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  SERV  INFO  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