Lectures are Mondays and Wednesdays. The lectures will be structured to explore a new topic each week.  The Monday lecture will initiate the topic and introduce the big picture concepts, led by Ken Kato – Associate Director of the InfoGraphics Lab. The Wednesday lecture will take the topic further, looking deeper under-the-hood, to explore the technical side the topic — looking at the APIs, code, systems architecture, methods, etc, led by Jacob Bartruff – Senior Developer of the InfoGraphics Lab. The readings are posted below. The readings will prepare you for the lecture and discussion.You are expected to have completed the readings prior to the lecture for which they are listed. The readings are all available online (linked below). In general, the readings will consist of one academic article and one mainstream media article on the topic.

March 30
Lecture 1: Course Introduction

April 1
Lecture 2: Smart Cities
Reading:  #1. Smart cities of the future (download full PDF).
#2. Can We Trust Smart Cities? (read all five sections).
Your Thoughts: Below is the word cloud based on the terms you wrote down in class.smartcity3

April 6
Lecture3: Big Data generated by the Sensors of a Smart City
Reading: #1. The real-time city? Big data and smart urbanism (download full PDF). #2. Big Data basic concepts and benefits explained
Your Thoughts: Below is the word cloud based on the terms you wrote down in class.bigdata2

April 8
Lecture4: How do sensors & sensor systems work
Reading: #1.Wireless Sensor Networks – An Introduction.
#2. SmartSantander – The City of Santander’s Sensor Network Facility

April 13
Lecture5: Visualizing sensor data
Reading: #1. Visualizing Sensor Data.
#2. To Go from Big Data to Big Insight, Start with a Visual
Your Thoughts: Below is the word cloud based on the terms you wrote down in class.


April 15
Lecture6: Mapping sensor data
Reading: #1.The New Cartographers – How a Mapping Renaissance Is Changing the Way We See Cities
#2. Spatial is Indeed Special

April 20
Lecture7: A sensor ontology
Reading: #1. A Sensor Classification Scheme.
#2. Ontology of the W3C Semantic Sensor Network Incubator Group.
#3. Sensors by Category. 

April 22
Lecture8: Sensor showcase
Reading: #1. Everything You Ever Wanted to Know About Arduino and Raspberry Pi

April 27
Lecture9: Project Design – What special considerations are necessary for projects employing sensors?
Reading: #1. The e€ffect of weather and climate on bicycle commuting.
#2 Estimating Annual Average Daily Bicyclist and Analyzing Cyclist Safety at Urban Intersections.  You don’t need to read the entire document. Read Chapters 2 and 3 of this dissertation for an excellent presentation of her project design. By all means… read the entire document… or if you’re wanting to go a little deeper – read Sections, 4.10, 4.11, 6.1.1, 6.1.2, 6.2.1 – 6.2.3. 

April 29
Lecture10  Sensing Location – a look under the hood at Geofencing
Reading: #1. Why does a smart city need to be spatially enabled? 

May 4
Lecture11: Privacy and security issues — what are the implications of millions of interconnected sensors continuously broadcasting data and what is the “internet of things”
Reading #1. Smart City Technology May Be Vulnerable To Hackers
Reading #2.Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment – download the  PDF(p11-raij) for easier reading.
Reading #3 ODOT embarks on “big data” project with purchase of Strava dataset

May 6

Lecture12: Sensing Presence – – a look under the hood at Bluetooth Low Energy
Reading: TBA

May 11
Lecture13: Your Sensor Project  and Security and Privacy Part II. We will introduce your final project/assignment. How will we pull data from the Campus Bike Counter? How will we visualize this live data feed? What other sensor data should we integrate to explore bike transportation at the University of Oregon. How will we publish our real-time data feeds? We will also conduct a group discussion on the Security and Privacy issues we covered in Lecture 11 but also consider moral and ethical perspectives of sensor-driven Smart Cities.
Reading#1: Smartmentality: The Smart City as Disciplinary Strategy
Reading #2:“If I look at the mass I will never act”: Psychic numbing and genocide by Paul Slovic.

May 13
Lecture14: Guest lecture with Josh Roll, Data & Modeling Coordinator from Central Lane Metropolitan Planning Organization. Josh will present how local planning staff are using bike traffic count data in GHG, health, and fuel consumption analysis work
Reading #1: Understanding and Measuring Bicycling Behavior: a Focus on Travel Time and Route Choice

May 18
Lecture15: Project Workshop
Group work session – breaking down your project proposals and providing peer review.

May 20
Lecture 16: Guest Lecture with Lyzi Diamond from Mapbox Education. Lyzi is also co-founder of Maptime, a beginner-oriented learning environment geared toward maps and programming with chapters all over the world. Before joining Mapbox, Lyzi was a fellow at Code for America working with city governments to help them better use technology. She holds a dual degree in Geography and Planning, Public Policy, and Management from the University of Oregon — an alum of the UO InfoGraphics Lab.

May 25
No class – Memorial Day

May 27
Lecture 18: Guest lecture with Colin Gibson, VP of Product Development – Diamond Traffic Products. Colin developed the bike counting sensor for campus. He will open up the hood and discuss the type of sensor employed for this project – what is actually being measured/sensed to detect a bike why this is challenging – compared to cars – and specifically our location.

June 1
Lecture 19: Graduate Student Presentations (Seth, Kayleigh, and Megen).

June 3
Lecture 20: Graduate Student Presentations (Blake and Dan) and Course Evaluation discussion with Professor Chris Bone.

June 11
FINAL: Presentations  10:15 – 12:15 in 206 Condon Hall