep track of the time you put in on this.

In lab. We will work through the exercises again with assistance as needed to get over rough spots.

: Exercise 3a Displaying map data
GTK: Exercise 3b Navigating a map
GTK: Exercise 3c Looking at feature attributes
GTK: Chapter 4: Exploring ArcCatalog
GTK: Exercise 4a Browsing map data
GTK: Exercise 4b Searching for map data
GTK: Exercise 4c Adding data to ArcMap
GISftUE: Lab Exercise Two: Exploring Basic GIS Functionality 442-457

After lab. You should finish working through the exercises.

Hand In.  Before next Tuesday's class, turn in finished map from Exercise 4c and copy of your work calendar to date.

Th 29 Jan

All Maps Lie: Maps as Abstraction

All maps are abstract representations of reality.  We can describe a range of techniques used to accomplish this.  We start with maps in general and then move on to GIS in particular.  Raster and vector.  Continuous and discrete.  Scale.  Generalization.  Extent, unit of analysis, aggregation.

Part I.  Map as drawing > map as objects and layers (raster and vector). Scale and zoom.Units of observation/analysis/aggregation (boundary layers as counting trays).

Part II. Sample Case Study Presentation by DJJR "Nonprofit Organization GIS for Strategic Planning and Outreach" ( GISftUE: 323ff).

Read GISftUE: Chapter Two: Spatial Data and Basic Mapping Concepts 25-38

Supplementary Material

Wood, D. & J. Krygier.  2009. "Maps." Elsevier. (pdf)

Check-In: As of today you should have completed GTK exercise 5d

Tu 3 Feb

Projections and Coordinates I:  

From 3-d reality into a 2-d representation.  This presentation describes the geometric tools used to display the surface of the earth on a flat piece of paper.  As it turns out, all the available techniques introduce distortion (e.g., tearing, compression/expansion (changing distances), shearing (changing angles)) into parts of a map.  To be responsible map makers we need to be aware of these distortions so that we can choose techniques most appropriate for the job at hand.  To be adept map readers we need to know what distortions have been introduced by choices made by the mapmaker.

We'll just scratch the surface in this one session on the topic so that we have enough background to proceed.  We'll return later to beef up our level of expertise.

Read GISftUE:  39-53
Read GTK: 331-337
Read Map Projections at Wikipedia"
In ArcGIS help, look over the entries for the following:
Projection, definition of
Projection Basics Overview
Projection Types, conic (and the others on that page)

Supplementary Materials

Geometric Aspects of Mapping" by Richard Knippers
Map Projections at the Geospatial Training and Analysis Cooperative
Review Gallery of Map Projections at Technical University Wien 

Th 5 Feb

Thematic I  : To Each Purpose, a Map

Maps come in multiple genres. In this section of the course we will develop a taxonomy of map types and, especially, learn about different kinds of thematic maps and how to make them well. Presentation may include slide show exhibiting variety of map types and suggestions of a taxonomy of thematic maps.

Read GISftUE: : 58-64

Check-In: By now you should have completed GTK exercises 7c.

Tu 10 Feb

Thematic II

Specialty Maps.  Types of thematic maps.  Multivariate maps.  The range of thematic maps.  Emphasis on visual communication, feature selection, symbolization.  Saying what you want to say, not saying what you don't want to say.  Why statistics starts to matter.  Visual presentation of quantity.  Possible example: how to show the "dividedness" of American politics.

Read GISftUE: : 65-89

We 11 Feb

Thematic Maps

Collectively putting together a presentation of a case study using thematic maps?

GISftUE: Lab Exercise Three: Thematic Mapping: Dot Density Maps
GISftUE: Lab Exercise Four: Thematic Mapping: Choropleth Maps

To prepare for this lab you should:

Read GISftUE: 459-481.
Read Borough (New York City) in Wikipedia
Read Bronx in Wikipedia
Visit the American Fact Finder site at the Census Bureua and see what you can find out about the Bronx

7 Th 12 Feb

OPEN - Guest Lecture?



Check-In: By now you should have completed GTK exercise 9b

Tu 17 Feb


Th 19 Feb

OPEN - Google Earth

Case Study Presentation 1, 2, 3 (45 minutes)

Check-In: As of today you should have completed GTK exercise 11d

Tu 24 Feb

Data Classification Methods, Exploration, Statistics

Four ways to measure things and the implications of levels of measurement for mapmaking.  Basic stats.  Ratios, percentages, frequency tables, doing it with Excel

Read GISftUE: 93-97

LAB 3 We 25 Feb

Excel and Statistics and Maps and Charts

Th 26 Feb

Data Classification Methods, Exploration, Statistics

The concept of a "distribution" is perhaps the most important take-away from a first encounter with things statistical.  We will start with the idea of a univariate distribution and how to characterize it and end up at the idea of multivariate distributions and what they can do for us in terms of understanding correlations and classifications.

Describing distributions -- frequency tables, histograms, charts, central tendency, dispersion, quantiles

Read GISftUE 93-115

Check-In: As of today you should have completed GTK exercise 13b

Tu 3 Mar

Data Classification Methods, Exploration, Statistics

How decisions about cutpoints and divisors lead to completely different maps. Ignorance can mean losing control of your map, failing to discover patterns in the data, and maps that (un)intentionally lie.

Read GISftUE 109-115

Case Study Presentation 4

Th 5 Mar

Data Classification Methods, Exploration, Statistics

Case Study Presentation 5

Check-In: As of today you should have completed GTK exercise 15b. 

Try review Qs:

Tu 10 Mar

Data Classification Methods, Exploration, Statistics, Charts

Making maps is a classic case of "part art, part science."  Today we'll highlight a few important elements from each realm.  Lesson one will be recognition that what we are doing with maps falls under the name "data visualization" and there are a few things we can learn from that.  Lesson two is that graphic designers have a few things we can benefit from learning.  Lesson three comes from statistical graphics and might be subtitled "how to lie with maps" except that's the title of a book so let's go, instead, with "how to be in control of the lies your maps are telling."

Read GISftUE: 126-133 (charts)
Read GISftUE: 134-155 (map design)

Case Study Presentation 6

LAB 4 We 11 Mar GISftUE: Lab Exercise Five: Integrating Graphs and Maps, and Designing Map Layouts

Th 12 Mar

Project Workshop #1

Each member of the class should come prepared with elements of a project proposal (which you should already have discussed with instructor).  Content will differ in each case but should include a very rough version of:
A case study from the book that you are thinking of using as a model
Your own background knowledge -- what expertise do you bring to the project?
Results of your preliminary research into likely data sources, etc.
Hand drawn story board of how you can imagine the project.

Case Study Presentation 7

Check-In: As of today you should have completed GTK exercise 17c

Tu 17 Mar

Project Workshop #2

Based on last week's class and your further research, you should bring to class and turn in your further developed project proposal (see above for content).

Case Study Presentation 8

Th 19 Mar

Data I: What's Out There?

First we learn ABOUT sources of data.  Which ones are out there, what they are good for, what you need to know about them to use them, etc.  Then we learn how to acquire them and bring them into the GIS.  Concepts to be covered include: GIS data vs. data with geography.  Boundaries vs. points.  Government sources of geographic data (USGS, Census Bureau, EPA, etc.); data quality issues, metadata, census hierarchy, TIGER, 7.5 min quads, DOQQ, DEM, HMDI, TRI, Sanborn maps)

GISftUE: 158-177

Have a look: Standford University Web Sites for Digital GIS Data

Case Study Presentation 9

S p r i n g   B r e a k   M a r c h   2 3  –  2 7

Check-In: As of today you should have completed GTK exercise 18c

Tu 31 Mar

Data II: Downloading and Importing GIS Data

How do we get data into a GIS?  Suppose you have survey data or field data in Excel form and you want to bring it into ArcGIS; how is it done?  The first question, of course, is does your data have geographic information in it.  If it does -- either in the form of GPS coordinates, long/lat, the name of a point or polygon object (e.g., city, state, zip code, country), or an address -- then we can import it and "geo-reference" it.

LAB 5 We 1 Apr GISftUE: Lab Exercise Six -- Developing an Attribute Database from an Internet Source (499-503)

Th 2 Apr


Case Study Presentations 10,11

Tu 7 Apr

Data III: Geocoding and Georeferencing

Lots of data contain geographic information (e.g., an address, or a zip code) but its "spatiality" remains latent until the information is interpreted.  The general process for doing this is called "geo-coding" or "geo-referencing."  Today we learn how to use address data to code data points.

A second way to georeference data is to join data by name to an existing geographic layer.

GISftUE: 181-189

Th 9 Apr

Data IV: Projections and Coordinates again

We really ought to have a little more expertise than we managed to cover in our first visit to these topics earlier in the semester.  Today we'll get a little technical and it will make more sense given where we've already been.

Read "Geometric Aspects of Mapping" by Richard Knippers (html)
Read "Map Projections" at the Geospatial Training and Analysis Cooperative (html)
Review "Gallery of Map Projections" at Technical University Wien (html)

Tu 14 Apr

Introduction to Relational Databases

Half the power behind GIS lies in the fact that it ties computer graphics to a "relational database."  A relational database consists of one or more tables of data the rows of which are linked on the basis of common information.  This section of the course provides a basic background knowledge about relational databases and the things we can do with them (designing them, putting data in, viewing data, generating summaries and reports, querying, etc.) and shows us how to do these things in a spatial database.

Read GISftUE: 191-207

LAB 6 We 15 Apr

Lab Title

GIS ftUE: Lab Exercise Ten: Geoprocessing Operations and Joining Tables (541-547)

GISftUE: Lab Exercise Eight: Working with Relational Databases (518-528)
GISftUE: Lab Exercise Nine: Generating Buffers and Using Selection for Proximity Analysis

GISftUE: Lab Exercise Eleven: Data Exploration and Geostatistical Analysis
GISftUE: Lab Exercise Twelve: Advanced Layout Techniques

Th 16 Apr
Spatial Analysis I Overview
Types of analysis. Simple query: Data search and analysis.  Reclassification.  Geoprocessing.  Optimal location/suitability analysis.  Boolean operators.  Modeling.  Expert systems and rule-based reasoning. Interpolation

Read GISftUE: Chapter Nine: Methods of Spatial Data Analysis
Read GISftUE: Written Exercise 9: Using GIS for problem solving

Section 5: Analyzing feature relationships

At this point you've already seen how to exploit the spatial analysis part of a GIS -- that is, the way you can query it in spatial terms (e.g., what is near what?), create new features (e.g., all the areas that have income over X and lots of trees and are near a freeway) and making calculations based on feature attributes.  What we'll review here is the analytical logic we are implementing with these tools.

Tu 21 Apr

Creating and editing data -- the "Edit Session"

Th 23 Apr

Topic TBA

Tu 28 Apr

Topic TBA

LAB 7 We 29 Apr

LAB: Project Work

Th 30 Apr


Tu 5 May