Evaluating the 2022 Philippine Senate Election voting patterns extracted using hyperspectral unmixing

Authors

  • Daniel Sebastianne B. Daiz ⋅ PH National Institute of Physics, University of the Philippines Diliman
  • May T. Lim ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

Using precinct-level election data for the 2022 Senate election, we extracted several voting patterns using two hyperspectral unmixing algorithms. Candidates with higher vote share tend to have stronger affinity to an archetype or voting pattern. Among the extracted archetypes, we found correspondence to administration-allied candidates, and the opposition. Using the extracted patterns as basis, and following unmixing, we reconstructed the precinct level data to determine the relative reconstruction error (RRE). The VCA algorithm had higher RRE compared to the R-CoNMF algorithm. The latter algorithm was also able to accurately reconstruct the order or ranking of all the candidates. Consistent with expectation, we found that all winning candidates had low RRE, significant vote counts or high median, and narrow distributions or low IQR.

Downloads

Issue

Article ID

SPP-2024-PB-12

Section

Poster Session B (Complex Systems, Computational Physics, and Astrophysics)

Published

2024-06-28

How to Cite

[1]
DSB Daiz and MT Lim, Evaluating the 2022 Philippine Senate Election voting patterns extracted using hyperspectral unmixing, Proceedings of the Samahang Pisika ng Pilipinas 42, SPP-2024-PB-12 (2024). URL: https://proceedings.spp-online.org/article/view/SPP-2024-PB-12.