Abstract
Recent years have witnessed rapid advances in online map-based mashups with Application Programming Interfaces (APIs) and web services. Map-based mashups often display different kinds of information (e.g., POIs, represented as icons) on base maps, such as Google Maps and Bing Maps. The visualization of large number of icons in a map on web browsers or mobile devices often results in the icon cluttering problem with icons touching and overlapping each other. This problem decreases map legibility, and thus prevents users from effectively processing the information presented in the map. It also leads to a dramatic degradation of performance, and a high transmission load. All these problems will greatly decrease the usability of a mashup application.
This paper surveys and assesses approaches from different disciplines (i.e., computer science and cartography) for avoiding icon cluttering in online map-based mashups. We focus on two issues: filtering of irrelevant POIs, and icon placement and aggregation. Different techniques from information filtering research are analyzed and compared for reducing the number of icons to be displayed in a map. For the latter issue, approaches of aggregating and placing icons from map generalization research are discussed and assessed for their applicability in online mashups. Some related APIs and typical mashup examples are also discussed and compared. This paper concludes that in order to provide more cartographically pleasing maps in mashups, techniques from computer science and cartography should be seamlessly integrated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
ACME (2011) JavaScript utilities. http://www.acme.com/javascript/#Clusterer. Accessed June 2011
Adomavicius G, Tuzhilin A (2005) Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749
Burigat S, Chittaro L (2008) Decluttering of icons based on aggregation in mobile maps. In: Meng L, Zipf A, Winter S (eds) Map-based mobile services – design interaction and usability. Springer, Berlin, pp 13–32
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap 12(4):331–370
Burke R (2007) Hybrid web recommender systems. In: The adaptive web. Springer, Berlin, pp 377–408
Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, pp 107–144
Felfernig A, Friedrich G, Jannach D, Zanker M (2011) Developing constraint-based recommenders. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, pp 187–215
Foerster T, Stoter JE (2008) Generalisation operators for practice: a survey at national mapping agencies. In: Proceedings of the 11th ICA workshop on generalisation and multiple representation, Montpellier, 20–21 June
GMaps Utility Library (2011) http://code.google.com/apis/maps/documentation/javascript/v2/overlays.html#Marker_Manager. Accessed June 2011
Haklay M, Singleton A, Parker C (2008) Web mapping 2.0: the neogeography of the geoweb. Geogr Compass 2(6):2011–2039
Hanani U, Shapira B, Shoval P (2001) Information filtering: overview of issues, research and systems. User Model User-Adap 11(3):203–259
Harrie L (1999) The constraint method for solving spatial conflicts in cartographic generalisation. Cartogr Geogr Inf 26(1):55–69
Harrie L, Stigmar H, Koivula T, Lehto L (2004) An algorithm for icon placement on a real-time map. In: Fisher P (ed) Developments in spatial data handling, Proceedings of the 11th international symposium on spatial data handling. Springer, Leicester, pp 493–507
Kovanen J, Sarjakoski LT (2010) Displacement and grouping of points of interest for multi-scaled mobile maps. In: Proceedings of LBS 2010, Guangzhou, 20–22 Sept 2010
Lonergan M, Jones CB (2001) An iterative displacement method for conflict resolution in map generalization. Algorithmica 30:287–301
Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, pp 73–105
Mackaness WA, Fisher PF (1987) Automatic recognition and resolution of spatial conflicts in cartographic symbolisation. In: Proceedings of AutoCarto 8, 29.03-03.04, Baltimore, pp 709–718
Mackaness WA, Purves RS (2001) Automated displacement for large numbers of discrete map objects. Algorithmica 30:302–311
MarkerManager v3 (2011) http://google-maps-utility-library-v3.googlecode.com/svn/tags/markermanager/1.0/docs/reference.html. Accessed June 2011
Marks J, Shieber S (1991) The computational complexity of cartographic label placement. Technical Report TR-05-91, Center for Research in Computing Technology, Harvard University
Morandell C (2010) Möglichkeiten der nutzerspezifischen Gestaltung von Location Based Services mit Daten aus Social Networks. Master thesis of Vienna University of Technology
Pearman M (2011) Google Maps API Projects. http://googlemapsapi.martinpearman.co.uk/readarticle.php?article_id = 4. Accessed June 2011
ProgrammableWeb (2011) http://www.programmableweb.com/mashups#topt-2. Accessed June 2011
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston, pp 1–35
Ruas A (1998) A method for building displacement in automated map generalisation. Int J Geogr Inf Sci 12(8):789–803
Sinha R, Swearingen K (2001) Comparing recommendations made by online systems and friends. In: DELOS workshop: personalisation and recommender systems in digital libraries
Svennerberg G (2009) Handling large amounts of markers in Google maps. http://www.svennerberg.com/2009/01/handling-large-amounts-of-markers-in-google-maps/. Accessed June 2011
Wang J, Vries A, Reinders M (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th ACM SIGIR conference on information retrieval. Seattle, Washington, USA, pp 501–508
Wikipedia (2011a) http://en.wikipedia.org/wiki/Web_2.0. Accessed June 2011
Wikipedia (2011b) http://en.wikipedia.org/wiki/Mashup_%28web_application_hybrid%29. Accessed June 2011
Wikipedia (2011c) http://en.wikipedia.org/wiki/Information_filtering_system. Accessed June 2011
Wikipedia (2011d) http://en.wikipedia.org/wiki/Information_overload. Accessed June 2011
Wu X (2011) MarkerClusterer: a solution to the too many markers problem. http://googlegeodevelopers.blogspot.com/2009/04/markerclusterer-solution-to-too-many.html. Accessed June 2011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Velag Berlin Heidelberg
About this chapter
Cite this chapter
Huang, H., Gartner, G. (2012). A Technical Survey on Decluttering of Icons in Online Map-Based Mashups. In: Peterson, M. (eds) Online Maps with APIs and WebServices. Lecture Notes in Geoinformation and Cartography(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27485-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-27485-5_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27484-8
Online ISBN: 978-3-642-27485-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)