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Metaphor Design of Dockless Bike-sharing Based on Spatio-temporal Geographic Data

Published: 20 October 2023 Publication History
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  • Abstract

    Dockless Bike-sharing systems can be treated as a typical paradigm of the sharing economy. Due to the indiscriminate use and placement of huge bikes, their scheduling rules are complicated, which poses a challenge to the macro-control of the companies To enhance the information dimension of bike Origin-Destination (OD) data, an algorithm based on grid features is proposed to reconstruct travel trajectories of OD data. This paper starts by analyzing and designing metaphors to describe the scheduling rules and trip characteristics of Dockless Bike-Sharing. The evaluation shows that the proposed metaphors are user-friendly to novice users. We argue that metaphors provide an effective way for users to understand abstract ideas in a visual design with a large dataset.

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    1. Metaphor Design of Dockless Bike-sharing Based on Spatio-temporal Geographic Data

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      VINCI '23: Proceedings of the 16th International Symposium on Visual Information Communication and Interaction
      September 2023
      308 pages
      ISBN:9798400707513
      DOI:10.1145/3615522
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      New York, NY, United States

      Publication History

      Published: 20 October 2023

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      Author Tags

      1. dockless bike-sharing
      2. metaphor
      3. origin-destination data
      4. visualization

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      • Short-paper
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      • National Social Science Foundation

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      VINCI 2023

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      Overall Acceptance Rate 71 of 193 submissions, 37%

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