English

MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies

Human-Computer Interaction 2019-10-09 v3

Abstract

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.

Cite

@article{arxiv.1907.13601,
  title  = {MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies},
  author = {Jieqiong Zhao and Morteza Karimzadeh and Luke S. Snyder and Chittayong Surakitbanharn and Zhenyu Cheryl Qian and David S. Ebert},
  journal= {arXiv preprint arXiv:1907.13601},
  year   = {2019}
}

Comments

To appear in 2019 IEEE Transactions on Visualization and Computer Graphics

R2 v1 2026-06-23T10:36:24.069Z