MetricsVis: A visual analytics framework for performance evaluation of law enforcement officers

Zhao, J. L.; Malik, Abish; Xu, Haidi; Wang, Guizhen; Zhang, Jiawei; Surakitbanharn, Chittayong; Ebert, David S.

doi:10.1109/ths.2017.7943468

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…For a color image with more information, see http://pubs.apa.org/books/ supp/woo improvement across departments. To facilitate this process, we designed a visual analytics application called MetricsVis that supports data-driven, multicriteria performance evaluation of employees (Zhao et al, 2017). Specifically, the system allows supervisors to both interactively customize evaluation metrics by defining what data characteristics constitute exemplar performance and discover influential factors that can improve resource allocation, strategic planning, and operational decision making.…”

Section: Performance Evaluation For Law Enforcement Agenciesmentioning

confidence: 99%

Human-guided visual analytics for big data.

Karimzadeh

Zhao

Wang

et al. 2020

Big Data in Psychological Research.

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I n this chapter, we provide an overview of the research and practice in visual analytics with a specific focus on decision-support systems that facilitate generating useful information from big, unstructured, and complex data. We first define what is usually referred to as big data and its unique characteristics. We then define visual analytics and human-computer collaborative decisionmaking (HCCD) environments, compare and contrast human-in-the-loop analysis methods with automated algorithms such as machine learning models, and explain how these approaches complement each other for real-world problem solving. To ground our discussions, we provide an overview of four exemplar visual analytics systems with applications in various domains, including humanitarian relief, social media analytics, critical infrastructure vulnerability modeling, resource allocation, and performance evaluation using multidimensional data. MOTIVATION AND OPPORTUNITYAdvanced analytics and computational algorithms enable the transformation of the evolving deluge of digital data into useful and actionable information. However, as data sets continue to increase in size and complexity in the digital

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Section: Performance Evaluation For Law Enforcement Agenciesmentioning

confidence: 99%

Human-guided visual analytics for big data.

Karimzadeh

Zhao

Wang

et al. 2020

Big Data in Psychological Research.

View full text Add to dashboard Cite

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“…We conducted this crowdsourcing online survey with two participating groups: police officers and community citizens, each rating the severity and economic impact of each offense category on a Likert scale. More details about the survey are available in our previous work [47]. MetricsVis transformed these crowdsourced ratings to weights, which can be assigned either based on the average rating from the survey or on interactive adjustment from end-users.…”

Section: Deriving Performance Metricsmentioning

confidence: 99%

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

Zhao

Karimzadeh

Snyder

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. MetricsVis overview: The priority adjustment view (2) encodes the crowdsourced crime severity ratings from police officers and citizens (perceived importance of factors); the red dots indicate the currently assigned weights used in the evaluation metrics. The projection view (6) shows the dimensionality reduction results. The group performance view (5) contains three visual representations that show an overview of group performance and the contribution of each member. The performance matrix view (3) displays the individual employee performance with employees in columns and job types in rows (here, employees are sorted based on their group first and then their total performance scores). The control panel shows the filters (1) and grouping method (4) applied in use case 1.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.

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MetricsVis: A visual analytics framework for performance evaluation of law enforcement officers

Cited by 2 publications

References 11 publications

Human-guided visual analytics for big data.

Human-guided visual analytics for big data.

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

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