Meeting: Thursday, 5:30pm – 8:00pm, Gladfelter 336

Instructor Info:

  • Prof. Lee Hachadoorian
  • 104 Gladfelter Hall – In GIS Studio, knock or enter 103A
  • Office Hours:
    • Tues/Wed 12:30 – 2:30pm
    • Thurs 1 – 2pm

General Information

Purpose of the Course

A wide variety of social and environmental phenomena, including crime, urban development, pollution, and commuting, are influenced by or affect people where they live and work. Data gathered by the United States Census Bureau are used across a wide variety of fields for everything from market research to public health research. The purpose of this course is to:

  • Provide significant exposure to and experience with the Decennial Census and American Community Survey
  • Familiarize the student with other Census products
  • Teach a selection of widely used methods for analysis of Census data, and combining census data with other sources of data
  • Teach how to work with Census data products in an automated and reproducible fashion


There are no course prerequisites; however a working knowledge of Windows and basic file management is expected.


This course will make heavy use of free and open source software. All software will be installed on lab computers. Since the software is free, students may also install all software on their own computers or laptops. If you have a laptop that you can bring to class, you may prefer to work on your own laptop, so that you don’t have to shuffle data between lab computers and flash drives. Also, if you prefer working on Mac, the software we use for this course is cross-platform and can be installed on Mac.

Note, however, that sometimes students have laptops that are old and slow, or find that the software we are using doesn’t install correctly. I will try to help you troubleshoot installation issues, but using your own laptop is a convenience. If it reaches the point where using your own laptop is actually less convenient than using university computers, you will not be able to use your own laptop.


Your attendance percentage will also indicate the maximum final grade you can earn in this course. If you miss 3 classes in a once-a-week course, or 6 classes in a twice-a-week course, you have attended 78.6% of class meetings. Accordingly, your final grade will not be higher than a C+, regardless of any other work completed. Please see my attendance policy at my Temple web page:

Note for Summer terms: This course meets twelve times during a six-week term. Missing any class meetings will hamper your ability to complete the work in this course.


Exploring the U.S. Census: Your Guide to America’s Data by Frank Donnelly (SAGE, 2019): This textbook is due to be published in October. The publisher will be providing us online access to digital proofs of the first few chapters. The professor will provide access codes. Once the book is published, you will be expected to purchase it just like any other textbook. The SAGE sales rep will work with us to make sure that it is delivered to the campus store as soon as possible, and in this case I will recommend not attempting to order it through other sellers.

The textbook is priced very reasonably at $45. In addition to the fact that the course will make use of lab exercises in the textbook, there hasn’t really been anything like it available until now. I highly recommend that you purchase it and keep it as a reference for your future academic and professional careers.

A number of exercises will be assigned using DataCamp, an online learning platform for data science:

Assignments and Grading

You will earn points along several tracks. Each track is worth 100 points. Your must progress along ALL tracks to be successful in this course. The tracks are not averaged. Your final grade is the based on the lowest score earned along any track.

For some of the assignments (particularly the lab exercises, and early term project milestones) you will have the opportunity to resubmit the assignment for a higher grade.

Your attendance score is a straight percentage of class sessions you are present for.
Lab Exercises
Most labs will be worth 10 points (approximately one letter grade in your final grade). Due dates and specific points associated with each exercise will be posted to Canvas. Lab exercises which are submitted on time will be allowed one revision.
Term Project
The term project will be completed in milestones. Points will be awarded for each milestone. This is a group project, with groups of 3-4 students. The full specification will be detailed separately, and separate standards will apply to graduate students and undergrads. Graduate students will be expected to be the project leads. Undergraduates will not be evaluated on the project proposal, but will be evaluated on their contribution to the interim and final reports, and their participation in the final presentation.
Student-Led Tutorial
Graduate students will develop a tutorial based on an analytical method they are applying in their term project. This could involve introducing a new dataset, an analysis using data and software we have already discussed, a new R package, a new QGIS plugin, etc. It must be cleared with me beforehand.
Wikipedia Entry (tentative)
This assignment will involve creating or contributing to a Wikipedia article related to an aspect of the Census, other Census data products, or your term project research.

Disability Policy

This course is open to all students who meet the academic requirements for participation. Any student who has a need for accommodation based on the impact of a disability should contact the instructor privately to discuss the specific situation as soon as possible. Contact Disability Resources and Services at 215-204-1280 in 100 Ritter Annex to coordinate reasonable accommodations for students with documented disabilities.

Academic Honesty

All submitted work should be your own. Please read my guide to Academic Integrity at

Classroom Environment

All persons participating in the course should be respectful of other students and the instructor in order to facilitate a civil learning environment. All persons participating in the course have a right to expect respectful treatment in the classroom.

Statement on Academic Freedom

Freedom to teach and freedom to learn are inseparable facets of academic freedom. The University has adopted a policy on Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) which can be downloaded from

Due Dates

Due dates will be posted to Canvas. Labs will generally be expected to be completed in about one week.

Working with Other Students

I encourage students to work together on lab assignments and assist each other in understanding the course material. However, all contents of each student’s lab reports (text and graphics) must be authored solely by that student.


Each lab assignment will indicate how to access data for that assignment. Exercises will typically take longer than one or two lab sessions to complete, so you will need to save incomplete labs so that you may continue to work on them at another time. It is the student’s responsibility to understand how data and projects are saved, and to manage and back up their own data and assignments. Please bring a USB flash drive or external hard drive with you to all class meetings. Storage has gotten so cheap that I would not even consider getting less than a 16GB flash drive, which will probably cost under $10. For each assignment, I suggest you copy all relevant data files to that device in a folder (e.g. named “Lab_01”) and then perform the lab assignments by working off the device.

Students using their own laptops will not have to worry about transferring their project files between lab computers and their personal devices. However, keep in mind that hard drives crash and portable devices go missing. You are responsible for keeping backups of your data. I highly recommend using a “set and forget” backup or filesharing service to store all of your files.

Keep in mind that everyone has access to OwlBox using their TU credentials. The Box Sync app can be installed on your laptop to create a local file that automatically backs up to the cloud.