Implicit Profiling for Contextual Reasoning About Users ’ Spatial Preferences

Abstract. Information overload is a well documented problem in many application domains. A way of addressing this problem is by creating user profiles and by filtering out all irrelevant information while presenting the users only with information that matches their interests. Our focus is on the spatial domain. We follow an implicit profiling approach by logging users ’ mouse movements as they interact with spatial data. The logged information is analysed to support context reasoning about each user’s level of interest in the spatial features shown to him. These inferred interests are used to calculate an interest model for each individual user. Based on this interest model we can filter the information returned to the user, reducing information overload and tailoring the content to suit the users spatial preferences. In this paper we present our approach and discuss the implementation of the system we are developing for capturing users ’ spatial interactions and generating user profiles.

Semantically enriching VGI in support of implicit feedback analysis

Paper presented at the 10th International Symposyum on Web and Wireless Geographical Information Systems, 3-4 March 2011, Kyoto, JapanIn recent years, the proliferation of Volunteered Geographic Information (VGI) has enabled many Internet users to contribute to the construction of rich and increasingly complex spatial datasets. This growth of geo-referenced information and the often loose semantic structure of such data have resulted in spatial information overload. For this reason, a semantic gap has emerged between unstructured geo-spatial datasets and high-level ontological concepts. Filling this semantic gap can help reduce spatial information overload, therefore facilitating both user interactions and the analysis of such interaction. Implicit Feedback analysis is the focus of our work. In this paper we address this problem by proposing a system that executes spatial discovery queries. Our system combines a semantically-rich and spatially-poor ontology (DBpedia) with a spatially-rich and semantically-poor VGI dataset (OpenStreetMap). This technique differs from existing ones, such as the aggregated dataset LinkedGeoData, as it is focused on user interest analysis and takes map scale into account. System architecture, functionality and preliminary results gathered about the system performance are discussed.Science Foundation Ireland12M embargo - release after 15/02/2012 ti, ke - kpw4/11/1

Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering

Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human–computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks.Science Foundation Irelan

Dryad entry.zip

this zip file contains all data required to perfom both the population genetic analysis, outlier tests and seascape genetics on turbot as discribed in the article

Gene-associated markers provide tools for tackling illegal fishing and false eco-certification

Illegal, Unreported and Unregulated fishing has had a major role in the overexploitation of global fish populations. In response, international regulations have been imposed and many fisheries have been 'eco-certified' by consumer organizations, but methods for independent control of catch certificates and eco-labels are urgently needed. Here we show that, by using gene-associated single nucleotide polymorphisms, individual marine fish can be assigned back to population of origin with unprecedented high levels of precision. By applying high differentiation single nucleotide polymorphism assays, in four commercial marine fish, on a pan-European scale, we find 93-100% of individuals could be correctly assigned to origin in policy-driven case studies. We show how case-targeted single nucleotide polymorphism assays can be created and forensically validated, using a centrally maintained and publicly available database. Our results demonstrate how application of gene-associated markers will likely revolutionize origin assignment and become highly valuable tools for fighting illegal fishing and mislabelling worldwide