Time | Space | Beyond



Juan C. Rocha

slides: juanrocha.se/presentations/USES_time_space_beyond

Outline

  1. Cities

  2. Bias | Confounding | Counterfactuals

  3. Time

  4. Space

  5. Beyond: social networks

  6. Cases in SES research

Cities



  • Epicentres of human creativity
  • Economic growth
  • Industry
  • Innovations

Cities are both the problem and solution of many sustainability challenges

Cities


Complex adaptive systems
  • Non-linear dynamics
  • Feedbacks
  • Delays
  • Heterogeneity
  • Resilience

Bettencourt L, Lobo J, Strumsky D.Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities. PLoS ONE. 2010.

Bias | Confounding | Counterfactuals

Bias

Bias

Discuss in groups (5min)

  1. What is bias?
  2. How can bias arise in qualitative studies?
  3. How can bias arise in quantitative studies?
  4. An example (made up or from your readings)

Confounding

Confounding

“…a variable that influences both the dependent variable and independent variable, causing a spurious association.”

Confounding

Discuss in groups (5min)

  1. Why is confounding relevant?
  2. What could be a good example of confounding factors in your qualitative studies?
  3. What could be a good example of confounding factors in your quantitative studies?
  4. How does one lead with confounding factors?

Counterfactual

“… we may define a cause to be an object, followed by another, and where all objects, similar to the first, are followed by objects similar to the second. Or in other words, where, if the first object had not been, the second never had existed …” — David Hume

Discuss in groups (5min)

  1. Examples of counterfactuals on your qualitative readings?
  2. Examples of counterfactuals on your quantitative readings?
  3. Why would you worry about them? Under which conditions?

Back to cities

Time 1D



  • Memory decays with time
  • % of your memories are made up
  • Seasonality
  • Non-stationary (trend)
  • Burstiness
  • Coarse graining (missing the signal?)
  • Effects change over time

Space 2-3D



  • Perceptions in space
  • Autocorrelation
  • Heterogeneity
  • Difussion
  • Coarse graining (missing the signal?)
  • Effects change over space

Beyond N-D

Social networks


“birds of a feather flock together”
  • Exponential
  • Small-world
  • Scale-free
  • Homophily
  • Network effects vs context?
  • Spreading: disease, gossip, (mis)information

Break (15min)

Re-cap

  1. Cities

  2. Bias | Counfunding | Counterfactuals

  3. Time

  4. Space

  5. Beyond: social networks

  6. Cases in SES research

Tack | Gracias

Questions?


email: juan.rocha@su.se
twitter: @juanrocha
slides: juanrocha.se/presentations/USES_qualitative_methods

Stockholm Resilience Centre
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