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|a 10.1109/TVCG.2019.2934603
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|a eng
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1 |
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|a Zhao, Jieqiong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a MetricsVis
|b A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies
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|c 2020
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|a Date Completed 04.01.2021
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|a Date Revised 04.01.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Karimzadeh, Morteza
|e verfasserin
|4 aut
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1 |
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|a Snyder, Luke S
|e verfasserin
|4 aut
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|a Surakitbanharn, Chittayong
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|a Qian, Zhenyu Cheryl
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|a Ebert, David S
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|t IEEE transactions on visualization and computer graphics
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|g 26(2020), 1 vom: 19. Jan., Seite 1193-1203
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|g year:2020
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|g day:19
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|g pages:1193-1203
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