Comparison of Haversine and Euclidean Distance Formulas for Calculating Distance Between Regencies in West Sumatra

Authors

  • Vinka Haura Nabilla Unversitas Negeri Padang
  • Dina Fitria
  • Dony Permana
  • Fadhilah Fitri

DOI:

https://doi.org/10.24036/ujsds/vol1-iss3/39

Keywords:

Euclidean distance, Haversine, latitude, longtitude, z test

Abstract

A distance is a number that indicates how far apart two place are. The benefits of using distance are widely used in research, one of which is in the application of spatial weighting matrices. The spatial weight matrix is obtained based on proximity information between regions. There are two types of spatial weights, namely, based on contiguity and distance. Determining the proximity of regions in West Sumatra is better to use spatial weighting based on distance because in West Sumatra there are islands and mountains that limit the regions. Some distance estimation equations that can be utilized are Haversine and Euclidean distance. The connection between the two points in Haversine takes into account the earth's curvature when calculating the distance, which is a difference between the two formulas. In contrast, the Euclidean distance method uses a straight line to connect two points. The purpose of this research is to ascertain whether the Haversine and Euclidean distance formulas produce significantly different results in terms of distance. Calculation of the coordinate point distance utilizes latitude and longitude obtained from Google Maps. The distances measured using both formulas were expressed as kilometers (km), then the data was processed using the z test. The findings demonstrated that the Haversine formula and the Euclidean distance formula did not significantly differ in the process of calculating distance.

Published

2023-05-31

How to Cite

Nabilla, V. H., Dina Fitria, Dony Permana, & Fadhilah Fitri. (2023). Comparison of Haversine and Euclidean Distance Formulas for Calculating Distance Between Regencies in West Sumatra. UNP Journal of Statistics and Data Science, 1(3), 120–125. https://doi.org/10.24036/ujsds/vol1-iss3/39

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