Abstract—Map Services like Google Maps provide visual positioning information of streets, geographic places and business objects through the web. However, straightforward implementation of map applications by using these APIs often fails in achieving effective presentation and user interaction against a large number of geospatial entities; too many markers on the map may cause both visual overload and sluggish interaction with the map. In order to address these issues, this paper proposes grid-based filtering and density-based clustering schemes for efficient layout and handling of over thousands of geographical entities that are returned from Map Services. The experimental results with Google Maps show that our proposed schemes are quite effective at rendering and user interaction against a large number of map entities.

Index Terms—grid-based filtering, density-based clustering, geospatial data.

Cite: Gayratjon Kholmuradov Habibullayevich, Xian Chen, and Hyoseop Shin, "Efficient Filtering and Clustering Mechanism for Google Maps," Journal of Advanced  Management Science, Vol. 1, No. 1, pp. 107-111, March 2013. doi: 10.12720/joams.1.1.107-111