Measuring conflation success

We are immersed in the Big Data era, where there is a large amount of heterogeneous data, both in time and spatial scales. This data starts to be streamed in real time from different devices and sensors, well illustrated by the new concept of Smart Cities. Conflation processes play an important role...

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Main Authors: Marta Padilla-Ruiz, Carlos López-Vázquez
Format: Article
Language:English
Published: Instituto Panamericano de Geografía e Historia 2017-04-01
Series:Revista Cartográfica
Subjects:
Online Access:https://revistasipgh.org/index.php/rcar/article/view/341
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author Marta Padilla-Ruiz
Carlos López-Vázquez
author_facet Marta Padilla-Ruiz
Carlos López-Vázquez
author_sort Marta Padilla-Ruiz
collection DOAJ
description We are immersed in the Big Data era, where there is a large amount of heterogeneous data, both in time and spatial scales. This data starts to be streamed in real time from different devices and sensors, well illustrated by the new concept of Smart Cities. Conflation processes play an important role in this scenario, defined as the procedure for the combination and integration of different data sources, improving the level of information of the result. It also allows to update geographical databases (GDB), conflating different kind of sources where one of them is more accurate or updated than the other. Regarding geometric conflation, the procedure involves transforming features from one data source to another, minimizing the geometric discrepancies between them. Accuracy has to be taken into account in these processes, and the results need to be measured and evaluated in order to have a better understanding of product quality. In this paper, conflation evaluation process is described along with the different metrics and approaches to assess its accuracy.
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spelling doaj.art-0c3220889f044960ab78b8a71f95c0772022-12-21T20:20:36ZengInstituto Panamericano de Geografía e HistoriaRevista Cartográfica0080-20852663-39812017-04-019410.35424/rcarto.i94.341Measuring conflation successMarta Padilla-RuizCarlos López-VázquezWe are immersed in the Big Data era, where there is a large amount of heterogeneous data, both in time and spatial scales. This data starts to be streamed in real time from different devices and sensors, well illustrated by the new concept of Smart Cities. Conflation processes play an important role in this scenario, defined as the procedure for the combination and integration of different data sources, improving the level of information of the result. It also allows to update geographical databases (GDB), conflating different kind of sources where one of them is more accurate or updated than the other. Regarding geometric conflation, the procedure involves transforming features from one data source to another, minimizing the geometric discrepancies between them. Accuracy has to be taken into account in these processes, and the results need to be measured and evaluated in order to have a better understanding of product quality. In this paper, conflation evaluation process is described along with the different metrics and approaches to assess its accuracy.https://revistasipgh.org/index.php/rcar/article/view/341ConflationData fusionData integrationSpatial AccuracyConflation Success
spellingShingle Marta Padilla-Ruiz
Carlos López-Vázquez
Measuring conflation success
Revista Cartográfica
Conflation
Data fusion
Data integration
Spatial Accuracy
Conflation Success
title Measuring conflation success
title_full Measuring conflation success
title_fullStr Measuring conflation success
title_full_unstemmed Measuring conflation success
title_short Measuring conflation success
title_sort measuring conflation success
topic Conflation
Data fusion
Data integration
Spatial Accuracy
Conflation Success
url https://revistasipgh.org/index.php/rcar/article/view/341
work_keys_str_mv AT martapadillaruiz measuringconflationsuccess
AT carloslopezvazquez measuringconflationsuccess