Alternative options of using processing knowledge to populate ontologies for the recognition of urban concepts

In this paper, we present an ontology-driven approach for cartographic pattern recognition in support of map generalisation. Spatial patterns are formalised by means of ontologies which are then used to deductively trigger appropriate low level pattern recognition techniques. Modelling ontologies suited for spatial pattern recognition is discussed by example of an ontology of terraced houses. The paper subsequently focuses on approaches for inferring the instances of higher level concepts. Three different approaches are employed to detect terraced houses in Ordnance Survey MasterMap® vector data: Weighted summation; Joint Bayes classifier; and Support Vector Machines. An evaluation by comparison to a manual classification reveals that weighted summation and the Joint Bayes classifier both have satisfactory prediction accuracy, but the Joint Bayes classifier has advantages when considering the calibration effort involved. In conclusion, we claim that the ontology-driven approach better captures the complex structure of spatial patterns and provides enhanced transparency and flexibility of the pattern recognition process in comparison to conventional, purely geometric and/or statistical techniques

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Geographical places as a personalisation element: extracting profiles from human activities and services of visited places in mobility logs

Collecting personal mobility traces of individuals is currently applicable on a large scale due to the popularity of position-aware mobile phones. Statistical analysis of GPS data streams, collected with a mobile phone, can reveal several interesting measures such as the most frequently visited geographical places by some individual. Applying probabilistic models to such data sets can predict the next place to visit, and when. Several practical applications can utilise the results of such analysis. Current state of the art, however, is limited in terms of the qualitative analysis of personal mobility logs. Without explicit user-interactions, not much semantics can be inferred from a GPS log. This work proposes the utilisation of the common human activities and services provided at certain place types to extract semantically rich profiles from personal mobility logs. The resulting profiles include spatial, temporal and generic thematic description of a user. The work introduces several pre-processing methods for GPS data streams, collected with personal mobile devices, which improved the quality of the place extraction process from GPS logs. The thesis also introduces a method for extracting place semantics from multiple data sources. A textual corpus of functional descriptions of human activities and services associated with certain geographic place types is analysed to identify the frequent linguistic patterns used to describe such terms. The patterns found are then matched against multiple textual data sources of place semantics, to extract such terms, for a collection of place types. The results were evaluated in comparison to an equivalent expert ontology, as well as to semantics collected from the general public. Finally, the work proposes a model for the resulting profiles, the necessary algorithms to build and utilise such profiles, along with an encoding mark-up language. A simulated mobile application was developed to show the usability and for evaluation of the resulting profiles

Geografische Empfehlungssysteme

Mobile Geräte werden immer häufiger mit Sensoren zur Bestimmung der eigenen Position ausgestattet, zum Beispiel mit GPS. Mit Hilfe der Ortsinformationen dieser Sensoren können beispielsweise moderne Bildmanagementanwendungen digitale Fotos automatisch nach geografischen Regionen gruppieren oder passende Schlagworte generieren. Dies führt unter anderem zu einer besseren Suchbarkeit dieser digitalen Daten. Grundsätzlich geben Ortsinformationen in digitalen Fotos nicht nur Hinweise auf das Foto selbst, sondern machen auch sichtbar, welche geografischen Entscheidungen der Fotograf bei deren Erstellung getroffen hat. Diese Arbeit nutzt diese Entscheidungen für die Berechnung von weiteren Empfehlungen für den Nutzer, beispielsweise einer Bildmanagementanwendung. Ein konkreter Anwendungsfall lautet folgendermaßen: Einem Nutzer sollen für eine frei wählbare geographische Region (z.B. einer Stadt), mehrere Bilder empfohlen werden, die zum einen typisch für diese Region sind, zum anderen aber auch für ihn persönlich interessant sein könnten. Um diese geografischen Mehr-Objekt-Empfehlungen zu berechnen, wurde ein neuartiger Algorithmus entwickelt, der zunächst die Ortsinformationen aller Nutzer zu einem geografischen Modell bündelt. Auf Grundlage dieser prototypischen Konzeptualisierung von einzelnen Regionen, können dann typische Bilder empfohlen werden. Weiterhin werden diese geografischen Modelle in einem zweiten Schritt für die zusätzliche Gewichtung der einzelnen geografischen Entscheidungen der Nutzer verwendet, um über den Ansatz eines kollaborativen Filters zu einer persönlichen Empfehlung zu gelangen. Dazu wurden mehrere Verfahren entwickelt und miteinander verglichen. Diese Arbeit ist im Rahmen des europäischen Projektes Tripod entstanden, für das der entwickelte geografische Empfehlungsalgorithmus als Softwaremodul prototypisch implementiert wurde. Damit wurden die Empfehlungen mit Hilfe von georeferenzierten Bildern evaluiert, die auf den Online-Galerien Panoramio.com und Flickr.de veröffentlicht wurden. Durch die Auswertung der geografischen Informationen und der daraus berechneten Ortsmodelle, ließen sich deutlich präzisere Empfehlungen vorschlagen, als mit anderen bekannten Empfehlungsverfahren