Challenges in data integration and interoperability in geovisual analytics

Geographic information technologies are evolving from stand-alone systems to a distributed model of independent web services. In parallel, voluminous geographic data are being collected with modern data acquisition techniques such as remote sensing and personal navigation devices. There is an urgent...

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Published in:Journal of Location Based Services
Main Authors: Turdukulov, Ulanbek, Blok, C., Köbben, B., Morales, J.
Format: Article in Journal/Newspaper
Language:unknown
Published: 2010
Subjects:
Online Access:https://hdl.handle.net/20.500.11937/15838
https://doi.org/10.1080/17489725.2010.532815
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spelling ftcurtin:oai:espace.curtin.edu.au:20.500.11937/15838 2023-06-11T04:06:27+02:00 Challenges in data integration and interoperability in geovisual analytics Turdukulov, Ulanbek Blok, C. Köbben, B. Morales, J. 2010 restricted https://hdl.handle.net/20.500.11937/15838 https://doi.org/10.1080/17489725.2010.532815 unknown http://hdl.handle.net/20.500.11937/15838 doi:10.1080/17489725.2010.532815 Journal Article 2010 ftcurtin https://doi.org/20.500.11937/1583810.1080/17489725.2010.532815 2023-05-30T19:28:13Z Geographic information technologies are evolving from stand-alone systems to a distributed model of independent web services. In parallel, voluminous geographic data are being collected with modern data acquisition techniques such as remote sensing and personal navigation devices. There is an urgent need for effective and efficient methods to integrate and explore relationships between remote sensing and trajectory datasets. When it comes to integration, one would commonly rely on a conventional chain of GIS operations to match trajectory locations to grid values: download grid data, georeference, match each trajectory record to a corresponding image cell, perform overlay, extract cell values for a given location and time and compose values into a resulting table. If one has to deal with large and dynamic spatio-temporal data sets, this approach is clearly unmanageable. We propose an alternative approach: a four-layered system architecture that utilises web services for the integration of trajectory and remote sensing data. We demonstrate how this integration service can be embedded into distributed components for manipulation, analysis and visualisation of geospatial trajectories, using Antarctic iceberg trajectories and wind data as a case study. The prototype can be accessed on the web. Future research will include optimisation and integration of more variables extracted from grid data sets, but the main focus will be on extension of the analytical component by implementing more mechanisms to discover patterns in the integrated data set, and on better visualisation. © 2010 Taylor & Francis. Article in Journal/Newspaper Antarc* Antarctic Iceberg* Curtin University: espace Antarctic Journal of Location Based Services 4 3-4 166 182
institution Open Polar
collection Curtin University: espace
op_collection_id ftcurtin
language unknown
description Geographic information technologies are evolving from stand-alone systems to a distributed model of independent web services. In parallel, voluminous geographic data are being collected with modern data acquisition techniques such as remote sensing and personal navigation devices. There is an urgent need for effective and efficient methods to integrate and explore relationships between remote sensing and trajectory datasets. When it comes to integration, one would commonly rely on a conventional chain of GIS operations to match trajectory locations to grid values: download grid data, georeference, match each trajectory record to a corresponding image cell, perform overlay, extract cell values for a given location and time and compose values into a resulting table. If one has to deal with large and dynamic spatio-temporal data sets, this approach is clearly unmanageable. We propose an alternative approach: a four-layered system architecture that utilises web services for the integration of trajectory and remote sensing data. We demonstrate how this integration service can be embedded into distributed components for manipulation, analysis and visualisation of geospatial trajectories, using Antarctic iceberg trajectories and wind data as a case study. The prototype can be accessed on the web. Future research will include optimisation and integration of more variables extracted from grid data sets, but the main focus will be on extension of the analytical component by implementing more mechanisms to discover patterns in the integrated data set, and on better visualisation. © 2010 Taylor & Francis.
format Article in Journal/Newspaper
author Turdukulov, Ulanbek
Blok, C.
Köbben, B.
Morales, J.
spellingShingle Turdukulov, Ulanbek
Blok, C.
Köbben, B.
Morales, J.
Challenges in data integration and interoperability in geovisual analytics
author_facet Turdukulov, Ulanbek
Blok, C.
Köbben, B.
Morales, J.
author_sort Turdukulov, Ulanbek
title Challenges in data integration and interoperability in geovisual analytics
title_short Challenges in data integration and interoperability in geovisual analytics
title_full Challenges in data integration and interoperability in geovisual analytics
title_fullStr Challenges in data integration and interoperability in geovisual analytics
title_full_unstemmed Challenges in data integration and interoperability in geovisual analytics
title_sort challenges in data integration and interoperability in geovisual analytics
publishDate 2010
url https://hdl.handle.net/20.500.11937/15838
https://doi.org/10.1080/17489725.2010.532815
geographic Antarctic
geographic_facet Antarctic
genre Antarc*
Antarctic
Iceberg*
genre_facet Antarc*
Antarctic
Iceberg*
op_relation http://hdl.handle.net/20.500.11937/15838
doi:10.1080/17489725.2010.532815
op_doi https://doi.org/20.500.11937/1583810.1080/17489725.2010.532815
container_title Journal of Location Based Services
container_volume 4
container_issue 3-4
container_start_page 166
op_container_end_page 182
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