Concluding thoughts

Human bias in data

• Humans are present at every stage of the data and mapping process: humans observe the world and structure the observations into data; humans interpret this data and visualize it into maps.

An illustration showing the human role in the data and mapping process, Tess McCann (2021).

• Because there are humans at every step of the way, human biases can seep into data and into maps (recall the two Wi-Fi accessibility maps from Session 1!)

Real-world example: Covid-19 testing capacity. Early in the pandemic, a lack of testing infrastructure clouded our understanding of community spread and transmission. Fluctuations in testing capacity and infrastructure affected the accuracy of the data.

• Humans bias and distortion actually begins much earlier than that—with the collection and structuring of data.

  • What gets lost in translation between spreadsheet and map?
  • Some helpful vocabulary for identifying this distortion:
    • Response bias
    • Sample size
    • Survey design (e.g. leading questions)
    • Missing data
      • i.e. the information that we fail to record (more to come in Session 3!)

• Any mode of data collection is imperfect; it is an attempt to record phenomena in our world. Being aware of these imperfections is the key!