Linked Data and Visualization: Two Sides of the Transparency Coin

Transparency is an important element of smart cities, and ongoing work is exploring the use of available open data to maximize it. This position paper argues that Linked Data and visualization play similar roles, for different agents, in this context. Linked Data increases transparency for machines, while visualization increases transparency for humans. The work also proposes a quantitative approach to the evaluation of visualization insights which rests on two premises: (i) visualizations could be modelled as a set of statements made by authors at some point in time, and (ii) statements made by experts could be used as ground truth while evaluating how much insights are effectively conveyed by visualizations on the Web. Drawing on the linked data rating scheme of Tim Berners-Lee, the paper proposes a five-stars rating scheme for visualizations on the Web. The ideas suggested are relevant to the development of techniques to automatically assess the transparency level of existing visualizations on the Web.

[1]  Marius Rohde Johannessen,et al.  The Transparent Smart City , 2018 .

[2]  Aba-Sah Dadzie,et al.  Visualisation of Linked Data - Reprise , 2016, Semantic Web.

[3]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[4]  Smart Cities and Transparency Does Smartness Influence Transparency , 2016, HICSS 2016.

[5]  Greg Michener,et al.  Identifying Transparency , 2013, Inf. Polity.

[6]  A. Treloar,et al.  Open Data in Global Environmental Research: The Belmont Forum’s Open Data Survey , 2016, PloS one.

[7]  Sören Auer,et al.  A systematic review of open government data initiatives , 2015, Gov. Inf. Q..

[8]  James A. Hendler,et al.  Visualization tools for open government data , 2013, DG.O.

[9]  Krzysztof Janowicz,et al.  Linked Data - A Paradigm Shift for Geographic Information Science , 2014, GIScience.

[10]  Gennady L. Andrienko,et al.  Exploratory spatio-temporal visualization: an analytical review , 2003, J. Vis. Lang. Comput..

[11]  Jarke J. van Wijk,et al.  Evaluation: A Challenge for Visual Analytics , 2013, Computer.

[12]  John B. Horrigan and Lee Rainie Americans' Views on Open Government Data , 2015 .

[13]  Aileen R. Buckley,et al.  Geospatial big data and cartography: research challenges and opportunities for making maps that matter , 2017 .

[14]  Michael E. Milakovich,et al.  The Internet and Increased Citizen Participation in Government , 2010 .

[15]  Carlos Granell,et al.  Toolkits for Smarter Cities: A Brief Assessment , 2016, UCAmI.

[16]  Chris North,et al.  Toward measuring visualization insight , 2006, IEEE Computer Graphics and Applications.

[17]  Jens Lehmann,et al.  DBtrends: Publishing and Benchmarking RDF Ranking Functions , 2016, SumPre@ESWC.

[18]  William Ribarsky,et al.  Defining Insight for Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[19]  Ij Dowman Encoding and validating data from maps and images , 1999 .

[20]  Hui Chen,et al.  A literature survey on smart cities , 2015, Science China Information Sciences.

[21]  Yannis Charalabidis,et al.  Benefits, Adoption Barriers and Myths of Open Data and Open Government , 2012, Inf. Syst. Manag..

[22]  Jeffrey Heer,et al.  Narrative Visualization: Telling Stories with Data , 2010, IEEE Transactions on Visualization and Computer Graphics.

[23]  M. Sheelagh T. Carpendale,et al.  Evaluating Information Visualizations , 2008, Information Visualization.

[24]  R. Roth,et al.  Envisioning the future of cartographic research , 2017 .

[25]  Ellen P. Goodman,et al.  Algorithmic Transparency for the Smart City , 2017 .

[26]  Wang Tao,et al.  Interdisciplinary urban GIS for smart cities: advancements and opportunities , 2013, Geo spatial Inf. Sci..

[27]  Carlos Granell,et al.  Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience , 2016, ISPRS Int. J. Geo Inf..

[28]  Arzu Çöltekin,et al.  Persistent challenges in geovisualization – a community perspective , 2017 .

[29]  Enrico Motta,et al.  Smart Cities' Data: Challenges and Opportunities for Semantic Technologies , 2015, IEEE Internet Computing.

[30]  Simon Scheider,et al.  Encoding and Querying Historic Map Content , 2014, AGILE Conf..

[31]  J. J. van Wijk The value of visualization , 2005 .

[32]  Adegboyega Ojo,et al.  Exploring the Nature of the Smart Cities Research Landscape , 2016 .

[33]  Jens Lehmann,et al.  Increasing the financial transparency of European Commission project funding , 2014, Semantic Web.

[34]  Theresa-Marie Rhyne Defi ning Insight for Visual Analytics , 2009 .

[35]  Krzysztof Janowicz,et al.  Linked Data, Big Data, and the 4th Paradigm , 2013, Semantic Web.

[36]  Sandro Rautenberg,et al.  DBtrends: Exploring Query Logs for Ranking RDF Data , 2016, SEMANTiCS.

[37]  David Heald,et al.  Varieties of transparency , 2006 .

[38]  Arzu Çöltekin,et al.  User studies in cartography: opportunities for empirical research on interactive maps and visualizations , 2017 .

[39]  Jason Dykes,et al.  Editorial - GeoVisualization and the Digital City , 2010, Comput. Environ. Urban Syst..

[40]  Soonhee Kim,et al.  Citizen Participation and Transparency in Local Government: Do Participation Channels and Policy Making Phases Matter? , 2017, HICSS.

[41]  Juan Carlos De Martin,et al.  Removing Barriers to Transparency: A Case Study on the Use of Semantic Technologies to Tackle Procurement Data Inconsistency , 2017, ESWC.

[42]  Tobias Isenberg,et al.  A Systematic Review on the Practice of Evaluating Visualization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[43]  Chris Preist,et al.  Adopting Semantic Technologies for Effective Corporate Transparency , 2017, ESWC.