GUS 5073 – Geovisualization

  • Instructor: Prof. Hachadoorian
  • Office Location: Gladfelter 334
  • Email:
  • Office Phone: 215.204.3331
  • Office Hours: M 1-3pm, W 4-5pm

Course Description

Maps can be powerful devices for communication, but also tools for exploration of relationships among social and physical processes manifesting in space. This computer-intensive course will address this dual purpose of maps as tools for visual communication and visual thinking. We will review traditional principles of cartography such as layout, typography, and color, subjects which are covered in greater depth in GUS 8065 – Cartographic Design. We will learn how to use maps interactively for exploratory analysis, as well as how to build and disseminate interactive maps for others to explore.

The course will emphasize the use of free and open source software (FOSS). The course will be taught in a computer lab with the necessary software installed. However, if you have access to a laptop, you may find it easier to install the free software and do most of your work on your personal machine.

Although technology changes at a rapid pace, some softwares and programming languages that we will very likely use include R, Python, D3.js, GeoDa, and QGIS.

Course Goals

This course is about cartography and geovisualization, and a significant part of the course will be devoted to acquiring specific technical skills related to specific software packages. But more broadly, I intend for you to learn the art of data visualization, both in terms of how to use visualization to understand your data, and how to use visualization to create powerful and effective communication.

As in any of my classes, you will also be learning how to learn. Therefore the course will include hands-on tutorials, geohacking, team programming, and exploration of online learning resources.

We will be using many different software tools, some of which will be used throughout the term, and some of which we will only use once or twice. So another significant goal of the course is to give you hands-on exposure to a wide variety of tools, and have you learn how to find, test, and teach new software to yourselves and each other. I believe this will give you valuable experience into how actual research takes place in academia and industry, where tools and standards are constantly changing, and it is not enough to learn one software.

Above all I hope to imbue in you the hacker ethos, which I would summarize, as try, fail, try again.

Learning Objectives

By the end of this course, students will be able to:

  • Understand principles of symbolization and which visual variables to use for what purposes
  • Create traditional statistical visualizations such as histograms and scatterplots
  • Create effective visualizations of geographic data
  • Combine statistical and geographic visualization effectively
  • Use interactive visualization to explore spatial data sets, including using linking and brushing
  • Represent temporal data in maps, including map animations
  • Create publishable interactive visualizations using tools such as R and D3.js


A previous course in statistics (such as GUS/ENST 3161) is required. A previous course in GIS or cartography will be helpful, as will previous exposure to a programming language.



Thematic Cartography and Geovisualization, 3e, Slocum, et al. (Prentice Hall)
This book is expensive, but is a valuable reference worth keeping on your shelf. Additionally, it is the required text for GUS 8065.
Visualize This, Yau (Wiley)
This book is available as a free PDF through the Temple Library (hyperlinked above). Additionally, the printe version is fairly inexpensive.
Exploring Spatial Data with GeoDa: A Workbook, by Anselin
This workbook is for an old version of the software. It is currently being updated, and the first chapter of the forthcoming version is available at the GeoDa Documentation page. Most of the functionality is similar, although menus may not be in the same place.


R in Action, 2e, Kabacoff (Manning)
I will not be assigning specific readings from this book. However, it will be an excellent reference to have at hand. The author also maintains, the Quick-R website, which covers similar material (though not as in-depth), and which I will refer you to during the term.


Lab Exercises – 20%

Method Presentation 20%

The students will work in small teams with the professor to explore a tool or visualization method not covered in the previous material. They will develop this visualization using the County Election Data, and present a tutorial to the other students demonstrating and instructing in its use.

Term Project – 60%

We will be working with County Election Data available at Students will also be responsible for identifying other county-level data of interest that can be analyzed with the lection data, to explore the results of the recent US Presidental Election. The term project will be comprised of the following:

Portfiolio (20%)
As they learn various skills and techniques, students will apply those techniques to the election data, to develop and explore hypotheses. They will publish occasional short pieces in a portfolio throughout the term.
Report (40%)
At the end of the term, the student will produce a final report in written or online format, including a range of visualizations, and an argument about factors influencing the election outcome.

Spring 2017 Schedule

Module 0


  • Preparation: Slocum 1; Yau Introduction, 1

Module 1

Basic Statistical and Cartographic Visualization

Class Meetings: Jan 19, Jan 26, Feb 2, Feb 9

  • Preparation: Slocum 3; Yau 4, 6
  • Exercises: Geoda 1, 2, 3; Yau 4, 6 – only parts using R (not Illustrator); Choropleth Mapping with R
  • Portfolio: Identify county data that will be of interest to analyze with election data. Code download and data cleaning in R.

Module 2

Interactive Visualization

Class Meetings: Feb 16, Feb 23

  • Preparation: Slocum 22
  • Exercises: GeoDa 7, 8
  • Portfolio: Visualizations of county election data

Module 3

Multivariate Visualization and Cartography

Class Meetings: Mar 2, Mar 9

  • Preparation: Slocum 18; Yau 7
  • Exercises: GeoDa 9, 10; Yau 7 (exc. Chernoff Faces, 238-243)
  • Portfolio: Heatmap, Star Chart, and PCP of County Election Data

Module 4

Advanced and Publishable Interactive Visualization

Class Meetings: Mar 23, Mar 30

  • Preparation: Slocum 24; Yau 5, 8
  • Exercises: GeoDa 11, D3.js
  • Portfolio: RShiny and D3.js explorations of County Election Data

Module 5


Class Meetings: Apr 6

  • Preparation: Slocum 21
  • Exercises: GeoDa 12; Animation with R

Module 6

Selected Topics and Project Time

Class Meetings: Apr 13, Apr 20, Apr 27

The final part of the course will be devoted to topics agreed upon by the class, student presentations, and continued work on the portfolio and final project.