QUANTITATIVE METHODS FOR URBAN PLANNING
2018/2019, Semester 2
School of Design and Environment (Dean's Office (School Of Design & Env))
Modular Credits: 4
Working with quantitative data is common in the planning profession. This module provides planning students with an introduction to the quantitative methods and techniques used in planning practice and urban research. It will prepare students to conduct basic statistical analysis of data themselves as well as to critically review analyses prepared by others. The emphasis is on how to develop sound arguments and research design, such that students appreciate both the power and limitation of quantitative analysis in planning discussions. As a result of this module, students will learn:
To develop statistical skills for the description and comparison of sets of planning data;
To identify the sources of data most frequently used by urban planners;
To be equipped with a variety of quantitative tools used to test hypotheses and generate estimates;
To generate variables, perform linear regressions, and interpret the results;
To critically review quantitative analyses and assess the validity of arguments made therein;
To be familiar with real world practices of quantitative analysis in the planning profession;
To be able to use statistical packages for the diverse quantitative analyses.
Must be taken together with DEP5103 Urban Planning Studio
: The students can learn the core subject knowledge and relevant urban planning examples.
: The students can learn how to use the statistic software program (STATA) to conduct empirical analysis.
: The students and the lecturer can work together and conduct the group project work.
NOTE: The lectures and workshops are conducted in SR12. The lab sessions are conducted in ES2. The actual class time is Friday 2 pm - 5 pm. The venues are reserved for 5 pm - 6 pm as well, so that you can conduct self-direct learning or group project discussion if you wish.
Assignment #/ Due Dates
1 Jan 14-18
No class (Design-workshop week)
2 Jan 21-25
Introduction to the module. Assignments and project brief.
Lecture 1: Preview
3 Jan 28-Feb 1
Lecture 2: Concepts of probability and statistics I
Lecture 3: Concepts of probability and statistics II
4 Feb 4-8
*CNY5&6 Feb Tue&Wed
Lecture 4: Research design
Project workshop 1: Presentation of MUP Year 2 students
5 Feb 11-15
Lecture 5: Hypothesis tests I
Lab session 1: STATA
6 Feb 18-22
Lecture 6: Linear regression I
Project workshop 2: Brainstorm the topic.
Submit the one-page proposal by Sat 23 Feb.
RECESS WEEK FEB 23 – MAR 3 2019
7 Mar 4-8
No class (MUP field trip)
8 Mar 11-15
Lecture 7: Hypothesis tests II
Lab session 2: STATA
HW1 due on Mar 18
9 Mar 18-22
Lecture 8: Linear regression II
Project workshop 3: Discuss preliminary results and sharpen the research.
10 Mar 25-29
Lecture 9: Diagnostic tests and solutions
Lab session 3: STATA
HW2 due on Apr 1
11 Apr 1-5
Lecture 10: Recapitulation
Project workshop 4: Discuss and polish results.
Lab session 4: STATA
12 Apr 8-12
Project workshops 5 & 6: Group project presentation
Upload the slides to IVLE by Tue 9 April
13 Apr 15-19
*Good Friday 19 Apr Fri
No class (Good Friday)
Project final report due on Apr 18
READING PERIOD 20-26 APRIL 2019
Tuesday, 7 May (Morning)
Gujarati, D.N., Porter, D.C. (2009)
Gujarati, D.N., Porter, D.C. (2010)
Essentials of Econometrics.
Sullivan, M. (2013)
Statistics: Informed Decision Using Data.
This textbook can be viewed online through NUS Library.
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week