DATA QUALITY CONTROL AND EXPLORATORY DATA ANALYSIS
Geostatistics gives you the tools for data quality control, helping you investigate, analyze, clean, and validate any geospatial dataset. Contamination data quality directly affects characterization reliability. A poor-quality data means an untrustworthy characterization and an increased risk of bad decision-making. It is why you must be sure of the quality of your data before using them.
Visualize your data in its environmental context
A first way of controlling data is to visualize them in 2D/3D. Qualitative and quantitative information (historical information, indirect measurements, aerial views, topography, geology, or physical models) may be gathered into a single view. It helps you gain a better understanding of the contamination history and evolution. Geovariances software Kartotrak has a specific feature that allows identifying GPS errors and correct data position. 3D data visualization and log display make it possible to navigate inside the data and search for anomalies.
Geostatistics offers powerful tools for exploratory data analysis and investigation. Thorough data analysis gives a better understanding of the nature of data and allows cross-checking different data acquisition campaigns. Conventional statistical tools, like histograms, q-q plots, correlation curves, are used for identifying data distribution, outliers, irregularities, and heterogeneities.
Combining geostatistical and statistical tools such as the variogram cloud and the base map is even more powerful to localize outliers and abnormal values. It easily puts into evidence points with significant variability in comparison to surrounding points.
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