Micro diagrams: visualization of categorical point data from location-based social media

ABSTRACT Location-based social media data from different platforms such as Twitter and Flickr increasingly serve with their point-geocoded content as data sources for a variety of applications. The standard visualization method uses a derivation of point maps, which works well with a limited amount of data, but it suffers from weaknesses related to cluttering and overlapping, especially for sets of categories. We developed a new visualization method for categorical point data, called “Micro Diagrams”, which uses small diagrams to show the percentages of categories and the spatial distribution. The processing steps to derive the micro diagrams start with aggregating the points in a regular grid structure, which is followed by the selection of the diagram type that represents the numerical proportions and the application of a size scaling function to show the amounts of data. Various parameterization options are discussed and the influence of the color selection is analyzed. Finally, a case study combined with a user test presents the strengths and limits of the micro diagram method.

[1]  M. J. Kraak,et al.  Cartography: Visualization of Geospatial Data , 1996 .

[2]  Alessandro Vespignani,et al.  The Twitter of Babel: Mapping World Languages through Microblogging Platforms , 2012, PloS one.

[3]  J. Gutiérrez,et al.  Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS , 2015 .

[4]  A. Collinson,et al.  Designing Better Maps: A Guide for GIS Users , 2016 .

[5]  A-Xing Zhu,et al.  A discrete global grid system for earth system modeling , 2018, Int. J. Geogr. Inf. Sci..

[6]  Graham McNeill,et al.  Generating Tile Maps , 2017, Comput. Graph. Forum.

[7]  Kurt Hornik,et al.  Escaping RGBland: Selecting colors for statistical graphics , 2009, Comput. Stat. Data Anal..

[8]  Jacques Bertin,et al.  Graphische Semiologie: Diagramme, Netze, Karten , 2010 .

[9]  Mohamed F. Mokbel,et al.  VisCAT: spatio-temporal visualization and aggregation of categorical attributes in twitter data , 2014, SIGSPATIAL/GIS.

[10]  Susanne Bleisch,et al.  Exploring multivariate representations of indices along linear geographic features , 2018 .

[11]  Kurt Hornik,et al.  colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes , 2019, J. Stat. Softw..

[12]  Lorenz Hurni,et al.  Designing a Rule-based Wizard for Visualizing Statistical Data on Thematic Maps , 2017 .

[13]  Shaowen Wang,et al.  Mapping the global Twitter heartbeat: The geography of Twitter , 2013, First Monday.

[14]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[15]  Andrew Vande Moere,et al.  BinSq: visualizing geographic dot density patterns with gridded maps , 2017 .

[16]  Florian Heimerl,et al.  Visual Designs for Binned Aggregation of Multi-Class Scatterplots , 2018, ArXiv.

[17]  Yuanzhen Li,et al.  Measuring visual clutter. , 2007, Journal of vision.

[18]  Si’en Chen,et al.  Analytics: The real-world use of big data in financial services studying with judge system events , 2016, Journal of Shanghai Jiaotong University (Science).

[19]  Alan J. Dix,et al.  A Taxonomy of Clutter Reduction for Information Visualisation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[20]  Susanne Heuser,et al.  Location Based Social Networks – Definition, Current State of the Art and Research Agenda , 2013, Trans. GIS.

[21]  Daniel A. Keim,et al.  The Gridfit algorithm: an efficient and effective approach to visualizing large amounts of spatial data , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[22]  Liqiu Meng,et al.  Understanding Taxi Driving Behaviors from Movement Data , 2015, AGILE Conf..

[23]  Jarke J. van Wijk,et al.  Interactive visualization of multivariate trajectory data with density maps , 2011, 2011 IEEE Pacific Visualization Symposium.

[24]  James R. Miller,et al.  Attribute Blocks: Visualizing Multiple Continuously Defined Attributes , 2007, IEEE Computer Graphics and Applications.

[25]  Xiaochong Tong,et al.  A spatial indexing method for the hexagon discrete global grid system , 2010, 2010 18th International Conference on Geoinformatics.

[26]  S. Lewandowsky,et al.  Displaying proportions and percentages , 1991 .

[27]  Terry A. Slocum Thematic Cartography and Visualization , 1998 .

[28]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

[29]  Jarke J. van Wijk,et al.  Non-overlapping Aggregated Multivariate Glyphs for Moving Objects , 2014, 2014 IEEE Pacific Visualization Symposium.

[30]  M. Tsou,et al.  Research challenges and opportunities in mapping social media and Big Data , 2015 .

[31]  Erik Arnberger Thematische Kartographie : mit e. Kurzeinf. über Automation in d. themat. Kartographie , 1977 .

[32]  Mengjie Zhou,et al.  Point grid map: a new type of thematic map for statistical data associated with geographic points , 2017 .

[33]  Pierre Dragicevic,et al.  A Declarative Rendering Model for Multiclass Density Maps , 2019, IEEE Transactions on Visualization and Computer Graphics.

[34]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.