Neighborhood Analysis
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Sharing Your Work

  • Schedule Overview
    • Course Schedule
  • Course Introduction
    • 1. Course Introduction
    • 2. What is a Neighborhood?
    • 3. Building a Data Pipeline
    • 4. Sharing Your Work
    • 5. Learner’s Permit
    • 6. Describing Places
    • 7. Describing Places
  • Strategies for Analysis
    • 8. Population and the Census
    • 9. Population and the Census
    • 10. Population Projections
    • 11. Population Projections
    • 12. Segregation
    • 13. Segregation
    • 14. Neighborhood Change
    • 15. Neighborhood Change
    • 16. Place Opportunity
    • 17. Place Opportunity
    • 18. TBD
    • 19. TBD
    • 20. TBD
    • 21. TBD
    • 22. Field Observation
    • 23. Field Observation
  • Course Wrap-Up
    • 24. Final Project Peer Review
    • 25. Final Presentations
    • 26. Independent Work and Advising
    • 27. Independent Work and Advising
    • 28. Final Presentations
    • 29. Final Presentations

On this page

  • Session Description
  • Before Class
  • Reflect
  • Slides
  • Resources for Further Exploration

Sharing Your Work

Session Description

This session builds upon the work on your last lab. In that lab, you worked on developing several workflows that will support your work over the course of the semester. Coming into this lab, you should have a formatted Quarto markdown document. In this session, we’ll talk about strategies for sharing that work, will configure your computer to communicate with GitHub, and will create your first publicly facing websites.

Before Class

  1. Read through the entire lab background description before approaching lab tasks.

  2. Be prepared to access the formatted Quarto notebook you worked on in the last lab that contains your analysis of Chicago community areas.

  3. Be prepared to access your Lab 1 reflection.

D’Ignazio, Catherine, and Lauren F. Klein. (2020). Data Feminism. MIT Press. Chapter 3 , Chapter 4

Reflect

  1. How can planners and others engaging directly in public policy discourse and debate leverage emotion in their analysis in ways that generate meaning and connection without manipulating or leading towards particular conclusions?

  2. What does the rhetorics of design look like today? How do we reclaim the rhetorical roots of data analysis amidst the proliferation of technocal approaches and (overly) abundant data?

  3. Is there such thing a “neutral” data analysis?

  4. Can you think of classification systems that may have unintended consequences or biases in data that you’ve used for urban analysis in the past?

Slides

Resources for Further Exploration

Content Andrew J. Greenlee
Made with and Quarto
Website Code on Github