Graph-Based Unsupervised Learning for Spatial Data

Spatial formulations for defining spatially-contiguous regions in geospatial data

References

2023

  1. skater_prob.png
    Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations
    Orhun Aydin, Mark V Janikas, Renato Martins Assunção, and 1 more author
    Geographical Analysis, 2023

2021

  1. aydin_reg_comp.png
    A quantitative comparison of regionalization methods
    Orhun Aydin, Mark V Janikas, Renato Martins Assunção, and 1 more author
    International Journal of Geographical Information Science, 2021

2018

  1. skater_con.png
    SKATER-CON: Unsupervised regionalization via stochastic tree partitioning within a consensus framework using random spanning trees
    Orhun Aydin, Mark V Janikas, Renato Assunçao, and 1 more author
    In Proceedings of the 2nd ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery, 2018