scholarly journals From Multiple Linked Views to Multiple Linked Analyses: The Meme Media Digital Dashboard

2014 ◽  
Author(s):  
Jonas Sjobergh ◽  
Yuzuru Tanaka
Keyword(s):  
Linked Views

Integrated Time and Distance Line Cartogram: a Schematic Approach to Understand the Narrative of Movements

2014 ◽  
pp. 7-16 ◽  
Author(s):  
Menno-Jan Kraak ◽  
Barend Köbben ◽  
Yanlin Tong
Keyword(s):  
Visual Representation ◽  
Preliminary Evaluation ◽  
Time And Space ◽  
Specific Location ◽  
Web Environment ◽  
Unique Character ◽  
Movement Data ◽  
Linked Views

To understand the nature of movement data, we introduce an alternative visual representation looking at paths from different perspectives. The movements and their stops are schematized into lines. These are distorted based on time or distance by applying line cartogram principles to answer specific location- or time-based questions. A prototype consisting of multiple linked views, including the line cartograms and a map, is implemented in a web environment using D3.js. It allows one to explore the nature of single or multiple movements. The option to compare multiple movements gives the solution its unique character. A preliminary evaluation of the product shows it is able the answer questions related to time and space accordingly. 

Linked Views

1992 ◽  
pp. 39-42
Author(s):  
Axel Benner
Keyword(s):  
Linked Views

An integrative framework for data-driven investigation of environmental systems

2020 ◽  
Author(s):  
Daniel Eggert ◽  
Doris Dransch
Keyword(s):  
Heterogeneous Data ◽  
Data Driven ◽  
Software Framework ◽  
Research Approach ◽  
Digital Workflow ◽  
Environmental Systems ◽  
Data Products ◽  
Analytical Tools ◽  
Tools And Methods ◽  
Linked Views

<p>Environmental scientists aim at understanding not only single components but systems, one example is the flood system; scientists investigate the conditions, drivers and effects of flood events and the relations between them. Investigating environmental systems with a data-driven research approach requires linking a variety of data, analytical methods, and derived results.</p><p><br>Several obstacles exist in the recent scientific work environment that hinder scientists to easily create these links. They are distributed and heterogeneous data sets, separated analytical tools, discontinuous analytical workflows, as well as isolated views to data and data products. We address these obstacles with the exception of distributed and heterogeneous data since this is part of other ongoing initiatives.</p><p><br>Our goal is to develop a framework supporting the data-driven investigation of environmental systems. First we integrate separated analytical tools and methods by the means of a component-based software framework. Furthermore we allow for seamless and continuous  analytical workflows by applying the concept of digital workflows, which also demands the aforementioned integration of separated tools and methods. Finally we provide integrated views of data and data products by interactive visual interfaces with multiple linked views. The combination of these three concepts from computer science allows us to create a digital research environment that enable scientists to create the initially mentioned links in a flexible way. We developed a generic concept for our approach, implemented a corresponding framework and finally applied both to realize a “Flood Event Explorer” prototype supporting the comprehensive investigation of a flood system.</p><p><br>In order to implement a digital workflow our approach intends to precisely define the workflow’s requirements. We mostly do this by conducting informal interviews with the domain scientists. The defined requirements also include the needed analytical tools and methods, as well as the utilized data and data products. For technically integrating the needed tools and methods our created software framework provides a modularization approach based on a messaging system. This allows us to create custom modules or wrap existing implementations and tools. The messaging system (e.g. pulsar) then connects these individual modules. This enables us to combine multiple methods and tools into a seamless digital workflow. The described approach of course demands the proper definition of interfaces to modules and data sources. Finally our software framework provides multiple generic visual front-end components (e.g. tables, maps and charts) to create interactive linked views supporting the visual analysis of the workflow’s data.</p>

scholarly journals Modelling Reachability in Transport Networks: Using Alternative Visual Representations in Interactive Linked‐Views to Gain Valuable Insights

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rehmat Ullah ◽  
Laiq Hasan ◽  
Farman Ullah ◽  
Ajmal Khan ◽  
You-Ze Cho
Keyword(s):  
Travel Time ◽  
Distance Measure ◽  
Physical Distance ◽  
Transport Network ◽  
Travel Times ◽  
Transport Networks ◽  
Railroad Network ◽  
Graphic Representations ◽  
Two Cities ◽  
Linked Views

Most people use maps for navigation. Geographic maps visually represent physical distance between locations. These maps sometimes provide a false impression of travel times. Two cities geographically close to each other might be “far apart” in terms of travel time because of slower connections, whereas two cities geographically distant might be “nearby” in terms of travel time because of faster connections. Under such circumstances, visualizing a transport network using time as a distance measure can make the transport network more understandable. This study integrates several (carto)graphic representations—a time line, a distance line, a time prism, a time cartogram, and a geographic map—in an interactive linked-views environment to model reachability in transport networks. A prototype is implemented in a web environment using D3.js. The implementation can be applied to any transport network. In this research, the approach is illustrated with railroad network data for the Dutch province of Overijssel. The solution provides an alternative and insightful perspective for analyzing the data. In addition to complementing a wide variety of methods to visualizing travel times, the approach could be applied in areas such as spatial analysis and transport planning.

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