Course Syllabus

PLANA 4208: Planning Methods

Professor Malo Hutson

Co-Instructor Valerie Stahl

Fall 2019 Syllabus

Course: Planning Methods Call # 41413  (3 points)

Time: Wednesdays 9 am – 11 am

Room: 209 Fayerweather


Professor Hutson’s Office: 305 Buell Hall

Professor Hutson’s Office Hours: Wednesdays, 3:30-5 p.m. 

Online office hours sign-up link: (Links to an external site.)



Co-Instructor, Quantitative Methods:

Valerie Stahl, PhD Candidate in Urban Planning

Office hours available by appointment:


Teaching Assistants (TAs):

Younghyun Kim (

Minh Nguyen (

Michael Snidal (


Course Description:

This is an introductory course designed to help prepare students for common analysis methods used in planning practice. Common methods of analysis are covered using publicly available data sets and data collected through assignments. Through weekly readings, lectures and lab sessions students will gain a basic understanding of the tools and skills required in planning practice. In addition to the lecture, students must register for one of the three weekly lab sections below taught by TAs.

Lab Section 001                                                                  Call # 91346

Mondays 2-4pm, UP Computer Lab

Lab Section 002                                                                  Call # 92346

Mondays 4-6pm, UP Computer Lab

Lab Section 003                                                                  Call # 92846

Thursdays 4-6pm, UP Computer Lab


Course Objectives:

  • Identify planning problems and questions
  • Design and implement a research project in response to a planning problem or question
  • Understand how to use secondary data to address planning problems and questions, and become familiar with the primary data sources and metrics used in planning practice
  • Become a critical consumer of statistics, methods, and evidence/arguments in the press and in policy, planning and advocacy publications
  • Think critically about research problems and research design, learn what kinds of problems planners address in day-to-day life, and recognize the role of theory in shaping both questions and research design
  • Prepare clear, accurate and compelling text, graphics and maps for use in documents
  • Learn how to write for different audiences, and effectively include data/evidence in writing
  • Gain literacy in basic descriptive and inferential statistical analysis
  • Learn a basic understanding of R, an open-source statistical coding software that allows you to process large datasets commonly used in planning.


Course Requirements:

Students are required to attend all lectures and lab sections for the entire semester.   In addition, students will complete weekly computer lab assignments and take a midterm that is scheduled for Wednesday, October 16, 2019.  Finally, students will be assigned a group and will complete an analysis of a New York City community district/neighborhood.  The final analysis will be in report format and presented in class on the last day of instruction Wednesday, December 4, 2019 and the final report will be due Sunday, December 15, 2019.   Information regarding the class project will be handed out in class once the semester begins.    


Grades will be based on the following:

Attendance and In-Class Exercises             15%

Computer Labs                                                        25%

Midterm                                                                      25%

Final Project                                                             35%

Class Attendance:

Students are expected to make every effort to attend lectures and discussion sections.  Please be on time to class and computer labs.   Attendance in lecture and computer labs  will be taken.

Policy on Religious Holidays:

If you will be observing any religious holidays this semester that will prevent you from attending a regularly scheduled class or interfere with fulfilling any course requirement, notify Professor Hutson or TAs within the first two weeks of the semester. Otherwise, any absence due to a religious holiday will be treated as a missed class. 

Important Dates

Midterm Exam: Wednesday, October 16, 2019

Final Presentation: Wednesday, December 4, 2019

Final Project Report Due: Sunday, December 15, 2019


Statement of Academic Integrity:

Any test, paper or report submitted by you and that bears your name is presumed to be your own original work that has not previously been submitted for credit in another course unless you obtain prior written approval to do so from Professor Hutson.

In all of your assignments, including your homework or drafts of papers, you may use words or ideas written by other individuals in publications, web sites, or other sources, but only with proper attribution. "Proper attribution" means that you have fully identified the original source and extent of your use of the words or ideas of others that you reproduce in your work for this course, usually in the form of a footnote or parenthesis.

As a general rule, if you are citing from a published source or from a web site and the quotation is short (up to a sentence or two) place it in quotation marks; if you employ a longer passage from a publication or web site, please indent it and use single spacing. In both cases, be sure to cite the original source in a footnote or in parentheses. 

If you are not clear about the expectations for completing an assignment or taking an examination, be sure to seek clarification from Professor Hutson or your assigned TAs beforehand.

Finally, you should keep in mind that as a member of the campus community, you are expected to demonstrate integrity in all of your academic endeavors and will be evaluated on your own merits. So be proud of your academic accomplishments and help to protect and promote academic integrity at Columbia University. The consequences of cheating and academic dishonesty - including a formal discipline file, possible loss of future internship, scholarship, or employment opportunities, and denial of admission to another graduate program - are simply not worth it.

Students with Disabilities:

If you need accommodations for any physical, psychological, or learning disability or if you want us to have emergency medical information, please speak to us after class or during office hours.

Required Reading for Course:

For the first two portions of the course, readings will be posted as PDFs onto Canvas. There are two texts used for the quantitative course:  

Quantitative Methods and Statistics: A Guide to Social Research by Sonia R. Wright. Will be uploaded to Canvas- can also purchase used for $5 on Amazon.

Statistical Methods for the Social Sciences: 4thEd. By Alan Agresti & Barbara Finlay. Will be uploaded to Canvas.


Course Content and Reading Schedule

I) Defining Planning, Identifying Problems and Conducting Field Research

Week #1: September 4: Introduction to the Course and What is Planning? 

Required Readings:

  • Forester, J. “Introduction: Renewing Planning Practice by Fostering Public Deliberations in an Adversarial World,” in The Deliberative Practitioner.
  • Escobar, Arturo. "Planning," in The Development Dictionary: A Guide to Knowledge as Power. Edited by Wolfgang Sachs. London: Zed Books, 2010, pp. 132-45. ISBN: 9781848133808.

Recommended Readings:

  • Young, Iris Marion. "Chapter 8-City Life and Difference," in Justice and the Politics Difference. Princeton University Press, 1990.
  • Susskind, Lawrence, and Jennifer Thomas-Larmer. "Conducting A Conflict Assessment." Chapter 2 in The Consensus Building Handbook. Thousand Oaks, CA: Sage, 1999, pp. 99-136. ISBN: 0761908447.
  • Sandercock, Leonie. “Introduction”, in Making the Invisible Visible: A Multicultural Planning History. Los Angeles: University of California Press, 1998, pp. 1-33.
  • Allison Arieff. “What Tech Hasn’t Learned from Urban Planning,” NY Times. December 13, 2013.
  • Robert Reich: "Policy Making in a Democracy," in The Power of Public Ideas, Reich, ed..  Cambridge: Ballinger, l988. 123-155.
  • Noveck, B. 2008. “Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful.” to an external site.) (Links to an external site.)


R Statistical Software Resources:

  • R Code School (Links to an external site.)

  • R Studio (Links to an external site.)

  • UCLA Institute for Digital Research and Education (Links to an external site.)

  • R-Bloggers (Links to an external site.)

 *IMPORTANT* Begin Familiarizing Yourself with R Statistical Software


Week #2: September 11: Research Design and Identifying Methods

Required Readings:

  • Creswell, John W. (2014). “Chapter 1: The Selection of a Research Approach,” in Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4th Edition. Los Angeles: Sage Publications, Inc.  3-22.

Recommended Readings:

  • Babbie, Earl.   The Practice of Social Research. 14thBoston: Cengage Learning. 
  • Berg, Bruce and Howard Lune. Qualitative Research Methods for the Social Sciences. 8thBoston: Pearson.


Week #3: September 18:  Accessing Data and Understanding Planning Agencies

Required readings:


Recommended Resource:

(Explore) Columbia University Digital Social Science Center

(Explore) NYU Furman Center (Links to an external site.)


Week #4: September 25: Defining Planning Problems

Required readings:

  • Hayden, D. 1997. Chapter 1: Contested Terrain, Chapter 2: Urban Landscape History: The Sense of Place and the Politics of Space. In Power of Place. Cambridge, MA: MIT Press. pp. 2-43.
  • Klass, Gary M. 2012. “Chapter 1: Measuring Political, Social and Economic Conditions,” in Just Plain Data Analysis (Plymouth: Rowman and Littlefield).
  • Sawicki, David. S. and Patrice Flynn. “Neighborhood Indicators: A Review of the Literature and an Assessment of Conceptual and Methodological Issues,” Journal of the American Planning Association 62(2): 165- 183. 

II) Qualitative Methods

Week #5: October 2:  Introduction to Qualitative Research Methods

Required readings:

  • Berg, Bruce and Howard Lune. “Chapter 2: Designing Qualitative Research,” in Qualitative Research Methods for the Social Sciences.  8thBoston: Pearson.  Pp. 19-60. 
  • Robert S. Weiss. 1994. “Introduction,” “Respondents: Choosing them and recruiting them,” and “Interviewing.” In Learning from strangers: The art and method of qualitative interview studies. New York: The Free Press. 1-14, 15-38, 61-120.
  • Corburn, Jason. 2005. Street science: Characterizing local knowledge. In Street science: Community knowledge and environmental health justice. Cambridge: MIT Press. 47-77.
  • Minkler, M. 2000. Using participatory action research to build healthy communities. Public Health Reports 115 (2–3) 191–97.

Week #6: October 9:  Qualitative Research Methods (Continued)

Required readings:

  • Hutson, Malo. “Chapter 5-San Francisco: The Fight to Preserve the Mission District,” in The Urban Struggle for Economic, Environmental, and Social Justice: Deepening Their Roots.  London and New York: Routledge.  Pp. 85-119
  • Frick, Karen T. 2014. The actions of discontent: Tea Party and property rights activists pushing back against regional planning. Journal of the American Planning Association 79 (3) 190–200.
  • Yin, Robert. 2014. “Chapter 1- “Getting Started: How to Whether and When to Use Case Study as a Research Method,” in Case Study Research:Design and Methods.  5thLos Angeles: Sage Publications, Inc.  pp. 3-26

Recommended Readings:

  • James Spradley. 1979. “Interviewing an informant” and “Asking descriptive questions.” In The ethnographic interview. New York: Holt, Rinehart and Winston, pages 55-68, 78-91.

Week #7: October 16: Midterm

III) Quantitative Methods

Week #8: October 23: Introduction to Statistical Analysis for Planning: The Six Questions of Quantitative Research + Introduction to Sampling and Measurement


  • Why are statistics important for planning?
  • What are the basics of data analysis? What is ‘big data’?
  • When should you use different types of statistical analysis (descriptive v. inferential) to answer planning questions?
  • Who collects data? Who do we sample to draw conclusions about a given population? Understand human element of data collection
  • How do we know that the data we sample is reliable?
  • Where do we access data sources commonly used in planning?
  • Download R and tutorial on how to open up datasets


Required Readings:

Chapter 1 and 2 in Agresti and Finlay

Chapter 3 in Wright

Vance, Ashlee. “Data Analysts Captivated by R’s Power,” 6 Jan. 2009, The New York Times.

Harmon, Amy. “As Cameras Track Detroit’s Residents, a Debate Ensues Over Racial Bias. 8 July 209.

Recommended readings:

Kolata, Gina. “The Myth, the Math, the Sex.” 12 Aug. 2007. New York Times.

Oder, Norman.”Oft-Quoted Studies Saying Gentrification Doesn’t Cause Displacement Are “Glaringly Stale.” 2 Jan. 2018. Shelterforce.


Week #9: October 30:   Descriptive Statistics: Describing one or more variables 


  • Discussion of when descriptive statistics would be useful for planning problems

  • Relative frequencies for one or more variables

  • Review of distributions, measures of center that are commonly depicted in table form

  • Measures of variability and dispersion across one or more variables

  • Changes over time, thinking about how data is presented

  • Learn how to calculate descriptive stats in R

Required readings:

Chapter 3 in Agresti & Finlay     

“Gentrification Did Not Displace NYC’s Most Vulnerable Children,” 31 Jul. 2019, CityLab,

Recommended reading: 

Chapters 6 in Wright      

Week #10: November 6: Probability, Sampling/Normal Distributions, and Confidence Intervals 


■Probability and Z-scores
■Sampling distributions and standard errors
■Methods of estimation: refresher 
■Confidence Intervals for Proportions
■T-scores and CIs for Means
■Choice of sample size
■5 parts of significance tests
■Intro to graphics and t-tests in R

Required readings:

Chapter 4-6, Agresti & Finlay


Dash, Garrett. “What Micro-Mapping a City's Density Reveals,” 9 July 2019, CityLab.

Week #11: November 13: Introduction to Inferential Statistics: Hypothesis Testing, Comparing Two Groups


  • Difference of means tests, difference of proportion tests
  • Classifying Type I and II errors and determining statistical significance
  • Chi-squared test for independence
  • Analysis of variance
  • Learn other tests for statistical significance (paired differences)
  • Be wary consumers of statistical significance!
  • Conduct statistical tests for significance in R

Required Readings:

Chapter 7-8, Agresti & Finlay

Fry, Hannah. (2019). "What Statistics can and can't tell us about ourselves." The New Yorker accessed online:

Week #12: November 20: Inferential Statistics Cont.: Introduction to Ordinary Least Squares (OLS) Linear Regression


  • Understand the basis for linear relationships, and when we want to use them as planners
  • Be able to interpret a OLS prediction equation and a linear regression model
  • Understand what correlation means (NOT CAUSATION!), inferences for slope and correlation
  • Comprehend the common assumptions and violations of linear regressions
  • Introduction to multivariate regression analysis, dummy variables
  • R: conduct an OLS Linear regression
  • Leave the classroom being critical consumers of data and careful producers of it– endwith linear regression, don’t start with it!

Required readings:

Chapter 9, Agresti & Finlay

“Is Soda A Smoking Gun For Teen Violence - Or Just Statistical Illiteracy?” 2 Nov. 2011, Forbes.

Wyly, E. (2009). Strategic Positivism. The Professional Geographer, 61(3), 310–322.

Week #13: November 27: Thanksgiving Holiday, NO CLASS

Week #14: December 4:  In-Class Final Presentations

Week #15: Friday, December 13: Final Report Due by 11:59 pm!


Course Summary:

Date Details