Hands-on Tutorials

Plotting continuous and categorical geospatial data scattered in a 3D coordinate system, applied on a mining dataset using Matlab

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Canadian Malarctic open-pit mine (photo by the author)

Subsurface borehole data is collected by drilling and extracting rock or soil core and consists of samples scattered in 3D space that measure different continuous or categorical variables. Each sample records: (1) the 3D spatial coordinates represented by an easting, northing, and elevation, (2) continuous variables such as element concentrations, contaminants, ore grades, or temperature to name a few, and (3) categorical variables such as lithology, alteration, or mineralization units.


How minor figure modifications and visualization decisions can result in clearer, more informative plots

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The field of data visualization started with the development of fundamental graphical designs by William Playfair in the late eighteenth century and has advanced to the point where trillions of images of statistical graphics are published every year. The importance of good data visualization skills is increasing as the datasets become larger and more complex in the current information age.

Regardless of the targeted use, the most effective designed data graphics are the simplest figures that convey the intended information. …


VIDEO TUTORIAL

A Python tutorial for dealing with the biased sampling of spatial data with a realistic example from the environmental industry

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Often times several decisions regarding a site or region are made based on statistical analyses of irregularly scattered geospatial data. The values of variables of interest for different applications are often impacted differently due to heterogeneities throughout a site. For example, meteorological data such as temperature could be influence by proximity to bodies of water, contaminant concentrations would be linked to directions of groundwater flow, natural resources such as ore, hydrocarbon, or forestry could be tied to the geological mediums in the subsurface.

If areas with anomalously high or low values for a given variable are sampled disproportionately to the average sampling for other areas in a given site, there will likely be a biased difference in the true distribution for that variable. When surveying a site, the areas that contain these anomalous values are often preferentially sampled because they represent areas of interest that we want to better understand. To avoid the biased difference which arises from preferential sampling, samples should be collected at regular gridded intervals or at random. The figure below illustrates how regular and random sampling does not skew the statistics of a site while biased sampling does. …


Getting Started

Quantifying the effects of varying different inputs, applied on a gemstone dataset with over 50K round-cut diamonds

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Photo by Mick Haupt on Unsplash

Sensitivity analyses involve varying a system’s inputs to assess the individual impacts of each variable on the output and ultimately provide information regarding the different effects of each tested variable. Sensitivity analyses are typically used in a variety of disciplines such as in business for financial modeling, or in engineering to optimize efficiency in a given system. If used correctly, the sensitivity analysis can be a powerful tool for revealing additional insights that would have otherwise been missed.

While data scientists are great at modeling and creating actionable information based on the understanding and interpretation of datasets or workflows, the sensitivities of basic inputs are often ignored. Conducting a simple sensitivity analysis could add value to a data science project by providing additional information to stakeholders for making more informed decisions. While implementing sensitivity analyses would not be feasible or desirable for certain tasks, they could serve as an additional exploratory tool for data scientists to derive additional insights from multivariate datasets. …


Tutorial for retrieving, processing, and visualizing elevation data with an application in the oil & gas industry

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Topographical map of Earth using a cylindrical projection and NASA’s bluemarble colormap. Image by the author using the Matplotlib Basemap Toolkit

Topographic maps represent Earth’s 3D landscape as a 2D map and are used to visualize both anthropogenic and natural features in their geographical context. The specific applications for topographic maps vary depending on the scale of the map. Some uses include planning transportation routes, guiding travelers, and delineating the location and extent of specific areas. Topographic maps also serve as excellent basemaps for plotting other geospatial datasets.

About

Fouad Faraj

Geological engineer with interests in geospatial data processing and numerical analysis for applications in the natural resource and environmental industries.

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