Urban Computing - Fall 2017

DESCRIPTION:

The world's urban population is growing -- leaving cities to face a variety of challenges, ranging from environmental pollution to providing community health to safety. This doctoral seminar course focuses on understanding and undertaking urban challenges using computing technologies. Cities and their residents produce large scale traces of data including communication data, social proximity data, or mobility data. This course covers three major areas on (1) understanding how these traces are generated, managed, and processed; (2) how they can be modeled to address urban challenges; and (3) what are the ethical issues associated with it. This is a project-based, hands-on course which involves applying solutions from state-of-the-art literature to address a real-world problem. A number of guest lectures will provide additional perspectives to the discussions of the class.

PREREQUISITES:

This course does not have any formal prerequisites. However, this is a doctoral seminar course which assumes critical thinking, high ambition, and desire to learn and being challenged with new topics.

TEXT:

We will be reading from a large number of articles. Electronic copies of all papers will be posted on CourseWeb

GRADING:

Participation in class discussions [15 points]
Readings [30 points]
Wikipedia article [10 points]
Project proposal [15 points]
Final project product [30 points]: we will aim to submit the results of the project to the IEEE special issue on Social Computing Applications for Smart Cities

OFFICE HOUR:

Thursday 12:30-2:00 pm, or by appointment, Room 709 (135 North Bellefield Avenue)

TOPICS TO BE COVERED:

Urban sensing and Urban traces
Urban data acquisition, data integration, and sense-making
Urban data analytics
Modeling
Privacy and ethical issues

SYLLABUS:

#	Date	Topic	Readings	Details
1	2017-08-30	Introduction and overview	UbiComp in the urban frontier. The familiar stranger: anxiety, comfort, and play in public places. Urban Computing: concepts, methodologies, and applications	introduction to course, instructor, and students course overview and logistics introduction to Urban Computing and its foundation
2	2017-09-06	Work session 1: Urban sensing	A review of urban computing for mobile phone traces: current methods, challenges and opportunities Daily travel behavior: lessons from a week-long survey for the extraction of human mobility motifs related information Urban sensing: Out of the woods	What methods are used to collect data? What sources are being used to collect data? Challenges of collecting urban data
3	2017-09-13	Instructor out of town		Meet to discuss the improvement of the Wikipedia article on Urban Computing
4	2017-09-20	Guest lecture 1: Bob Gradeck and David Walker from Western PA Regional Data Center		Introduction to WPRDC activities Available datasets and potential challenges targeted by WPRDC Privacy issues associated with each dataset
5	2017-09-27	Work session 2: Urban data representation Guest lecture 2: Dr. Stephen Hirtle Professor School of Computing and Information	SPUD - Semantic processing of urban data A survey of traffic data visualization Illuminating LEGOs with digital information to create urban data observatory and intervention simulator	Guest lecture on urban routes and urban navigation Processing of urban data What methods and approaches are used to represent urban data? Visualization methods for urban data
6	2017-10-04	Instructor out of town		Wikipedia working session
7	2017-10-11	Work session 3: modeling		Modeling linked data Features of urban data
8	2017-10-18	Guest Lecture 3: Dr. Konstantinos Pelechrinis Associate Professor School of Computing and Information		Analytical techniques to mine urban data Urban shared transportation
9	2017-10-25	Work session 4: modeling	Dissecting urban noises from heterogeneous geo-social media and sensor data	Approaches to modeling of urban data Challenges of modeling urban data
10	2017-11-01	Work session 5: Privacy and Ethics	Participatory privacy in urban sensing Protecting privacy in public? Surveillance technologies and the value of public places Smart environments \& the convergence of the veillances: Privacy violations to consider	What are the potential privacy risks? What are the ethical concerns? Approaches in addressing ethical concerns Impact of surveillance technologies
11	2017-11-08	Guest Lecture 4: Dr. Stephen Smith Research Professor, Robotics Carnegie Mellon University		Smart Infrastructure for Urban Mobility Adaptive traffic signal control
12	2017-11-15	Work session 6: project		Work on IEEE submission
13	2017-11-22	Thanksgiving break		No class
14	2017-11-29	Work session 7: project		Deadline for IEEE submission on Social Computing for Smart Cities
15	2017-12-06	Work session 8: project		Finishing touches on the project
16	2017-12-13	Final project session		Open presentation to the school

COURSE POLICIES

Academic Integrity: You are expected to be fully aware of your responsibility to maintain a high quality of integrity in all of your work. All work must be your own, unless collaboration is specifically and explicitly permitted as in the course group project. Any unauthorized collaboration or copying will at minimum result in no credit for the affected assignment and may be subject to further action under the University Guidelines for Academic Integrity. You are expected to have read and understood these Guidelines. A document discussing these guidelines was included in your orientation materials.

Attendance: Class attendance, while not mandatory, is required if you want to succeed in this course, especially since the course does not have any course book and involves a lot of in-class discussions. The class participation credit is engineered to encourage your attendance.

Concerning Students with Disabilities: If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

An important note on plagiarism: Cheating and plagiarism will not be tolerated. Students caught cheating or plagiarizing will receive no credit for the assignment on which the cheating occurred. Additional actions -- including assigning the student a failing grade in the class or referring the case for disciplinary action -- may be taken at the discretion of the instructors. You may incorporate excerpts from publications by other authors, but they must be clearly marked as quotations and properly attributed. You may obtain copy editing assistance, and you may discuss your ideas with others, but all substantive writing and ideas must be your own or else be explicitly attributed to another, using a citation sufficiently detailed for someone else to easily locate your source.