GENERATING AND INTERPRETING EVIDENCE FOR EVIDENCE-BASED DECISION IN URBAN ENVIRONMENTS
A.K.A. PRINCIPLES OF URBAN SCIENCE - UDel 20
Class website http://fbb.space/PUS2020/
This course will teach you the basis of data driven research on urban environments.
You will acquire basic computational skills, basic knowledge of statistical analysis including traditional methods and machine learning, error analysis, good practises for handling data and big-data, and communication and visualization skills. After this class you should be able to formulate a question relevant to Urban Science, findan appropriate data to answer the question, prepare and analyze the data, get an answer, to whichever confidence level, and communicate your answer, and your confidence level in the answer.
This is an interdisciplinary class that combines technical and domain skills. Don't worry about how much you already know, especially do not compare it to what other students know: all skills and perspectives will come together to solve problems.
The course will be organized in a modular fashion, with some guest lectures.
Meeting pattern: M-W 3:30-4:50PM online at
The instructors is: Dr. Federica Bianco fbianco@udel.edu
office hours: Tentatively: Tuesday 1200-2PM (or by appointment).
Books: These books are available online, for free.
While there is not a single book that supports the material we will cover in this class, several textbooks can help you through the class primary textbooks are:
Also these may be helpful
Each week you will attend one lecture and one lab session. Classes are synchronous and attendance in lecture and lab is mandatory. The material will be covered in the slides handed out every week and, but participating to the class and the discussions therein will be crucial for your development in the class.
By the end of this class you should be able to formulate an appropriate analysis plan for a research question, select, gather, and prepare data for analysis, and choose and apply machine learning methods to the data.
Technology
Google Collaboratory will be used for the class. Homework can be developed on any platform as long as the computational set up is consistent for the entire class: I need to be able to reproduce your work and obtain the same results on google collab. Modules and library used in your work need to be accessible to me, the graders, and your classmates.
The course will be organized in a modular fashion, with some guest lectures. Each statistical and machine learning method will be studied as it is applied to a physical problem, based on open data and literature examples.
Homework will be exclusively received through github.
Homework projects must be turned in as iPython notebooks by checking them into your github account in the PUS2020_<fistinitialLastname>/HW<hwnumber>_<fistinitialLastname> repo (unless otherwise stated).
Grades are based on
Weekly assignments will be handed out at the end of the class, and will be due strictly before the first class of the following week on Monday 12:00PM Noon unless otherwise stated (no submissions at all can be accepted after that as the homework may be reviewed in class).
Before each class there will be a set of questions to be answered about the material covered in previous lectures. This serves to assure you review the material as we go, and to assure that a topic was generally understood by the class before we move to the next one. In other words, this is more an assessment of how clear I was at teaching something than anything else. Nonetheless, to enforce a regular review of the material, the pre-class questions will be graded. Please come to class on time: at the beginning of each class you will be handed a sheet of “Pre-class Questions” to be answered before each lecture and lab. You will have up to 7 minutes after the official start time of the class to answer them. The later you arrive at the class, the less time you will have to answer the questions.
Late homework will not be accepted. A single 72-hour exception is allowed throughout the semester, explicitly declare that you are going to use it before the deadline, and do use it wisely. If you fail to turn in an assignment that will be a 0, and (likely) the lowest grade. However, the lowest grade in the first half of the course (before midterm), and the lowest grade in the second half will be disregarded in assigning you a final grade.
I encourage you to work in groups of up to 3 people, but as a collaborative project. Individual notebooks must be returned for each homework. Different group members should lead different aspects of the work. A statement must be included in the README explaining each team member’s contribution (similar to an acknowledge of contribution you would find in a Nature letter see, for example these contributions). Midterm and Final will include aspects of the work developed in the homework sessions.
For the Midterm and the Final you are responsible for material in the labs, the reading, and the homework.
There will be opportunities for extra credit projects to improve your grade after the first half of the semester (grade counting toward participation).
Lecture and reading schedule (details are subject to change depending on relevant social events, and ongoing learning outcomes):
Week 0 (9/2):
Lecture: philosophy and good practices of data science: the flow chart of a data-driven project from idea to divulgation, the concepts of falsifiability, reproducibility, open science.
Week 1 (9/7 & 9/9)
Lecture: what do science have to do with policy, why python, the importance of version control.
Lab: github repositories, Jupyter Notebooks
Week 2 (09/14 & 09/16)
Lecture: Descriptive statistics: why everything is gaussian (...or not), bias, basic distributions, moments,
Lab: acquiring and preparing data (CSV, TSV, downloadable ascii files, basic SQL) merging data from different files, plotting histograms and scatter plots, data types incl. ordinal, continuous, categorical data. Data: Citibikes
Week 3 (9/21 & 9/23)
Lecture: Hypothesis testing (chi-square, z-test, p-value).
Lab: Philadelphia criminal data
Week 4 (9/28 & 9/30)
Lecture: Time series analysis
Lab: changes time series data: the NYC subway data
Week 4 (10/5 & 10/7)
Lecture: PDF/CDF, data dredging, sample of 1, correlation vs causality, error analysis, testing models (KS, anderson darling, KL divergence), basic plotting.
Lab: hypothesis testing
Week 5 ( 10/12 & 10/14)
Midterm projects presentations
Week 6 ( 10/19 & 10/21)
Visualizations. Communication through visualizations, history, significance, good and bad visualization examples, what have we learnt since the 1800s?
Lab: a viz
Week 7 ( 10/26 & 10/28)
Lecture: Geospatial analysis with python
Lab: Geopandas, mapping census
Week 8 ( 11/2 & 11/4)
Lecture: Regression: OLS, WLS, missing data, small data.
Lab: TBD
Week 9 ( 11/9 & 11/11)
Lecture: Supervised learning: Tree models
Lab: TBD
Week 10 ( 11/16 & 11/18)
Lecture: Unsupervised learning
Lab: Census + Philadelphia data clustering: gentrification
Week 11 ( 11/30 & 12/2)
Lecture: Data Ethics
Lab: neural networks for super resolution
Week 12:( 12/7 & 12/9)
Final presentations
The Final exam is cumulative: you are responsible for ALL OF THE MATERIAL.
Missing Topics:
Attendance
Absences on religious holidays listed in university calendars are recognized as excused absences. Nevertheless, students are urged to remind the instructor of their intention to be absent on a particular upcoming holiday. Absences on religious holidays not listed in university calendars, as well as absences due to athletic participation or other extracurricular activities in which students are official representatives of the university, shall be recognized as excused absences when the student informs the instructor in writing during the first two weeks of the semester of these planned absences for the semester. All unexcused absences will result in loss of participation credit for the session in question.
Late assignments
Late homework will not be accepted. A single 72-hour exception is allowed throughout the semester
Extra Credit
There may be opportunities for an extra credit project to catch your grade after the first half of the semester (grade counting toward participation).
Professional Conduct
● Adhere to the University of Delaware Code of Conduct.
● Be punctual.
● Complete all reading and homework assignments.
● Be respectful of your peers and instructor.
● Hold yourself accountable for your own academic performance.
Academic Integrity
Please familiarize yourself with UD policies regarding academic dishonesty. To falsify the results of one's research, to steal the words or ideas of another, to cheat on an assignment, to re-submit the same assignment for different classes, or to allow or assist another to commit these acts corrupts the educational process. Students are expected to do their own work and neither give nor recieve unauthorized assistance. Complete details of the university's academic integrity policies and procedures can be found at http://www1.udel.edu/studentconduct/policyref.html Office of Student Conduct, 218 Hullihen Hall, (302) 831-2117. E-mail: student-conduct@udel.edu
Policies concerning plagiarism, in particular, will be strictly followed. Please consult the Chicago Manual of Style for guidelines on citations. Do not hesitate to ask if you have any questions regarding writing style, citations, or any academic policies.
Harassment and Discrimination
The University of Delaware works to promote an academic and work environment that is free from all forms of discrimination, including harassment. As a member of the community, your rights, resource and responsibilities are reflected in the non-discrimination and sexual misconduct policies. Please familiarize yourself with these policies at www.udel.edu/oei . You can report any concerns to the University’s Office of Equity & Inclusion, at 305 Hullihen Hall, (302) 831-8063 or you can report anonymously through UD Police (302) 831-2222 or the EthicsPoint Compliance Hotline at www1.udel.edu/compliance. You can also report any violation of UD policy on harassment, discrimination, or abuse of any person at this site: sites.udel.edu/sexualmisconduct/how-to-report/
Faculty Statement on Disclosures of Instances of Sexual Misconduct
If, at any time during this course, I happen to be made aware that a student may have been the victim of sexual misconduct (including sexual harassment, sexual violence, domestic/dating violence, or stalking), I am obligated to inform the university’s Title IX Coordinator. The university needs to know information about such incidents in order to offer resources to victims and to ensure a safe campus environment for everyone. The Title IX Coordinator will decide if the incident should be examined further. If such a situation is disclosed to me in class, in a paper assignment, or in office hours, I promise to protect your privacy--I will not disclose the incident to anyone but the Title IX Coordinator. For more information on Sexual Misconduct policies, where to get help, and how to reporting information, please refer to www.udel.edu/sexualmisconduct. At UD, we provide 24-hour crisis assistance and victim advocacy and counseling. Contact 302-831-1001, UD Helpline 24/7/365, to get in touch with a sexual offense support advocate.
For information on various places you can turn for help, more information on Sexual Misconduct policies, where to get help, and reporting information please refer to www.udel.edu/sexualmisconduct
Inclusion of Diverse Learning Needs
Any student who thinks he/she may need an accommodation based on a disability should contact the Office of Disability Support Services (DSS) office as soon as possible. The DSS office is located at 240 Academy Street, Alison Hall Suite 130, Phone: 302-831-4643, fax: 302-831-3261, DSS website (www.udel.edu/DSS/). You may contact DSS at dssoffice@udel.edu
Non-Discrimination
The University of Delaware does not discriminate against any person on the basis of race, color, national origin, sex, gender identity or expression, sexual orientation, genetic information, marital status, disability, religion, age, veteran status or any other characteristic protected by applicable law in its employment, educational programs and activities, admissions policies, and scholarship and loan programs as required by Title IX of the Educational Amendments of 1972, the Americans with Disabilities Act of 1990, Section 504 of the Rehabilitation Act of 1973, Title VII of the Civil Rights Act of 1964, and other applicable statutes and University policies. The University of Delaware also prohibits unlawful harassment including sexual harassment and sexual violence.
For inquiries or complaints related to non-discrimination policies, please contact:
Interim Director, Institutional Equity & Title IX Coordinator - Fatimah Stone titleixcoordinator@udel.edu, 305 Hullihen Hall Newark, DE 19716 (302) 831-8063
For complaints related to Section 504 of the Rehabilitation Act of 1973 and/or the Americans with Disabilities Act, please contact: Director, Office of Disability Support Services, Anne L. Jannarone, M.Ed., Ed.S. - ajannaro@udel.edu Alison Hall, Suite 130, Newark, DE 19716 (302) 831-4643 OR contact the U.S. Department of Education - Office for Civil Rights (wdcrobcolp01.ed.gov/CFAPPS/OCR/contactus.cfm)