Disruptions, Engagement, and GIScience Solutions

Rachel Franklin

Newcastle University + The Alan Turing Institute

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

Other late-night worries

Distinguishing between disruptive science and science disruption

What can go wrong when we privilege disruptive science

makers versus carers

breakers versus maintainers

The mischaracterisation of political/social problems as research problems

time, resources, and policy attention devoted to technical and research investment rather than investment in actual change

GIScience solutions

Answer is more than more AI or more openness or more new names
A piece of the answer is definitely data, however
Knowing when to stay in our lane and when to swerve
And policy
And disciplinary solidarity

Aligning what’s good for careers, what’s good for science, and what’s good for the world

The End