Geographical inquiry involves collecting, using and making sense of the data to investigate some geographical phenomena. With the increasing number of mobile devices equipped with Internet access capabilities, there is a wide scope for using it in field inquiry where learning can take place in the form of social interactions between team members while performing a common task. The authors examine the use of MobiTOP (Mobile Tagging of Objects and People), a geospatial digital library system which allows users to contribute and share multimedia annotations via mobile devices, within a geographical field inquiry. A key feature of MobiTOP that is well suited for collaborative learning is that annotations are hierarchical, allowing annotations to be annotated by other users to an arbitrary depth. A group of student and teachers involved in an inquiry-based learning activity in geography was instructed to identify rock types and associated landforms by collaborating with each other using the MobiTOP system. A method combining several data collection methods was used. Four main findings on the affordances of the mobile phone, the need for specialized training in using the application with the mobile phone, design considerations of the application and the authentic context of learning with fieldwork are reported. A discussion on the implication of these findings and how mobile technology may be used for geographical field learning are also included in this paper.

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The development of numerous information sharing platforms have led to the emergence of multimedia user-generated content. With the prevalence of networking mobile devices and Global Positioning System (GPS) functionality, these contents could also be tagged with their location (geo-tagged) and visualised on a map through a geospatial information system. This in turn raises new challenges to manage the information retrieval process due to the potentially large amounts of data presented on the map. In this paper, we present a spatial clustering approach to enhance the searching feature of MobiTOP, a geospatial annotation system. The technique, which is a modification of DBSCAN (Density based spatial clustering applications with noise) coupled with our ranking schemes, is compared against other techniques. The evaluation results suggest the viability of our approach, and implications are also discussed.