iRedistrict: Powerful Spatial Optimization Guided by Human Judgment

Introduction

Redistricting is a challenging spatial optimization problem that involves dividing geographic areas into contiguous districts while satisfying multiple criteria and constraints, such as population equality and geographic compactness. The vast solution space, competing objectives, and often subjective criteria make human judgment and domain knowledge essential.

iRedistrict combines powerful computational optimization with human expertise and interactive visual exploration. It enables users to flexibly define criteria and constraints, evaluate trade-offs, and iteratively develop high-quality redistricting plans that satisfy mandatory requirements and user preferences.

Beyond electoral redistricting, iRedistrict has been applied to a range of real-world planning problems, including school attendance boundary design, municipal electoral districting, service delivery zone design, and healthcare service planning.

@article{guo2011iredistrict,
  author = {Guo, Diansheng and Jin, Hai},
  title = {iRedistrict: Geovisual analytics for redistricting optimization},
  journal = {Journal of Visual Languages \& Computing},
  year = {2011},
  volume = {22},
  number = {4},
  pages = {279--289},
  publisher = {Elsevier BV},
  doi = {10.1016/j.jvlc.2011.03.001},
  url = {https://doi.org/10.1016/j.jvlc.2011.03.001},
}

@misc{jin2026fasteffectiveredistrictingoptimization,
  author = {Hai Jin and Diansheng Guo},
  title = {Fast and Effective Redistricting Optimization via Composite-Move Tabu Search},
  year = {2026},
  doi = {10.48550/arXiv.2605.06682},
  url = {https://arxiv.org/abs/2605.06682},
  eprint = {2605.06682},
  archivePrefix = {arXiv},
}