APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE

The scarcity of metrics for analysing the quality of Voluntary Geographic Information without direct comparisons with reference data makes it impossible to use this information in many areas of society. Especially in developing countries, where collaborative data can help fill the deficit of officia...

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Main Authors: C. A. Paiva, R. G. Campos, S. P. Camboim
Format: Article
Language:English
Published: Copernicus Publications 2021-08-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W2-2021/119/2021/isprs-archives-XLVI-4-W2-2021-119-2021.pdf
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spelling doaj-3d7ffc897f4448b397a2c8d1fad1b5312021-08-23T07:04:23ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-08-01XLVI-4-W2-202111912210.5194/isprs-archives-XLVI-4-W2-2021-119-2021APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCEC. A. Paiva0R. G. Campos1S. P. Camboim2Geodetic Science Graduate Program, Department of Geomatics, Federal University of Parana, Curitiba 81531970, BrazilGeodetic Science Graduate Program, Department of Geomatics, Federal University of Parana, Curitiba 81531970, BrazilGeodetic Science Graduate Program, Department of Geomatics, Federal University of Parana, Curitiba 81531970, BrazilThe scarcity of metrics for analysing the quality of Voluntary Geographic Information without direct comparisons with reference data makes it impossible to use this information in many areas of society. Especially in developing countries, where collaborative data can help fill the deficit of official data, studies on intrinsic parameters of quality become an alternative to conventional comparative methods for evaluating spatial data. A recurring parameter in related research is Collective Spatial Intelligence. Seeking to offer researchers on the subject a tool capable of measuring the Collective Spatial Intelligence in predefined areas, we developed a Python application that counts representative values of this intelligence in political-administrative limits. Considering that, in general, the quality of spatial data is inferred on these limits, research that seeks to explain the VGI quality without using official data as a reference can be facilitated.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W2-2021/119/2021/isprs-archives-XLVI-4-W2-2021-119-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author C. A. Paiva
R. G. Campos
S. P. Camboim
spellingShingle C. A. Paiva
R. G. Campos
S. P. Camboim
APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet C. A. Paiva
R. G. Campos
S. P. Camboim
author_sort C. A. Paiva
title APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
title_short APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
title_full APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
title_fullStr APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
title_full_unstemmed APPLICATION FOR MEASURING REPRESENTATIVE VARIABLES OF COLLECTIVE SPACE INTELLIGENCE
title_sort application for measuring representative variables of collective space intelligence
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2021-08-01
description The scarcity of metrics for analysing the quality of Voluntary Geographic Information without direct comparisons with reference data makes it impossible to use this information in many areas of society. Especially in developing countries, where collaborative data can help fill the deficit of official data, studies on intrinsic parameters of quality become an alternative to conventional comparative methods for evaluating spatial data. A recurring parameter in related research is Collective Spatial Intelligence. Seeking to offer researchers on the subject a tool capable of measuring the Collective Spatial Intelligence in predefined areas, we developed a Python application that counts representative values of this intelligence in political-administrative limits. Considering that, in general, the quality of spatial data is inferred on these limits, research that seeks to explain the VGI quality without using official data as a reference can be facilitated.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W2-2021/119/2021/isprs-archives-XLVI-4-W2-2021-119-2021.pdf
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