Use of Geostatistics on Absolute Positional Accuracy Assesment of Geospatial Data

Abstract

Abstract:In the area of Geosciences it is intuitive to think of spatial correlation as a phenomenon under study, and Geostatistics has tools to identify and represent the behavior of such dependency. The spatial analysis of the results of an inspection of the quality of a cartographic product is generally not addressed in the standards, which are restricted to descriptive and tabular findings, based on the assumption of the Classical Statistics of independence of observed data. At the Brazilian National Infrastructure of Spatial Data (INDE), various cartographic products should be made available to society, along with their metadata. This paper proposes a methodology for quality inspection based on international standards and on the Cartographic Accuracy Standard (PEC), using geostatistical methods and spatial representation of this benchmarking, through positional quality maps. The method of evaluating the quality of data was applied to Brazil's Continual Cartographic Base, at the scale of 1:250000 -BC250, with a focus on absolute positional accuracy. The quality map generated presented regionalizations of the planimetric error confirmed by the producer team of the referred cartographic base of IBGE. Such information can help users and producers to understand the spatial behavior of cartographic product quality in study.

Comparison Between Absolute and Relative Positional Accuracy Assessment - A Case Study Applied to Digital Elevation Models

“…The assessment of positional accuracy of geospatial data corresponds to a statistical analysis of the feature discrepancies identified on the map in relation to a given reference (DoD, 1990). Depending on the characteristics of the reference, positional accuracy has the following classification (Santos et al, 2017):…”

Geometry accuracy of DSM in water body margin obtained from an RGB camera with NIR band and a multispectral sensor embedded in UAV

“…The independence test of the data can be made by geostatistical analysis (Santos, 2015;Santos, Medeiros, Santos, & Lisboa Filho, 2017), using the experimental semivariogram. The experimental semivariogram for each positional discrepancy (EN and h) are obtained from the calculation of the semivariances σ GNSS UAV given by Equation 3, where N(h) is the number of positional discrepancy values pairs of dp(x i ) and dp(x i + h) separated by a distance of h.…”

“…Then, the correlation of the standardized residuals with the positional discrepancies is verified. If the correlation is greater than 0.6 (strong correlation), it is followed by the verification of the accuracy based on the standard residue, if it is less than 0.6 (weak correlation), the semivariogram should be revised (Santos, 2015;Santos et al, 2017).…”

Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia

“…At the analysis level, to obtain the precise outputs for disease mapping, the quality of the spatial data used in the analysis should be evaluated based on a number of elements, i.e., (1) positional accuracy, (2) thematic accuracy, (3) temporal accuracy, (4) completeness, (5) logical consistency, and ( 6) usability [20]. Disease incidence data may be represented differently when mapping disease cases or counts within tracts, compared to mapping disease structures based mainly on estimates of complex models [11].…”