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Smart Cities

The future of urban environments

  • Instructor is a researcher at MIT
  • Every module has a case study at the end of it
  • 7 hours of distilled content that will enable you to gain a deep understanding of the future of urban environments

Course Description

The United Nations predicts that by 2050 almost 70% of the world’s population will live in city environments, this increase presents tremendous opportunities and challenges. The purpose of this course is to provide a deep understanding of the digital technologies, infrastructure, and social political forces shaping the future of our urban environments. It considers how the latest methods in urban analysis and governance can lead to sustainable and high performing cities. We begin by defining Smart Cites through lectures and case studies and drill down into the technologies shaping new and existing cities. Topics range from the beginnings of cartography to autonomous vehicles and micro grids. This course is for anyone interacting with urban environments or those fascinated by the technology that is essential to the growing population of urban inhabitants.

What am I going to get from this course?
  • Have a deep understanding of of the digital technologies, infrastructure, and social political forces shaping the future of our urban environments.
  • Understand how how urban analysis and governance can lead to sustainable and high-performing cities

Prerequisites and Target Audience

What will students need to know or do before starting this course?
Who should take this course? Who should not?
  • This course is for anyone interacting with urban environments or those fascinated by the technology that is essential to the growing population of urban inhabitants

Curriculum

Module 1: Course Overview
Lecture 1 Instructor Introduction
Lecture 2 Course Goals
Lecture 3 Learning Objectives
Lecture 4 Overview
Lecture 5 Case Study Preview
Lecture 6 Workshop Preview
Module 2: Urbanization
Lecture 7 Density
Lecture 8 History of Urban Growth
Lecture 9 Elements of Urban Fabric
Lecture 10 Rapid Development
Lecture 11 Benefits and Challenges
Lecture 12 Utopian Futures
Lecture 13 "Case Study" Masdar City
Module 3: The Fourth Utility
Lecture 14 Information and the Data Deluge
Lecture 15 Storage Methodologies
Lecture 16 Mobile Computing
Lecture 17 Social Media
Lecture 18 Geocoding, Geoprocessing and Geotagging
Lecture 19 Data Integration
Lecture 20 Cloud Computing
Lecture 21 Open Source
Lecture 22 Open Data Initiatives
Lecture 23 Civic Hacking
Lecture 24 Case Study - Hack for Change
Module 4: Resources and Tools Workshop
Lecture 25 Datasets
Lecture 26 Collecting Data
Lecture 27 Parsing
Lecture 28 Transfer Protocols
Lecture 29 Services
Lecture 30 Tools for Insight
Lecture 31 Rapid Visualization
Module 5: The Contemporary Connected City Part 1
Lecture 32 Networks
Lecture 33 Urban Information
Lecture 34 Types of Spatial Data
Lecture 35 Sensors
Lecture 36 Computer Vision
Lecture 37 Low Orbiting Satellite Imagery
Lecture 38 Internet of Things
Lecture 39 RFID
Lecture 40 Machine to Machine Communication
Lecture 41 Smart City Initiatives Framework
Lecture 42 Case Study: Songdo IBD
Module 6: The Contemporary Connected City Part 2
Lecture 43 Smart City Metrics and Dashboarding
Lecture 44 Mobility
Lecture 45 Shared Services
Lecture 46 Agent Based Modeling
Lecture 47 Resiliency and Disaster Recovery
Lecture 48 Asset Monitoring
Lecture 49 City Service Management
Lecture 50 Energy Simulations
Lecture 51 Measuring and Reporting
Lecture 52 Case Study: Rio Operations Center
Module 7: Governance
Lecture 53 Smart Regulations
Lecture 54 Policy 2.0
Lecture 55 Glocalism and Mayors
Lecture 56 Ecosystems
Lecture 57 Public Private Partnerships
Lecture 58 City-Building as Industry
Lecture 59 Revitalization
Lecture 60 Smart Work Centers and Virtualization
Lecture 61 Data-Driven
Lecture 62 Disruption
Lecture 63 Case Study: New Urban Mechanics
Module 8: Omnipresent and Ubiquity
Lecture 64 Emerging Technology
Lecture 65 Ownership
Lecture 66 Security and Privacy
Lecture 67 Informatics
Lecture 68 Strong Positioning
Lecture 69 Data-Driven
Lecture 70 Network Theory and Complexity
Lecture 71 Machine Learning
Lecture 72 Autonomous Urban Vehicles
Lecture 73 Case Study - USDOTFHWA