@article{Meier, author = {Meier, Sebastian}, title = {The Marker Cluster}, series = {International Journal of Agricultural and Environmental Information Systems}, volume = {7}, journal = {International Journal of Agricultural and Environmental Information Systems}, number = {1}, publisher = {IGI Global}, address = {Hershey}, issn = {1947-3206}, doi = {10.4018/IJAEIS.2016010102}, pages = {28 -- 43}, abstract = {The growing amount of gathered, stored and available data is creating a need for useful mass-data visualizations in many domains. The mapping of large spatial data sets is not only of interest for experts anymore, but, with regard to the latest advances in web cartography, also moves into the domain of public cartographic applications. One interactive web-based cartographic interface design pattern that helps with visualizing and interacting with large, high density data sets is the marker cluster; a functionality already in use in many web-based products and solutions. In this article, the author will present their ongoing research on the problem of "too many markers." They will present an empirical evaluation and comparison of marker cluster techniques and similar approaches, including heatmaps and tiled heatmaps. They conclude with a first concept for overcoming some of the obstacles that they were able to identify in their study and thereby introduce a new direction for further research.}, subject = {Big Data}, language = {en} } @inproceedings{Meier, author = {Meier, Sebastian}, title = {Enhancing Location Recommendation Through Proximity Indicators, Areal Descriptors, and Similarity Clusters}, series = {Progress in Location-Based Services 2016}, booktitle = {Progress in Location-Based Services 2016}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-47289-8}, issn = {1863-2351}, doi = {10.1007/978-3-319-47289-8_14}, pages = {273 -- 291}, abstract = {Location recommendation (LR) or rather location-based recommender systems (LBRS) are an integral part of modern location-based services (LBS). Most LR algorithms only focus on location-specific attributes when calculating recommendations, while completely ignoring the urban structure surrounding the locations. (In this paper we refer to a geographic coordinate (latitude and longitude) as position. Locations and places in contrast refer to physical entities e.g. a restaurant, a bus stop or a lake). This paper demonstrates how the urban structure can be modelled in LR calculations by using data from OpenStreetMap (OSM) and the location data itself. Based on these datasets, we present two approaches to extend the LR process by (1) including the urban structure in direct proximity of the location (Proximity Indicators and Areal Descriptors) and by (2) not only looking for individual locations but location clusters (Similarity Clusters). Thereby we acknowledge the complexity of a location, which can not be perceived as a detached entity. A location is part of a given urban structure and we need to include the parameters of this structure in our algorithms. A prototypical implementation compares locations from four major German cities: Berlin, Hamburg, Munich and Cologne and thereby highlights the applicability of the underlying data structures derived from OSM and the location data itself. We conclude by outlining the potential of the presented approaches in the context of LR as well as their relevancy for urban planning and neighboring disciplines.}, subject = {Empfehlungssystem}, language = {en} }