Abstract
Spatial data conflation is a key task for consolidating geographic knowledge from different data sources covering overlapping regions that were gathered using different methodologies and objectives. Nowadays this research area is becoming more challenging because of the increasing size and number of overlapping spatial data sets being produced. This paper presents an approach towards distributed vector to vector conflation, which can be applied to overlapping heterogeneous spatial data sets through the implementation of Web Processing Services
(WPS). Initial results show that distributed spatial conflation can be effortlessly achieved if during the pre-processing phase disjoint clusters are created. However, if this is not possible further horizontal conflation algorithms are applied to neighbor clusters before obtaining the final data set.
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BibTeX (Download)
@inproceedings{freitas2012distributed,
title = {Distributed Vector based Spatial Data Conflation Services},
author = { Sérgio Freitas and Ana Paula Afonso},
editor = {Laercio M. Namikawa and Vania Bogorny},
url = {http://www.geoinfo.info/geoinfo2012/papers/freitas.pdf},
issn = {2179-4847},
year = {2012},
date = {2012-01-01},
pages = {23-29},
abstract = {Spatial data conflation is a key task for consolidating geographic knowledge from different data sources covering overlapping regions that were gathered using different methodologies and objectives. Nowadays this research area is becoming more challenging because of the increasing size and number of overlapping spatial data sets being produced. This paper presents an approach towards distributed vector to vector conflation, which can be applied to overlapping heterogeneous spatial data sets through the implementation of Web Processing Services
(WPS). Initial results show that distributed spatial conflation can be effortlessly achieved if during the pre-processing phase disjoint clusters are created. However, if this is not possible further horizontal conflation algorithms are applied to neighbor clusters before obtaining the final data set.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}