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- W4220921669 abstract "Abstract Updating reference maps is the focus of research on the use of voluntary geographic information (VGI) in countries with spatial data deficits. However, data quality needs to be known so collaborative data can fill the geospatial data gaps in this situation. Most research uses comparative metrics for this purpose (i.e. they compare collaborative data with official data considered more reliable). However, this study proposes a method for cases with uneven coverage of available reference data. Therefore, we propose to use the geographically weighted regression method with the definition of collective spatial intelligence as a parameter to explain the positional accuracy of collaborative data. The number of collaborators and edits observed on OpenStreetMap features was used to represent this parameter. The results show that such variables are significant and can explain up to 93% of the positional accuracy, which points the way to estimate the positional accuracy of VGI without direct comparisons between collaborative data and data from official sources." @default.
- W4220921669 created "2022-04-03" @default.
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- W4220921669 date "2022-03-04" @default.
- W4220921669 modified "2023-10-16" @default.
- W4220921669 title "Inference of positional accuracy of collaborative data from intrinsic parameters" @default.
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- W4220921669 doi "https://doi.org/10.1111/tgis.12913" @default.
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