HPC and Geographic Research: An overview of the Human System Modelling Consortium

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HPC and Geographic Research: An overview of the Human System Modelling Consortium


Faster Computers are stimulating new ways of doing science!


Most geographers and social scientists seemingly do not currently understand what HPC can deliver and few make use of it!

This is a GREAT PITY!

If that PC on your desk had access to a HPC that was 5,000 times faster and had 1,000 times more memory what would you do with it?


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This is one of the things we wanted to change!

Geographers have a KEY role to play here in developing a computational approach to social science

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Typical Data Set Sizes are rapidly increasing

The World about us is becoming increasingly DATA RICH but theory poor

we need NEW TECHNOLOGIES so we can start to cope!

We NEED much FASTER and BIGGER supercomputers to help us cope!


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WE have to be able to demonstrate that if we perform 100,000 or several million times more computation that the benefits are worthwhile!

So what are the Problems?


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The challenge is to:


Problems Encountered

A new philosophy

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GeoComputation catches on!

What has the HSM Consortium done?

Codes Ported

Preserving the Investment

Why it is worth the effort


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Improve legacy models and statistical methods

Apply new approaches based on Computational Intelligent Methods

Investigate novel computational technologies

Seven Brief Case Studies that illustrate:

(1) Parallel Spatial Interaction Models

Spatial Interaction

Examples of Spatial Interaction Models (SIMS)

Origin Constrained Model

Why Parallelise it?

Calibration of the SIM

Porting the Spatial Interaction Model


New Results and Better Science

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(2) Better ways of Parameter Estimation for Spatial Interaction Models

But there are PROBLEMS with conventional non-linear optimisation methods

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If you use more parameters in your simple model then.. the problem becomes even WORSE!

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Most sophisticated statistical modelling methods PROBABLY suffer from similar risks it is just that few realize it!!

Various Solutions

GA has many advantages

The principal disadvantage is that the GA takes about 10,000 times more compute time than a more conventional nonlinear optimiser

BUT its explicitly parallel

HPC to the rescue!

(3) Spatial Location Optimisation Modelling


A complex combinatorial optimisation problem

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(4) New Computationally Intelligent Methods can be used to build better performing models

If you can AFFORD the computation you can dramatically improve model performances

New Spatial Interaction Models

Often a built-in prejudice against computationally derived models BUT not all are Black Boxes

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HPC challenges many established wisdom's

(5) Engineering Geographical Zoning Systems

Zones are commonly used as:

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Historically there has been little zone design technology

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Zone Design is a subject of current and recent significance but it is only recently that it has become possible to EXPLICITLY DESIGN zoning systems due to lack of digital map data and fast enough HPC

Zone Design can be viewed as a special type of combinatorial optimization problem

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Basic algorithms were first produced 20 years ago

New Algorithms



Simulated Annealing takes a lot of compute time!

so.. the big question

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is there?

a Cray T3D 512 processor solution to this problem???

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But the problem is that Simulated Annealing is a highly serial method of optimization

AND.. we did not ‘merely’ want a better result that took longer than previously but a good result that could be produced faster than before

Openshaw-Schmidt hybrid genetic simulated annealer


Example using 1991 census data for Leeds - Bradford region

Unemployment Leeds and Bradford Wards

Unemployment Leeds and Bradford EDs

Unemployment Equal Population

(6) Searching for Geographical Patterns in Large Databases

There is a VAST and RAPIDLY growing GEOCYBERSPACE of information

Mark 1 Geographical Analysis Machine

Geographical Analysis Machine

GAM Algorithm

GAM needed HPC because..

Monte Carlo Multiple Testing Outer Loop needed

10 years ago GAM was a mixed blessing!


Results of a recent evaluation exercise published in Alexander and Boyle (1996)

Overall Performance when Detecting Clustering on 50 synthetic data sets

Estimated Positive Sensitivities in Finding CLUSTER locations

Applying GAM to Long Term Limiting Illness data for Northern England

Ward Level LLTI

Bootstrap, Regional

Bootstrap, Regional Teeside

Bootstrap, Regional Tyneside

Random Data

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(7)Building a Geographical Explanation Machine (GEM)

GEM can be run in 4 modes

Insufficient time to describe how GEM works instead we present some results using as pseudo coverages

GEM is computationally intensive

GEM mode=2

GEM Mode =3

Other Applications

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Author: Stan Openshaw, Univedrsity of Leeds

Email: stan@geog.leeds.ac.uk

Home Page: http://www.geog.leeds.ac.uk/staff/s.openshaw/