Functionality for adding the geological timescale to bivariate plots.

Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.

Understanding spatial association is essential for spatial 
             statistical inference, including factor exploration and spatial prediction. 
             Geographically optimal similarity (GOS) model is an effective method 
             for spatial prediction, as described in Yongze Song (2022) 
             <doi:10.1007/s11004-022-10036-8>. GOS was developed based on 
             the geographical similarity principle, as described in Axing Zhu (2018) 
             <doi:10.1080/19475683.2018.1534890>. GOS has advantages in 
             more accurate spatial prediction using fewer samples and 
             critically reduced prediction uncertainty.

R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' 
    <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a
    package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access
    for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring 
    the same simple features access like sf but running on Spark distributed system.

A comprehensive toolkit for geospatiotemporal analysis
    featuring 60+ vegetation indices, advanced raster visualization,
    universal spatial mapping, water quality analysis, CDL crop analysis,
    spatial interpolation, temporal analysis, and terrain analysis.
    Designed for agricultural research, environmental monitoring, remote
    sensing applications, and publication-quality mapping with support for
    any geographic region and robust error handling. Methods include
    vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced
    vegetation indices (Huete et al. 1997)
    <doi:10.1016/S0034-4257(97)00104-1>, spatial interpolation techniques
    (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters
    1996) <doi:10.1080/01431169608948714>, and crop data layer analysis
    (USDA NASS 2024)
    <https://www.nass.usda.gov/Research_and_Science/Cropland/>.  Funding:
    This material is based upon financial support by the National Science
    Foundation, EEC Division of Engineering Education and Centers, NSF
    Engineering Research Center for Advancing Sustainable and Distributed
    Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research
    Centers award 2133576.