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DEP5103A 

QUANTITATIVE METHODS FOR URBAN PLANNING
   2016/2017, Semester 2
   School of Design and Environment (Dean's Office (School Of Design & Env))
Modular Credits: 4
  Tags: --

Learning Outcomes

TopWorking with quantitative data is common in the planning profession. Developing the skill of expressing statistical ideas in a clear and simple language is essential for effective urban planning practices. This module provides the students in the Master in Urban Planning 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. 
  1. To develop statistical skills for the description and comparison of sets of planning data;
  2. To identify the sources of data most frequently used by urban planners;
  3. To be equipped with a variety of quantitative tools used to test hypotheses and generate estimates;
  4. To generate variables, perform linear regressions, and interpret the results;
  5. To critically review quantitative analyses and assess the validity of arguments made therein;
  6. To be familiar with real world practices of quantitative analysis in the planning profession;
  7. To be able to use statistical packages for the diverse quantitative analyses.

Prerequisites

TopMust be taken together with DEP5103 Urban Planning Studio

Teaching Modes

Top
 1. Classes: Each class will be organized into lectures on the core subject and discussions of relevant urban planning examples as well as hands-on lab exercises of statistical packages. The lecture is scheduled on Wednesday, 10am-1pm in SDE SR14, but we will occasionally move to SDE CR4 for the lab sessions. There will be also several guest speakers sharing the real-world experience of using quantitative methods in urban planning research. 

2. Module Requirements: The grade will be based on four components: problem sets, a final exam, an analysis project, and class participation.
 
 1) Problem Sets: Four problem sets will account for 20% of the course grade. Unless otherwise stated, all problem sets are DUE one week after distribution. It will usually be possible to receive credit for problem sets that are up to one week late. After that date, no credit will be given for late problem sets. You are encouraged to work on problem sets in groups, but each individual is required to complete his/her own problem set. Copying the answers from someone else’s problem set is plagiarism and will be treated as such.
 
2) Final Exam: There will be a final exam at the end of the semester.
 
3) Planning Analysis Report Using Quantitative Methods: Students, working in small groups (max. 3 students), will use quantitative method(s) to address a real planning problem. Select a problem/decision of interest to you, and formulate the problem and construct hypotheses to be tested, collect the data, perform the statistical analysis, and write up your results. You may use data that have been collected and/or analyzed by other researchers, but you must clearly differentiate your analysis from their work. You are allowed to choose one or more statistical techniques that you have learned from the module (descriptive stats, comparison of means, correlation, linear regression, and etc.).

4) Class participation.
 
3. Computer Requirements: You will need access to a statistical software package (e.g., STATA, SAS, SPSS) for use in this module. Although you may use any package with which you are familiar for class assignments, the statistical labs during the lecture will be based on STATA.
 

Schedule

TopWeekly Module Outline
 
Week Dates Lecture (10am-1pm)
1. Jan 11 No Class (Due to MUP Field Trip)
2. Jan 18 Introduction and Expectations: Introduction to statistics and its application to urban planning; Graphical presentation of quantitative results
Descriptive Statistics: Sample and population; Central tendency
(mean/median/mode); Dispersion (variance, standard deviation);
Correlation
3. Jan 25 Probability I: Introduction to probability and probability distribution; Random events; Bayes’ Rule; Binomial/Normal/Poisson distribution
       · Lab session 1: STATA
4. Feb 1 Probability II: Interpretation and application of the normal probability distribution; Population distribution vs. sampling distribution; Z score
      ·  Lab session 2: STATA
  1. Feb 8 (HW1Due)
Statistical Inference: Differentiation of a sample and a population; Confidence interval; Significance tests
      ·  Lab session 3: STATA
6. Feb 15 Research Design: Potential topics; Research process; Data collection; Data organization
   ·  Presentation of MUP Year 2 Students
Feb 18 - Feb 26 MID SEMESTER BREAK
7. Mar 1 (HW2 Due) Hypotheses Concerning a Single Population: Logic of hypothesis testing; Definition of research/null hypotheses; Type I and Type II errors
      · Lab session 4: STATA
8. Mar 8 Hypotheses Comparing Two Populations: Inferences for two population means; Comparing proportions from independent samples
      · Lab session 5: STATA
9. Mar 15       (HW3 Due) Additional Hypothesis Tests: One-way ANOVA (Analysis of Variance); F-distribution; Association between categorical variables
   · Lab session 6: STATA
10. Mar 22 Linear Regression I: Describing the relation between two variables; Introduction to simple, linear regression analysis
      · Paper study
11. Mar 29      (HW4 Due) Linear Regression II: Interpretation of a coefficient of regression and correlation; Multiple regressions; Recognition of the limitations of regressions
      · Paper study
12. APR 5 Course Review
   · Lab session 7: Group projects consultation
13. APR 12 Project Presentation
 
 

Assessment

Top20%  Problem Sets (Due: one week after distribution)
 
40%  Final Exam (During the examination period)
 
30%  Planning Analysis Project Using Quantitative Methods (Presentation on April 12, report due on April 12)

10%  Class Participation
 

Textbook and Readings

TopTextbook
 Sullivan, Michael. Statistics: Informed Decisions Using Data. 4th Edition. Pearson. (Available at CL PBR: QA276.12 Sul 2007)
 
Additional Resources
1. Weiss, Neil. A. Introductory Statistics. 9th Edition. Pearson. (Available at CL PBR: QA276.12 Wei 2012) 

2. A set of instructional videos that were created by PBS a number of years ago called Against All Odds: Inside Statistics. They are very good resources for reviewing or reinforcing statistical concepts. They may be accessed at: http://www.learner.org/resources/series65.html?pop=yes&pid=3148
 
3. Other articles, reports, and web sites demonstrating the use in urban planning settings of the statistical techniques taught in the module will be posted on IVLE.

Reading List
 
Week Dates Reading List
1. Jan 18 Introduction and Expectations:
1. Sullivan Chapter 1 Data Collection; Chapter 2 Organizing and Summarizing Data
2. Weiss Chapter 1 The Nature of Statistics
3. Against All Odds #1 What is Statistics #2 Stemplots #3 Histograms
Descriptive Statistics:
1. Sullivan Chapter 2 Organizing and Summarizing Data; Chapter 3 Numerically Summarizing Data; Chapter 4 Describing the Relation between Two Variables
2. Weiss Chapter 3 Descriptive Measures
3. Against All Odds #4 Measures of Center #5 Boxplots #6 Standard
2. Jan 25 Probability I:
1. Sullivan Chapter 5 Probability; Chapter 6 Discrete Probability Distributions
2. Weiss Chapter 4 Probability Concepts; Chapter 5 Discrete Random Variables
3. Against All Odds #13 Two-Way Tables #18 Introduction to Probability #19 Probability Models #20 Random Variables #21 Binomial Distributions
3. Feb 1 Probability II:
1. Sullivan Chapter 7 The Normal Probability Distribution; Chapter 8 Sampling Distributions
2. Weiss Chapter 6 The Normal Distribution
3. Against All Odds #7 Normal Curves #8 Normal Calculations #9 Checking Assumption of Normality #22 Sampling Distribution
4. Feb 8 Statistical Inference:
1. Sullivan Chapter 9 Estimating the Value of a Parameter
2. Weiss Chapter 7 The Sampling Distribution of the Sample Mean; Chapter 8 Confidence Intervals for One Population Mean
3. Against All Odds #24 Confidence Intervals #25 Test of Significance
5. Feb 15 Research Design:
1. Sullivan Chapter 1 Data Collection; Chapter 2 Organizing and Summarizing Data
2. Weiss Chapter 1 The Nature of Statistics; Chapter 2 Organizing Data
3. Against All Odds #15 Designing Experiments #16 Census and Sampling #17 Samples and Surveys
6. Mar 1 Hypotheses Concerning a Single Population:
1. Sullivan Chapter 10 Hypothesis Tests Regarding a Parameter
2. Weiss Chapter 9 Hypothesis Tests for One Population Mean
3. Against All Odds #26 Small Sample Inference for One Mean
Feb 20 - Feb 28 MID SEMESTER BREAK
7. Mar 8 Hypotheses Comparing Two Populations:
1. Sullivan Chapter 11 Inferences on Two Samples
2. Weiss Chapter 10 Inferences for Two Population Means
3. Against All Odds #27 Comparing Two Means
8. Mar 15 Additional Hypothesis Tests:
1. Sullivan Chapter 12 Inferences on Categorical Data Chapter 13 Comparing Three or More Means
2. Weiss Chapter 13 Chi-Sqaure Procedures Chapter 16 Analysis of Variance (ANOVA)
3. Against All Odds #29 Inference for Two-Way Tables #31 One-Way ANOVA
9. Mar 22 Linear Regression I:
1. Sullivan Chapter 4 Describing the Relation between Two Variables
2. Weiss Chapter 14 Descriptive Measures in Regression and Correlation 3. Against All Odds #10 Scatterplots #11 Fitting Lines to Data #12 Correlation
10. Mar 29 Linear Regression II:
1. Sullivan Chapter 14 Inference on the Least-Square Regression Model and Multiple Regression
2. Weiss Chapter 14 Descriptive Measures in Regression and Correlation; Chapter 15 Inferential Methods in Regression and Correlation
3. Against All Odds #14 The Question of Causation #30 Inference for Regression
 

 

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