Recognizing the activities being performed on a map is crucial for adaptive map design based on user context. Despite eye tracking (ET) demonstrating potential in recognizing map activities and electroencephalography (EEG) measuring map users’ cognitive load, no studies have yet combined ET and EEG for recognition of the user’s activity on maps. Our study collected participants’ ET and EEG data during four types of map activities. After feature extraction and selection, we trained LightGBM (light Gradient-Boosting Machine) to classify these activities, and achieved 88.0% accuracy when combining ET and EEG features in the entire map usage trial, which is higher than using ET (85.9%) or EEG (53.9%) alone. Acceptable recognition accuracy could also be achieved with the early time windows (73.1% when using the first 3 seconds). Saccade features of ET were the most important for differentiating map activities, indicating selective map content for different tasks. Our findings demonstrate the feasibility and advantages of combining ET and EEG for activity recognition in map use. The results not only improve our understanding of visual patterns and cognitive processes in map use, but also enable the design of adaptive maps that can automatically adapt to the activities a map user is performing.

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.

Big GPS trajectory datasets can have redundant spatio-temporal information for applications, which requires simplification as a key preprocessing for modeling. Many existing simplification methods focus on the geometric information from a trajectory per se. Conversely, methods considering geographic context often fail to provide spatially adaptive simplification, or require complex parameter settings to achieve this task. This study proposes a novel two-stage adaptive trajectory simplification method embedding spatial indexing, enrichment, and aggregation in an integrated process. The first stage employs a quadtree for the subdivision depending on the density of geographic context features (i.e. POIs), leading to a variable-resolution representation of the area. The second stage aggregates trajectory waypoints locating in the same quadtree leaf node into a representative point, making the aggregation adapting to the spatial layout of the geographic feature in the first stage. Evaluation with a real-world vehicle trajectory dataset shows that the proposed approach can automatically simplify trajectory segments at variable compression ratios with greater simplification in areas with sparse context features (e.g. rural) and less simplification in areas with dense context features (e.g. urban). More importantly, the method can still preserve inter-trajectory distances between original trajectories and simplified ones, while significantly reducing the computing time.

Meng Huangab , Zhixiang Fangac*, Robert Weibelb , Tao Zhangd & Haosheng Huange* a State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, PR Chinab Department of Geography, University of Zurich, Zurich, Switzerlandc Collaborative Innovation Center of Geospatial Technology, Wuhan, PR Chinad China Mobile Group Hubei Company Limited, Wuhan, Chinae Department of Geography, Ghent University, Ghent, BelgiumCONTACT Zhixiang Fang [email protected] Huang [email protected] billboards, as a new form of outdoor advertising, has gained popularity in recent years per its revolutionized way to control when and where the specific ads appear. However, this development also demands more complicated optimization for strategic deployments: the advertisers have to not only decide on a set of locations to display their ads, but also when to display them. The existing static optimization approaches become insufficient for this dynamic scenario to match advertisement and intended audience. Therefore, this research proposes three models in a workflow to mine mobile phone data and points of interest (POIs) data and to meet advertising needs in various situations. The three optimization models include a dynamic audience model to maximize the coverage of the target users, a dynamic environment model to maximize the coverage of the target environment, and a dynamic integrated model to maximize the coverage of both target audience and environment. A case study using shopping ads in Wuxue, China tests the three optimalization models. The results show that the proposed models are effective for providing an optimal solution for digital billboard configuration with a greater coverage of the target audience and environment compared to the state-of-the-art static models.

Eye movement data convey a wealth of information that can be used to probe human behaviour and cognitive processes. To date, eye tracking studies have mainly focused on laboratory-based evaluations of cartographic interfaces; in contrast, little attention has been paid to eye movement data mining for real-world applications. In this study, we propose using machine-learning methods to infer user tasks from eye movement data in real-world pedestrian navigation scenarios. We conducted a real-world pedestrian navigation experiment in which we recorded eye movement data from 38 participants. We trained and cross-validated a random forest classifier for classifying five common navigation tasks using five types of eye movement features. The results show that the classifier can achieve an overall accuracy of 67%. We found that statistical eye movement features and saccade encoding features are more useful than the other investigated types of features for distinguishing user tasks. We also identified that the choice of classifier, the time window size and the eye movement features considered are all important factors that influence task inference performance. Results of the research open doors to some potential real-world innovative applications, such as navigation systems that can provide task-related information depending on the task a user is performing.

The last decade has seen an exponential growth in location-based big data research. Indeed, the availability of fine-grained location-based big data has created unprecedented opportunities for rese...

Humans perceive and evaluate environments affectively. Some places are experienced as unsafe, while some others as attractive and interesting. These affective responses to environments influence people’s daily behavior and decision-making in space, e.g., choosing which route to take, or which place to visit. In this article, we report on a methodology of using people’s affective responses to environments for enhancing computer-based route planning. More specifically, we explore a crowdsourcing approach to model and collect people’s affective responses to environments; an Affect-Space-Model and a mobile application are developed to facilitate this crowdsourcing approach; a routing algorithm (named AffectRoute) is then proposed to aggregate and integrate the collected data for automatic route planning. Evaluation with human participants shows that the routes generated by considering people’s affective responses to environments are significantly preferred over the conventional shortest ones, which are employed in car navigation systems and many online route planners. In conclusion, considering people’s affective responses to environments contributes to the improvement of automatic route planning. The proposed method can be integrated into existing route-planning services (e.g., location-based services) to provide users with more satisfying routing results.

XML/GML/SVG-based approaches are promising for building Web-based geographic information systems (WebGIS). However, current XML/GML/SVG-based WebGISs are lacking in spatial analysis. Some of them are designed for web mapping only. Others adopt a server-side solution for spatial analysis, which suffers from the ‘bottleneck’ problem and results in a high network transmission load. Load-balancing spatial analysis between server side and browser side can be used to solve the above problems. This article focuses on one of the key building blocks of load-balancing spatial analysis, that is, SVG-based spatial analysis which enables spatial querying and analysis directly on SVG (on the browser side). After analyzing the workflow of spatial analysis, we identify and focus on two key issues in providing spatial analysis on SVG: SVG-based spatial information representation and SVG-based spatial extended structured query language (SSESQL). For the first issue, a theoretical foundation is set up to develop an SVG-based spatial information representation model. Some spatial operators are designed and integrated into an SSESQL to support spatial querying on SVG. Finally, we design and implement two case studies. The results of these case studies show that the proposed method is feasible and operable in supporting spatial analysis directly on SVG on the browser side. The proposed method can be easily incorporated with some existing methods (e.g., GML-based spatial analysis on the server side) to provide load-balancing spatial analysis (load balancing between server side and browser side) in XML/GML/SVG-based WebGIS. As a result, users can access high-performance spatial analysis simply via a web browser (such as Internet Explorer and Firefox).