A conceptual framework for developing dashboards for big mobility data

ABSTRACT

Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets.

Key policy highlights

• Defines essential components of big mobility dashboards for stakeholders to understand key considerations and data management/pre-processing needs

• Clarifies the differences between dashboards and visual analytics applications

• Provides guidance for gathering information from end-users to ensure displays are fit-for-purpose

• Illustrates the application of the conceptual framework for dashboard design by discussing several examples of mobility data dashboards

KEYWORDS:

• Dashboard
• big mobility data
• visualization
• conceptual framework
• design guidelines

Acknowledgments

The authors also appreciate the comments of four anonymous reviewers which helped improve the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data used for Dashboard 1, 2, and 3 are not available due to both legal and commercial restrictions. Dashboard 4, 5, 6 are from third-party authors who are not affiliated with this manuscript. Data availability of Dashboard 4, 5, and 6 is subject to the creators of the dashboards.

Additional information

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement [No 780754].