@article{balaka2026hamcat,
author = {Balaka, Hanna and Hauser, Helwig and Garrison, Laura Ann},
title = {HamCat: Ego-Centric Relationship Exploration for Multidimensional Categorical Data},
year = {2026},
journal = {Computer Graphics Forum},
pages = {e70440},
doi = {10.1111/cgf.70440},
abstract = {We introduce HamCat, a novel visualization method for exploring and analyzing multidimensional categorical survey data. Typical visualization approaches for multidimensional categorical data do not support simultaneous analysis of attributes and items, nor do they allow for in-depth similarity analysis of an entire dataset from the perspective of a specific reference point. HamCat, in contrast, aims to facilitate detailed analysis of multidimensional categorical data across both attributes and items. Our approach builds on the concept of a Hamming ball combined with a force-directed layout to support ego-centric, user-steered analysis of inter-item and inter-attribute relationships in multidimensional categorical survey data. In addition, our method supports the inclusion and nuanced visualization of missingness. We illustrate the value of HamCat through two case studies. The first case focuses on a survey on wellbeing collected by the European Social Survey, while the second is an expert-driven study for a survey on sense of belonging in computer science higher education. These case studies show how HamCat complements existing analysis workflows to reveal relationships and item groupings across attributes that are not easily discoverable through conventional means. Supplementary materials for our method are available at https://osf.io/uz2jv/.},
pdf = {pdfs/balaka2026hamcat.pdf},
images = {images/balaka2026hamcat.png},
thumbnails = {images/balaka2026hamcat_thumb.png},
}