The Future of GeoComputation

Ian Turton

Centre for Computational Geography

University of Leeds

Summary

• People• Data

– Space

– Time

• Computing• Methods

– Explorative

– Explanative

– Exploitative

The CCG

Some of them anyway

Mountains of Data

Swamps of Data

We know what you spend...

…where you spend it...

…who you talk to...

…where you live...

What your neighbours are like, what your house is

...Crime data and...

• crime type• crime location• insurance data

...Health data

• environmental data• socio-economic data• admissions data

The Cray T3D and T3E

• High Performance Computing

• Time machines• Just big enough for

modern geographical problems

The Internet

• GIS and the Web– Public participation in

planning

• Distributed Computing– “many hands make light

work”

What can we do with all this data and computer power?

•Explore it

•Explain it

•Exploit it

Exploration

• Given some (large amount of) data

• find anything that is “interesting” in that data

Pattern Analysis

• GAM• GEM• Automated analysis• Easy to understand

output• No statistical

assumptions• crime, health,

education ...

Spatial Search Agents

• If we don’t know where to look

• Look every where?• Or let something else

do the looking?

Urban Social Structure

Glasgow and London

Fourier-Mellin space

Glasgow and London

Rezoning

• Census variables and areas

• Sales areas• Voting districts

Explanation

• Having found something “interesting” in a data set

• Attempt to explain it or model it

Spatial Interaction Models

• Migration flows• Commuting flows

– GB Ward to Wards flows (10,000)

• Phone flows – (20+ Million)

• EU Flows

Cellular Automata

• Simple CA Life• Complex multi-state

CA forest fires• Pedestrian or traffic

movements

Neural Nets

• Black Box • Non-linear parameter

free estimations• Used any where a

“normal” model could be used.

Fuzzy Logic

• Allows the introduction of imprecision to model• More computation gives better answers

Agents on a Ring

• Catherine Dibble• Agents can move

along the lines GROW

MAKE

SERVSERV

INFOINFO

Generate reasonable patterns

Exploitation

• Having found something of interest

• and explained it (in some way)

• make use of this knowledge

Spatial Location Optimisation

• Based on spatial interaction model

• Run the model 1000’s of times

• In this case 10,000 zones

Flood Forecasting

• How likely is it to flood in the next 6 hours?

• Neural nets• Fuzzy Logic

Sensitivity Analysis on Models

• Run the model 1000’s of times with perturbations to inputs

• Get out real error estimates

• Population Models• Flood Models• Drainage Models

Conclusions

• More data– better data

• More computing– better computing

• More models– better models