What we talk about when we talk about disruptions in spatial data science

What we talk about when we talk about disruptions in spatial data science

• Obvious disruptions

• Other, less obvious, disruptions

• A secret third type of disruptions that keep Rachel up at night

Some obvious disruptions

• Data—new, big, at scale
• Methods—AI, GeoAI, Spatial Data Science, Urban Analytics, etc
• Computational ease—real time, multiscalar, at scale
  
  
  

my guess is we will discuss these exhaustively

Other less obvious disruptions

• Openness—for better…and maybe (?) sometimes for worse…
• Closure—of state borders, as well as data access and ownership
• Inclusion—the who, what, and where of research
  
  
  

what matters more: what we do or how we do it? (answer: both)

Disruptions that keep Rachel up at night

• Data disappearance—the old and the new
• Conceptual + ontological drift—whether we’re measuring what we think we’re measuring*
• Easy science—separating the scientific wheat from the chaff
  
  
  

*many of the basic precepts we organise our data and methods around are rapidly evolving: e.g., gender, household structure, place of work, employment, race/ethnicity