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:
Co-Instructor, Quantitative Methods:
Valerie Stahl, PhD Candidate in Urban Planning
Office hours available by appointment:
Teaching Assistants (TAs):
Younghyun Kim (email@example.com)
Minh Nguyen (firstname.lastname@example.org)
Michael Snidal (email@example.com)
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
- 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.
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%
Final Project 35%
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.
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?
- 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.
- 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.” http://www.democracyjournal.org/pdf/7/031-043.noveck.final.pdf(Links to an external site.)http://www.democracyjournal.org/pdf/7/031-043.noveck.final.pdf (Links to an external site.)
R Statistical Software Resources:
- R Code School
- R Studio
- UCLA Institute for Digital Research and Education
*IMPORTANT* Begin Familiarizing Yourself with R Statistical Software
Week #2: September 11: Research Design and Identifying Methods
- 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.
- 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
- MacDonald, Heather. "The American Community Survey." Journal of the American Planning Association 72, 4: 491-503.
- Pratt, Beverly M. et al. “Measuring Race and Ethnicity Across the Decades: 1790-2010.” https://www.census.gov/newsroom/blogs/random-samplings/2015/11/measuring-race-and-ethnicity-across-the-decades-1790-2010.html(Links to an external site.)
- Pew Research Center. 2015. “What Census Calls Us: A Historical Timeline.” http://www.pewsocialtrends.org/interactives/multiracial-timeline/
- Census Bureau. 2017. “State Data Centers Are Key to Accessing and Understanding Census Bureau Data.” https://www.census.gov/newsroom/blogs/random-samplings/2017/06/state_data_centersa.html(Links to an external site.)
- Census Bureau. 2009. A Compass for Understanding and Using American Community Survey Data: What Researchers Need to Know (U.S. Government Printing Office: Washington, DC). https://www.census.gov/content/dam/Census/library/publications/2009/acs/ACSResearch.pdf(Links to an external site.)
(Explore) Columbia University Digital Social Science Center http://library.columbia.edu/locations/dssc/data.html
(Explore) NYU Furman Center CoreData.nyc http://coredata.nyc (Links to an external site.)
Week #4: September 25: Defining Planning Problems
- 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
- 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)
- 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
- 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
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.https://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?pagewanted=all
Harmon, Amy. “As Cameras Track Detroit’s Residents, a Debate Ensues Over Racial Bias. 8 July 209. https://www.nytimes.com/2019/07/08/us/detroit-facial-recognition-cameras.html
Kolata, Gina. “The Myth, the Math, the Sex.” 12 Aug. 2007. New York Times. https://www.nytimes.com/2007/08/12/weekinreview/12kolata.html
Oder, Norman.”Oft-Quoted Studies Saying Gentrification Doesn’t Cause Displacement Are “Glaringly Stale.” 2 Jan. 2018. Shelterforce. https://shelterforce.org/2018/01/02/gentrification-doesnt-cause-displacement-some-datas-gotten-stale/
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
Chapter 3 in Agresti & Finlay
“Gentrification Did Not Displace NYC’s Most Vulnerable Children,” 31 Jul. 2019, CityLab,https://www.citylab.com/equity/2019/07/gentrification-displacement-link-children-nyc-medicaid-data/594250/
Chapters 6 in Wright
Week #10: November 6: Probability, Sampling/Normal Distributions, and Confidence Intervals
Chapter 4-6, Agresti & Finlay
Dash, Garrett. “What Micro-Mapping a City's Density Reveals,” 9 July 2019, CityLab. https://www.citylab.com/perspective/2019/07/urban-density-map-city-population-data-geography/591760/?utm_medium=social&utm_term=2019-07-09T14%3A04%3A09&utm_content=edit-promo&utm_source=twitter&utm_campaign=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
Chapter 7-8, Agresti & Finlay
Fry, Hannah. (2019). "What Statistics can and can't tell us about ourselves." The New Yorker accessed online: https://www.newyorker.com/magazine/2019/09/09/what-statistics-can-and-cant-tell-us-about-ourselves
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!
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. https://doi.org/10.1080/00330120902931952
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!
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