Haversines are not Slow

The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y Ríos (1795) < https://books.google.cat/books?id=030t0OqlX2AC> (In Spanish).

Fast, Dependency-Free Geodesic Distance Calculations

Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.

Voter Distance to Polling Locations

Calculates the distance between each voter in a voter file (given lat/long coordinates or sf point geometries) and multiple polling or vote-by-mail drop box locations. Returns nearest location, k-nearest locations, or all locations within a distance threshold. Core computation uses the Haversine formula implemented in C++ via 'Rcpp'.

Smallest Enclosing Disc for Latitude and Longitude Points

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>.

Leader Clustering Algorithm

The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.

Projected Actor Locations for Spatial Interaction Modeling

Implements the Projected Actor Locations (PALS) method for spatial modeling of dyadic interactions between geographically mobile actors, as described in Kim, Liu and Desmarais (2023) . PALS applies exponential-smoothing weights to the spatiotemporal histories of a focal actor and its interaction partners ("alters") to project the location of future interactions. The package provides projection, maximum-similarity parameter estimation by minimizing great-circle (Haversine) prediction error, nonparametric bootstrap with multiple-imputation (Rubin's Rules) pooling, dyadic distance covariate construction, visualization, and a simulated example dataset of subnational conflict.