CyberGISX (alpha)

The current implementation of CyberGISX is a proof-of-concept system designed to enhance cutting-edge cyberGIS for enabling diverse geospatial research and education with a particular focus on evaluating CyberGIS-Jupyter in various educational settings.

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WhereCOVID-19 notebooks map multi-scale spread of COVID-19 and assess related impacts by integrating diverse geospatial data, analysis, and modeling capabilities based on CyberGIS-Jupyter, an innovative cyberGIS framework for conducting data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebooks as the primary user environment.

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CyberGISX notebooks include geospatial codes, workflows with comments, and visualizations providing a solid foundation for conducting computationally reproducible research and education.

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Spatial Interpolation

Spatial interpolation is used to predicts values for cells in a raster from a limited number of sample data points around it. We are studying streaming high-frequency temperature data in Chicago retrieved from Array of Thing (AoT). Kriging is a family of estimators used to interpolate spatial data. This family includes ordinary kriging, universal kriging, indicator kriging, co-kriging, and others (Taken from Lefohn et al., 2005). The choice of which kriging to use depends on the characteristics of the data and the type of spatial model desired. The most commonly used method is ordinary kriging, which was selected for this study. Reference:

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Spatial Interpolation The Context Makes the Difference: Reproducibility and Replicability in Measures of Spatial Accessibility Python Geospatial Libraries

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