Geographical Data Mining

Stan Openshaw

Centre for Computational Geography

University of Leeds

BUTIan Turton, CCG, Leeds University

For the latest on Stanhttp://www.geog.leeds.ac.uk/staff/s.openshaw/latest.html

Why would we want to do this?

• Geographical Data Explosion

• Public imperative

• Lack of geographically aware tools

Mountains of Data

Swamps of Data

We know what you spend...

…where you spend it...

…who you talk to...

…where you live...

LS2 9JT

What your neighbours are like

...Crime data and...

• crime type• crime location• insurance data

...Health data

• environmental data• socio-economic data• admissions data

Geographical Hyperspace

• Geography – x,y co-ordinates, postcodes

• Time – days, hours, months

• Attributes– place - pollution sources, soil type, distance to

motorway– cases - type of disease, age, sex

Data Mining

Turning data into knowledge

• How do these data sets fit together?

• Is there anything important hidden in here?

• Does geography make a difference?

Datatype Nature of Data Interaction_________________________________________1. spatial data2. time data3. multiple attribute data4. geography and time data5. time and multiple attribute data6. geography and multiple attribute data7. geography, time, and multiple

attribute data

HISTORICALLY

these effects have been hidden

by research design

BUT

BUT

The result is often data strangulation

The patterns are being destroyed

or

damaged

by the research design

What is needed is a geographic data mining technology that works

How can we do this?

• Developing new smarter methods

• Testing them– HPC is vital to this

process

• Disseminating them– Internet

– Java

Being SMART is not just a matter of

methodology but also involves access,

usability, relevancy, and result

communication factors

The complete novice should be able to perform some

sophisticated geographical analysis and get some useful and understandable results on the same day the work

started

User Friendly Spatial Analysis

• provides analysis that users need

• simple to perform

• highly automated making it fast and efficient

• readily understood

• results are self-evident and can be communicated to non-experts

• safe and trustworthy

What we did in this study

• Comparison of techniques on the same data

• Multiple techniques– GAM/K– GAM/K-T– MAPEX– GDM1/2– FLOCK– Proprietary Data Mining Tools

Study Area

Stan’s Cases

Chris’ cases

How to search the geographic space

• Exhaustively – GAM, GEM

• Smartly– Genetic algorithm

• mapex, gdm

– Flocking • boids

GAM & GEM

Mapex & GDM

FLOCK

And the Attributes...

• Exhaustively – GAM, GEM

• Smartly– Genetic algorithm

• mapex, gdm, boids

GAM & GEM with time

Rock A

Rock B Rock C

Rock D

Geology Map

railway

2 km

buffer polygon

Combined Geology and Railway Buffer Map

Rock A

Rock B Rock C

Rock D2 km

Combinations of Attributes

• If we have 8 attributes with 10 classes each

• There are 3160 permutations of 2 classes from 80 compared with 24,040,016 if any 5 are used

• Smart searches are essential– use GA to generate possible combinations of

interest

Proprietary Data Miners

Results

How to visualise

them?

Results• GAM/K

– did very well– was not put off by time or attributes

• GAM/KT– worked well – time clusters found

• MAPEX / GDM/1– worked well

Results continued

• FLOCK– worked very well

• Data mining– didn’t work at all well out of the box– could have built a GAM inside them

What next?

• Build a harder data set for more tests

• Re-run the analysis

• Put it all on the web

Thanks to

• European Research Office of the US Army

• ESRC grant R237260 for paying Ian’s salary.

• ESRC/JISC for the Census data purchase.

• OS for the bits of the maps they own.

To find out more

• Web based Multi-engine spatial analysis tools James Macgill, Openshaw and Turton– Session 1A - 14.00 Sunday

• Smart Crime Pattern Analysis using GAM Ian Turton, Openshaw and Macgill– Session 7A - 10.40 Tuesday

Contacts

Email ian,stan,pgjm@geog.leeds.ac.uk

check out smart pattern analysis on the web

http://www.ccg.leeds.ac.uk/smart

http://www.ccg.leeds.ac.uk/smart/hyper.doc

Latest news on Stanhttp://www.geog.leeds.ac.uk/staff/s.openshaw/latest.html