Spatial Data Science
@darribas]
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The Rise of Data Science
Data, Data, Data
Every-thing produces data
New, dense, quantitative representations of the world
But data is not useful, insights are
Data Science
Loukides (2011)
Hold on…
- A lot of the new data is spatial data
- Spatial is special
- We don’t want to reinvent the (GISc) wheel
Geo/Spatial Data Science
Why?
Fully tap into the new data revolution
- Data are different (“half-baked”)
- New data allow more flexibility (+ data, - structure)
“Business-as-usual” best approach?
A (rough) sketch
What
- Systems engineering [Geo-infrastructure]
- Methods [Explicitly spatial ML/Stats]
- Epistemology [Adequate conceptualization]
How
- Coupling [Linking Platforms]
- Assimilation [space fully embedded in DS]
- Full Integration [Co-Proudction]
Potential Benefits
- Fully leverage the new data revolution
- Do not reinvent the wheel
- New answers to old questions (e.g. MAUP)
- Old answers in new contexts
- New questions to answer (new methods?)
- …
Getting everyone behind G/SDS
G/SDS is for all
Industry & Academia
- Shared problems
- Access to (/baking of) data
- Similar questions in different contexts
- Complementary resources
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[A]Training muscle, space to experiment -
[I]Funding muscle, production-ready tooling
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Clear synergies
Challenges
- Communication →what’s needed/valued?
- Interaction →how?
- Credit →Fame-sharing
For example…
- Teach relevant G/SDS skills
- Provide close interaction w/ industry in training
- Build platforms that enabling data sharing/access
For example…
- Teach relevant G/SDS skills
- Provide close interaction w/ industry in training
- Build platforms that enabling data sharing/access
For example…
- Teach relevant G/SDS skills
- Provide close interaction w/ industry in training
- Build platforms that enabling data sharing/access
For example…
- Teach relevant G/SDS skills
- Provide close interaction w/ industry in training
- Build platforms that enabling data sharing/access
Spatial Data Science
@darribas]
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