Geospatial data sciences
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Geospatial Data Sciences. Ranga Raju Vatsavai Auroop Ganguly Budhendra Bhaduri Geographic Information Science and Technology Computational Sciences and Engineering. Geospatial data sciences. Disaster mapping and monitoring. Geospatial intelligence discovery. HP Visualization. Knowledge

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Geospatial Data Sciences

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Geospatial data sciences

Geospatial Data Sciences

Ranga Raju Vatsavai

Auroop Ganguly

Budhendra Bhaduri

Geographic Information Science and Technology Computational Sciences and Engineering


Geospatial data sciences1

Geospatial data sciences

Disaster mapping and monitoring

Geospatial

intelligence

discovery

HP Visualization

Knowledge

Discovery

Actionable insights/ knowledge

Extremes

Social networks

Efficient storage and retrieval methods

Terabytes of spatially referenced data per day

National

security

HPC

Errors and

uncertainty

GeoDatabases

Petabytes of spatiotemporal data archives


Pattern detection and decision support

?

Pattern detection and decision support

Consequences

Additional data hypothesis generation

Action

End-user

Analyst

Process modeling

Event summary

Decision

Offline

Predictive analysis

Body of actionable knowledge

HIT

Decision support

Pattern detection

Online

Decision making

Data integration

Stream of new data


Spatial classification and prediction

Spatial classification and prediction

  • Spatial autoregressive regression (SAR)

  • Markov random fields (MRF)

SAR

W matrix

IKNOS

1 m

TM

30 m

60,000

3,600,000,000

60 km

2000

60 km

2000

60,000

3,600,000,000

Overlap

  • Challenge

    • Large neighborhood matrices

    • Overlapping computation

Node 1

Node 2


Extremes

Extremes

  • A new measure to describe the relationship among extremes in space and time:

    • Simultaneous occurrence of 100-year return levels:

      • Perfect dependence ï‚® 100 year event.

      • No dependence ï‚® 10000 year event.

Correlation

TailDependence

Correlation

Tail Dependence

Rainfall observations in South America

Simulations from CCSM3 Climate Model

  • Challenge

    Spatiotemporal dependence estimation is computationally expensive (1000 grid cells results in scanning more than half a million pairs).


Managing uncertainty in spatiotemporal environments muse

Managing uncertainty in spatiotemporal environments (MUSE)

  • Reasoning

  • Data mining

  • Spatial analysis

  • Visualization

Knowledge Discovery

  • Challenge

    • Expensive query processing and spatial analysis

Propagation

Modeling

  • Conceptual

  • Logical

GeoDB

Representation

Distributed asynchronous update framework

Updates

  • Ontology

  • Semantic

  • Reasoning

Data collection

source of uncertainty

Source


Contacts

Contacts

  • Ranga Raju Vatsavai

  • Geographic Information Science and Technology

  • Computational Sciences and Engineering

  • (865) 576-3569

  • [email protected]

  • Auroop Ganguly

  • Geographic Information Science and Technology

  • Computational Sciences and Engineering

  • (865) 241-1305

  • [email protected]

  • Budhendra Bhaduri

  • Geographic Information Science and Technology

  • Computational Sciences and Engineering

  • (865) 241-9272

  • [email protected]

7 Bhaduri_GDS_SC07


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