the future of geocomputation ian turton centre for computational geography university of leeds
Post on 21-Dec-2015
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![Page 1: The Future of GeoComputation Ian Turton Centre for Computational Geography University of Leeds](https://reader030.vdocument.in/reader030/viewer/2022032704/56649d5f5503460f94a3f0fa/html5/thumbnails/1.jpg)
The Future of GeoComputation
Ian Turton
Centre for Computational Geography
University of Leeds
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Summary
• People• Data
– Space
– Time
• Computing• Methods
– Explorative
– Explanative
– Exploitative
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The CCG
Some of them anyway
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Mountains of Data
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Swamps of Data
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We know what you spend...
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…where you spend it...
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…who you talk to...
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…where you live...
What your neighbours are like, what your house is
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...Crime data and...
• crime type• crime location• insurance data
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...Health data
• environmental data• socio-economic data• admissions data
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The Cray T3D and T3E
• High Performance Computing
• Time machines• Just big enough for
modern geographical problems
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The Internet
• GIS and the Web– Public participation in
planning
• Distributed Computing– “many hands make light
work”
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What can we do with all this data and computer power?
•Explore it
•Explain it
•Exploit it
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Exploration
• Given some (large amount of) data
• find anything that is “interesting” in that data
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Pattern Analysis
• GAM• GEM• Automated analysis• Easy to understand
output• No statistical
assumptions• crime, health,
education ...
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Spatial Search Agents
• If we don’t know where to look
• Look every where?• Or let something else
do the looking?
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Urban Social Structure
Glasgow and London
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Fourier-Mellin space
Glasgow and London
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Rezoning
• Census variables and areas
• Sales areas• Voting districts
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Explanation
• Having found something “interesting” in a data set
• Attempt to explain it or model it
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Spatial Interaction Models
• Migration flows• Commuting flows
– GB Ward to Wards flows (10,000)
• Phone flows – (20+ Million)
• EU Flows
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Cellular Automata
• Simple CA Life• Complex multi-state
CA forest fires• Pedestrian or traffic
movements
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Neural Nets
• Black Box • Non-linear parameter
free estimations• Used any where a
“normal” model could be used.
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Fuzzy Logic
• Allows the introduction of imprecision to model• More computation gives better answers
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Agents on a Ring
• Catherine Dibble• Agents can move
along the lines GROW
MAKE
SERVSERV
INFOINFO
Generate reasonable patterns
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Exploitation
• Having found something of interest
• and explained it (in some way)
• make use of this knowledge
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Spatial Location Optimisation
• Based on spatial interaction model
• Run the model 1000’s of times
• In this case 10,000 zones
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Flood Forecasting
• How likely is it to flood in the next 6 hours?
• Neural nets• Fuzzy Logic
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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
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Conclusions
• More data– better data
• More computing– better computing
• More models– better models