Course Modules: 11.S187/11.S954 Applied Data Science for Cities

Welcome

Welcome Welcome

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Course Overview & Syllabus Course Overview & Syllabus 1

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Primer Primer 2

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Hacking Resources Hacking Resources 3

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About Your Instructor About Your Instructor 4

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Coding Support (optional self-learning material) Coding Support (optional self-learning material) 5

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Project Guidelines

Project Guidelines Project Guidelines

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Project Proposal Guideline - Data Section Project Proposal Guideline - Data Section 1

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Project Proposal Guideline - Analytics Section Project Proposal Guideline - Analytics Section 2

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Cambridge Civic Challenge Inventory Cambridge Civic Challenge Inventory 3

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Attachment

Applied Data Science Final Report Guideline.pdf Applied Data Science Final Report Guideline.pdf 4

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Session 1: Making Sense of Urban Data Science

Session 1: Making Sense of Urban Data Science Session 1: Making Sense of Urban Data Science

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Session 1 Recording Session 1 Recording 1

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Attachment

11S187/11S954_20FA_lecture_note_01.pdf 11S187/11S954_20FA_lecture_note_01.pdf 2

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Context Module Sub Header

Recommended Readings 3

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Attachment

Townsend, A. (2015). Cities of data: Examining the new urban science. Public Culture, 27(2 (76)), 201-212 Townsend, A. (2015). Cities of data: Examining the new urban science. Public Culture, 27(2 (76)), 201-212 4

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Attachment

Koonin, S. E., & Holland, M. (2014). The value of big data for urban science. Privacy, Big Data, and the Public Good: Frameworks for Engagement, 1, 137-152 Koonin, S. E., & Holland, M. (2014). The value of big data for urban science. Privacy, Big Data, and the Public Good: Frameworks for Engagement, 1, 137-152 5

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Session 2: Computing Urban Data

Session 2: Computing Urban Data Session 2: Computing Urban Data

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Session 2 Recording Session 2 Recording 1

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Attachment

11S187_20FA_lecture_note_02.pdf 11S187_20FA_lecture_note_02.pdf 2

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Context Module Sub Header

Data Science Workshop: Intro to Python and Jupyter Notebook 3

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Attachment

DS-workshop-2.zip DS-workshop-2.zip 4

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Context Module Sub Header

Optional Readings 5

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Attachment

Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 38 Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 38 6

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Attachment

Glaeser, E. L., Kominers, S. D., Luca, M., & Naik, N. (2018). Big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry, 56(1), 114-137 Glaeser, E. L., Kominers, S. D., Luca, M., & Naik, N. (2018). Big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry, 56(1), 114-137 7

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Session 3: Instrumentation of Urban Systems

Session 3: Instrumentation of Urban Systems Session 3: Instrumentation of Urban Systems

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Session 3 Recording Session 3 Recording 1

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Attachment

11S187_20FA_lecture_note_03.pdf 11S187_20FA_lecture_note_03.pdf 2

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Context Module Sub Header

Release: Project proposal guideline (data section) 3

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Context Module Sub Header

Readings 4

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External Url

Smart and Connected Communities, National Science Foundation Smart and Connected Communities, National Science Foundation 5

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Context Module Sub Header

Data Science Workshop: Data Acquisition and Inspection 6

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Attachment

DS Workshop notebook 3 - Data Acquisition.ipynb DS Workshop notebook 3 - Data Acquisition.ipynb 7

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Session 4: Cities as Cybersocial Systems

Session 4: Cities as Cybersocial Systems Session 4: Cities as Cybersocial Systems

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Session 4 Class Recording Session 4 Class Recording 1

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Attachment

Lecture_note_04.pdf Lecture_note_04.pdf 2

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Context Module Sub Header

Readings 3

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Attachment

Dong, L., Ratti, C., & Zheng, S. (2019). Predicting neighborhoods’ socioeconomic attributes using restaurant data. Proceedings of the National Academy of Sciences, 116(31), 15447-15452 Dong, L., Ratti, C., & Zheng, S. (2019). Predicting neighborhoods’ socioeconomic attributes using restaurant data. Proceedings of the National Academy of Sciences, 116(31), 15447-15452 4

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Context Module Sub Header

Data Science Workshop: Data Manipulation and Description 5

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Attachment

DS-workshop-4 (in-class).zip DS-workshop-4 (in-class).zip 6

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Attachment

Notebook 4 - Data integration and manipulation (full version).ipynb Notebook 4 - Data integration and manipulation (full version).ipynb 7

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Session 5: Spatial Data in Cities

Session 5: Spatial Data in Cities Session 5: Spatial Data in Cities

Page

Session 5 Class Recording Session 5 Class Recording 1

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Attachment

Lecture_note_05.pdf Lecture_note_05.pdf 2

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Context Module Sub Header

Readings 3

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External Url

Strava, Popular with Cyclists and Runners, Wants to Sell Its Data to Urban Planners. The Wall Street Journal. 2014 Strava, Popular with Cyclists and Runners, Wants to Sell Its Data to Urban Planners. The Wall Street Journal. 2014 4

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Context Module Sub Header

Data Science Workshop: Spatial data in GeoPandas 5

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Attachment

DS-workshop-5.zip DS-workshop-5.zip 6

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Attachment

Notebook 5 - Handling Spatial Data in GeoPandas (Full notebook).ipynb Notebook 5 - Handling Spatial Data in GeoPandas (Full notebook).ipynb 7

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Attachment

Workshop-5-Challenge with Answers.ipynb Workshop-5-Challenge with Answers.ipynb 8

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Session 6: Analyzing Multidimensional Urban Phenomena

Session 6: Analyzing Multidimensional Urban Phenomena Session 6: Analyzing Multidimensional Urban Phenomena

Page

Session 6 Class Recording Session 6 Class Recording 1

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Attachment

Lecture_note_06.pdf Lecture_note_06.pdf 2

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Readings 3

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External Url

DataKind - Creating Safer Streets Through Data Science DataKind - Creating Safer Streets Through Data Science 4

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External Url

Airbnb and New York City Reach a Truce on Home-Sharing Data Airbnb and New York City Reach a Truce on Home-Sharing Data 5

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Context Module Sub Header

Data Science Workshop: High-Dimensional Data 6

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DS_Workshop-6.zip DS_Workshop-6.zip 7

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Notebook 6 - Exploring Airbnb Data (Full notebook).ipynb Notebook 6 - Exploring Airbnb Data (Full notebook).ipynb 8

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Session 7: Data-Driven Urban Decision-Making and Operations

Session 7: Data-Driven Urban Decision-Making and Operations Session 7: Data-Driven Urban Decision-Making and Operations

Page

Session 7 Class Recording Session 7 Class Recording 1

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Lecture_note_07.pdf Lecture_note_07.pdf 2

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Readings 3

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External Url

How Data-driven is Your City? HKS Ash Center (3 min read) How Data-driven is Your City? HKS Ash Center (3 min read) 4

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External Url

Lai, Y., Yeung, W., & Celi, L. A. (2020). Urban Intelligence for Pandemic Response. JMIR public health and surveillance, 6(2), e18873. Lai, Y., Yeung, W., & Celi, L. A. (2020). Urban Intelligence for Pandemic Response. JMIR public health and surveillance, 6(2), e18873. 5

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Optional Readings 6

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External Url

Tracking Boston’s Progress with Just One Number Tracking Boston’s Progress with Just One Number 7

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External Url

5 Lessons from the Best Data-driven Cities. Bloomberg Cities (7 min read) 5 Lessons from the Best Data-driven Cities. Bloomberg Cities (7 min read) 8

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Context Module Sub Header

Data Science Workshop: Time-series Data 9

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DS-workshop-7.zip DS-workshop-7.zip 10

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Attachment

Notebook 7 - Time-series Analysis (Full notebook).ipynb Notebook 7 - Time-series Analysis (Full notebook).ipynb 11

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Session 8: Mid-Point Project Review

Session 8: Mid-Point Project Review Session 8: Mid-Point Project Review

Page

Session 8 Class Recording Session 8 Class Recording 1

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meeting_saved_chat.txt meeting_saved_chat.txt 2

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Session 9: Responsible Data Science for Cities

Session 9: Responsible Data Science for Cities Session 9: Responsible Data Science for Cities

Page

Session 9 Class Recording Session 9 Class Recording 1

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11S187_20FA_lecture_note_09.pdf 11S187_20FA_lecture_note_09.pdf 2

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Readings 3

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External Url

New York City Automated Decision Systems Task Force New York City Automated Decision Systems Task Force 4

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Context Module Sub Header

Optional 5

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External Url

Why the Bronx Burned Why the Bronx Burned 6

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External Url

With Great Data Comes Great Responsibility: How Data Science Can Be Applied to Solve Public Interest Problems Without Losing Its Soul With Great Data Comes Great Responsibility: How Data Science Can Be Applied to Solve Public Interest Problems Without Losing Its Soul 7

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Context Module Sub Header

Data Science Workshop: Python Review 8

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Notebook_09_Python Review.zip Notebook_09_Python Review.zip 9

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Notebook_09_Python Review (Full notebook).ipynb Notebook_09_Python Review (Full notebook).ipynb 10

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Session 10: Statistical Plotting

Session 10: Statistical Plotting Session 10: Statistical Plotting

Page

Session 10 Class Recording Session 10 Class Recording 1

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11S187_20FA_lecture_note_10.pdf 11S187_20FA_lecture_note_10.pdf 2

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Reading 3

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Page

Police Told to Give Street-Stop Data Police Told to Give Street-Stop Data 4

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Data Science Workshop: Statistical Plotting with Seaborn 5

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Notebook 10 - Statistical Plotting (In-class).ipynb Notebook 10 - Statistical Plotting (In-class).ipynb 6

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Notebook 10 - Statistical Plotting (Full-notebook).ipynb Notebook 10 - Statistical Plotting (Full-notebook).ipynb 7

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sqf-2018.xlsx sqf-2018.xlsx 8

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Session 11: Machine Learning for Cities - Application and Controversies

Session 11: Machine Learning for Cities - Application and Controversies Session 11: Machine Learning for Cities - Application and Controversies

Page

Session 11 Class Recording Session 11 Class Recording 1

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Readings 3

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Page

The ethics of smart cities and urban science The ethics of smart cities and urban science 4

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Page

Why Real-Time Traffic Control Has Mobility Experts Spooked Why Real-Time Traffic Control Has Mobility Experts Spooked 5

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Data Science Workshop: NLTK 6

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Notebook 11 - Unstructured data I (In-class).ipynb Notebook 11 - Unstructured data I (In-class).ipynb 7

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Notebook 11 - Unstructured data I (Full notebook).ipynb Notebook 11 - Unstructured data I (Full notebook).ipynb 8

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Attachment

Cambridge_listings_2019_July.csv Cambridge_listings_2019_July.csv 9

attachment 185772 0

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Attachment

Cambridge_listings_2019_September.csv Cambridge_listings_2019_September.csv 10

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Attachment

Cambridge_listings_2019_August.csv Cambridge_listings_2019_August.csv 11

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Session 12: Art and Science of Cities

Session 12: Art and Science of Cities Session 12: Art and Science of Cities

Page

Session 12 Class Recording Session 12 Class Recording 1

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Context Module Sub Header

Reading 2

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Page

Jane Jacobs: Is There Good Science behind Urban Planning? Jane Jacobs: Is There Good Science behind Urban Planning? 3

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Project Final Presentations

Project Final Presentations Project Final Presentations

Page

Final Presentation Slide-Deck + Logistics + Recommendations Final Presentation Slide-Deck + Logistics + Recommendations 1

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Course Modules

Welcome

Project Guidelines

Session 1: Making Sense of Urban Data Science

Recommended Readings 3

Session 2: Computing Urban Data

Data Science Workshop: Intro to Python and Jupyter Notebook 3

Optional Readings 5

Session 3: Instrumentation of Urban Systems

Release: Project proposal guideline (data section) 3

Readings 4

Data Science Workshop: Data Acquisition and Inspection 6

Session 4: Cities as Cybersocial Systems

Readings 3

Data Science Workshop: Data Manipulation and Description 5

Session 5: Spatial Data in Cities

Readings 3

Data Science Workshop: Spatial data in GeoPandas 5

Session 6: Analyzing Multidimensional Urban Phenomena

Readings 3

Data Science Workshop: High-Dimensional Data 6

Session 7: Data-Driven Urban Decision-Making and Operations

Readings 3

Optional Readings 6

Data Science Workshop: Time-series Data 9

Session 8: Mid-Point Project Review

Session 9: Responsible Data Science for Cities

Readings 3

Optional 5

Data Science Workshop: Python Review 8

Session 10: Statistical Plotting

Reading 3

Data Science Workshop: Statistical Plotting with Seaborn 5

Session 11: Machine Learning for Cities - Application and Controversies

Readings 3

Data Science Workshop: NLTK 6

Session 12: Art and Science of Cities

Reading 2

Project Final Presentations