The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

/pdf/mining-interesting-locations-and-travel-sequences-from-gps-33g2tbk5p5.pdf

Mining interesting locations and travel sequences from GPS trajectories

With the increasing popularity of location-based services, such as tour guide and location-based social network, we now have accumulated many location data on the Web. In this paper, we show that, by using the location data based on GPS and users' comments at various locations, we can discover interesting locations and possible activities that can be performed there for recommendations. Our research is highlighted in the following location-related queries in our daily life: 1) if we want to do something such as sightseeing or food-hunting in a large city such as Beijing, where should we go? 2) If we have already visited some places such as the Bird's Nest building in Beijing's Olympic park, what else can we do there? By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc. For the second question, if the user visits Bird's Nest, we can recommend her to not only do sightseeing but also to experience its outdoor exercise facilities or try some nice food nearby. To achieve this goal, we first model the users' location and activity histories that we take as input. We then mine knowledge, such as the location features and activity-activity correlations from the geographical databases and the Web, to gather additional inputs. Finally, we apply a collective matrix factorization method to mine interesting locations and activities, and use them to recommend to the users where they can visit if they want to perform some specific activities and what they can do if they visit some specific places. We empirically evaluated our system using a large GPS dataset collected by 162 users over a period of 2.5 years in the real-world. We extensively evaluated our system and showed that our system can outperform several state-of-the-art baselines.

/pdf/collaborative-location-and-activity-recommendations-with-gps-32xzsk6czm.pdf

Collaborative location and activity recommendations with GPS history data

The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in a mobile context. This increase in usefulness has come at the expense of the usability of these devices in some contexts. We conducted a small review of mobile usability models and found that usability is usually measured in terms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend to be overlooked in the usability models that are most prominent despite their likely impact on the success or failure of an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile Application Development) usability model which was designed to address the limitations of existing usability models when applied to mobile devices. PACMAD brings together significant attributes from different usability models in order to create a more comprehensive model. None of the attributes that it includes are new, but the existing prominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation. We performed a literature search to compile a collection of studies that evaluate mobile applications and then evaluated the studies using our model.

/pdf/usability-of-mobile-applications-literature-review-and-2sgkvggy18.pdf

Usability of mobile applications: literature review and rationale for a new usability model

L'article, publie dans Science, sur une des premieres utilisations analytiques de Google Books, fondee sur les n-grammes (Google Ngrams) We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of "culturomics", focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can ...

Quantitative Analysis of Culture Using Millions of Digitized Books

Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-based segmentation method, an inference model and a post-processing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.

/pdf/learning-transportation-mode-from-raw-gps-data-for-2r5pfd0d25.pdf

Learning transportation mode from raw gps data for geographic applications on the web

Annotations allow end users to augment digital items with information, which can then be exploited for search and retrieval. We are currently extending Europeana, a platform which links to millions of digital items in European institutions, with an annotation mechanism that exposes annotations as linked data and enriches newly created annotations with links to contextually relevant resources on the Web. In two demos we showcase how we integrated that kind of content augmentation into two clients that allow users to annotate videos and historic maps.

/pdf/augmenting-europeana-content-with-linked-data-resources-4f7a4rj0pg.pdf

Augmenting Europeana content with linked data resources

The new, larger display screens can improve program comprehension—if the added space is used for mome effective presentation, not just more code or larger type

/pdf/display-strategies-for-program-browsing-concepts-and-4m3qfykmxo.pdf

Display Strategies for Program Browsing: Concepts and Experiment

Mobile phones are starting to become the major platform for interaction with spatial information. Recent research has yielded promising applications and approaches for exploring, accessing and augmenting information related to the user's immediate surroundings. The CHI workshop "Mobile Spatial Interaction" (MSI) aims at gathering researchers working on this emerging and multifaceted, but quickly evolving topic. A forum for open dialogue is needed to enable researchers to obtain a picture of the facets and the benefits of mobile spatial interaction as well as its challenges. Potential ways to combine the various approaches will be examined and discussed.

Mobile spatial interaction

Summary: Recogito is an open source tool for the semi-automatic annotation of place references in maps and texts. It was developed as part of the Pelagios 3 research project, which aims to build up a comprehensive directory of places referred to in early maps and geographic writing predating the year 1492. Pelagios 3 focuses specifically on sources from the Classical Latin, Greek and Byzantine periods; on Mappae Mundi and narrative texts from the European Medieval period; on Late Medieval Portolans; and on maps and texts from the early Islamic and early Chinese traditions. Since the start of the project in September 2013, the team has harvested more than 120,000 toponyms, manually verifying almost 60,000 of them. Furthermore, the team held two public annotation workshops supported through the Open Humanities Awards 2014. In these workshops, a mixed audience of students and academics of different backgrounds used Recogito to add several thousand contributions on each workshop day. A number of benefits arise out of this work: on the one hand, the digital identification of places – and the names used for them – makes the documents' contents amenable to information retrieval technology, i.e. documents become more easily search- and discoverable to users than through conventional metadata-based search alone. On the other hand, the documents are opened up to new forms of re-use. For example, it becomes possible to “map” and compare the narrative of texts, and the contents of maps with modern day tools like Web maps and GIS; or to analyze and contrast documents’ geographic properties, toponymy and spatial relationships. Seen in a wider context, we argue that initiatives such as ours contribute to the growing ecosystem of the “Graph of Humanities Data” that is gathering pace in the Digital Humanities (linking data about people, places, events, canonical references, etc.), which has the potential to open up new avenues for computational and quantitative research in a variety of fields including History, Geography, Archaeology, Classics, Genealogy and Modern Languages.

/pdf/linking-early-geospatial-documents-one-place-at-a-time-1k8dm92ksr.pdf

Linking early geospatial documents, one place at a time: annotation of geographic documents with Recogito

The growing trend towards mobile phones with integrated GPS clearly suggests that navigation and location-based services (LBS) will be key applications for future mobile devices. New hardware features that are currently finding their way into state-of-the-art phones-such as digital compasses and tilt sensors-promise to drive the adoption of mobile geospatial services, and to change the way people navigate, explore and interact with their physical environment: location-based applications that exploit attitude information to realise orientation-aware interaction have been discussed in research for several years. Yet, few actual results on the achievable real-world performance of such systems exist in literature. In this article, we report on a series of function trials carried out with a prototype Geo-Wand-a portable system that allows users to access geo-referenced information by physically pointing towards objects in the real world. The application was realised with a mass market mobile phone connected to a Bluetooth GPS and a custom-built orientation sensor module. We present test results for multiple types of urban terrain and discuss the possibilities and limitations of this next-generation mobile LBS technology.

GeoPointing: evaluating the performance of orientation-aware location-based interaction under real-world conditions

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