Web and Wireless Geographical Information Systems

In this paper, we present a novel strategy for distributed spatial query optimization that involves multiple sites. Most previous work in the area of distributed spatial query processing and optimization focuses only on strategies for performing spatial joins and spatial semijoins, and distributed spatial queries that only involve two sites. We propose a new strategy, called the Restricted strategy, for optimizing a distributed spatial query. It uses spatial semijoins and can involve any number of sites in a distributed spatial database. The Restricted strategy improves upon an existing strategy by sending group approximations, instead of sending approximations for all objects, in order to reduce the number of comparisons between objects and thereby minimize the CPU and data transmission cost. A performance evaluation of our strategy finds that it significantly minimizes the number of data comparisons and CPU time of distributed spatial queries.

Western society is distinguished by its mobility. At no time in our history have we enjoyed the capacity to travel as rapidly, conveniently and safely. On the surface, this might suggest that human mobility patterns are highly irregular and impossible to predict. Drawing on a detailed multisensory positioning data set, we replicate earlier cell tower based predictability analyses with granular spatial and temporal multisensory data, and demonstrate a spatial resolution dependence of entropy, while reinforcing the claims of inherent predictability of human mobility advanced in early works. We demonstrate that mobility entropies reported with GPS data remain essentially unchanged with pruning of noisy GPS signals, lending additional credence to our methodology. We further compare cell tower results to those from WiFi-based localization for exactly the same time periods and participants, and demonstrate that the finer spatial resolution of WiFi also results in reported entropy exceeding that from cell tower traces, indicating that the resolution dependence observed for GPS data is not entirely due to GPS noise or discretization representation. This work represents a significant step towards fundamental understanding of human mobility patterns, which serve as key mediators to policy design in fields as diverse as public health, urban planning, and delay tolerant networks.

With the rapid development of location sensing technology such as GPS, huge amount of location data through GPS are produced every day. The flood of taxi GPS data make it possible to predict the plentitude of traffic events on road network. In this paper, we propose a data-driven approach for traffic state convergence prediction on road network. We introduce a new method predicting the future location of taxis on road network. Furthermore we propose a statistical model to predict real time convergence on road network. We experimentally demonstrated that our approach achieves high prediction precision on the real world massive taxi GPS data.

The present article introduces the outdoor activity tour suggestion problem (OATSP). This problem involves finding a closed path of maximal attractiveness in a transportation network graph, given a target path length and tolerance. Total path attractiveness is evaluated as the sum of the average arc attractiveness and the sum of the vertex prizes in the path. This problem definition takes its rise in the design of an interactive web application, which suggests closed paths for several outdoor activity routing modi, such as mountain biking. Both path length and starting point are specified by the user. The inclusion of POIs of some given types enrich the suggested outdoor activity experience.

A fast method for the generation of heuristic solutions to the OATSP is presented. It is based on spatial filtering, the evaluation of triangles in a simplified search space and shortest path calculation. It generates valuable suggestions in the context of a web application. It is a promising method to generate candidate paths used by any local search algorithm, which further optimizes the solution.

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

Mobile users frequently pass non-obvious features that could be represented to the user in a multi-modal manner. This type of information can be used to affect the decision making of the user or to complement his or her navigation experience. However, data providers do not have a common data interchange schema for describing geographical features multi-modally. This paper presents a multi-modal approach by extending the GeoJSON, GML, and KML formats to describe the surroundings of a mobile user in a Location-Based Service. In addition, the paper discusses how the approach can be implemented on a mobile client. Finally, the paper demonstrates how the proposal has been implemented with a functional prototype for a hiking use case.

The increasing number of mobile devices allows users to access applications anytime and anywhere. In such applications, location is a key information to improve the interaction between user and services. Existing applications combine location with other context information, such as weather, user’s activity, temperature, among others. However, developing context-aware applications is still a non-trivial task due to the complexity to implement context management. Additionally, existing context management infrastructures are too brittle to handle changes in the underlying execution infrastructure. In this scenario, this work proposes a context acquisition framework, which tries to reduce the development complexity of mobile spatial and context-aware applications. The framework uses tuples space and OSGi to promote uncoupling and to adapt itself according to application requirements. A proof of concept was developed in order to show how spatial and context filters can be easily implemented during the development of a tracking application.

Movement is a ubiquitous phenomenon in the physical and virtual world. Analysing movement can reveal interesting trends and patterns. In the Human-Computer Interaction (HCI) domain, eye and mouse movements reveal the interests and intentions of users. By identifying common HCI patterns in the spatial domain, profiles containing the spatial interests of users can be generated. These profiles can be used to address the spatial information overload problem through map personalisation. This paper presents the analysis and findings of a case study of users performing spatial tasks on a campus map. Mouse movement was recorded and analysed as users performed specific spatial tasks. The tasks correspond to the mouse trajectories produced while interacting with the Web map. When multiple users conduct similar and dissimilar spatial tasks, it becomes interesting to observe the behaviour patterns of these users. Clustering and geovisual analysis help to understand large movement datasets such as mouse movements. The knowledge gained through this analysis can be used to strengthen map personalisation techniques. In this work, we apply OPTICS clustering algorithm to a set of map user trajectories. We focus on two similarity measures and compare the results obtained with both when applied to particular saptial tasks carried out by multiple users. In particular, we show how route-based similarity, an advanced distance measure, performs better for spatial tasks involving scanning of the map area.

This paper presents a unique formulation and concept of the dynamic object effect on constant objects, such as buildings, dealing with visibility problems in 3D urban environments. Dynamic objects in a 3D urban environment, such as cars, pedestrians and trees, are usually omitted from visibility analysis. In order to challenge this problem, we focus on modeling predicting and estimating dynamic objects’ future location in the environment. We integrate all these factors into our visibility analysis and create a probabilistic visibility analysis, changed over time due to the dynamic character of these objects.

Our probabilistic visibility concept takes into account 3D boxes and cylinders, generating a fast and exact analytic solution to dynamic and static objects; to illustrate our concept, we use web-cameras located at constant points in the treated environments in order to update our model in each time period from web source data and to analyze the environment. Dynamic objects prediction is based on validated models for driver behavior; pedestrians’ walking routes, trees’ displacement and wind effect. A real urban environment with dynamic objects approximated by 3D boxes and cylinders demonstrates our approach.

In this paper, we present a web-based system, termed ISOGA, that uses isochrones to perform geographical reachability analysis. An isochrone in a spatial network covers all space points from where a query point is reachable within given time constraints. The core of the system builds an efficient algorithm for the computation of isochrones in multimodal spatial networks. By joining isochrones with other databases, various kinds of geospatial reachability analysis can be performed, such as how well is a city covered by public services or where to look for an apartment at moderate prices that is close to the working place. ISOGA adopts a service-oriented three-tier architecture and uses technologies that are compliant with OGC standards. We describe several application scenarios in urban and extra-urban areas, which show the applicability of the tool.

Large amount of data are produced at different spatiotemporal scales by many sensors observing Earth and model simulations. Although advancements of contemporary technologies provide better solutions to access the spatiotemporal data, it is still a big challenge for researchers to easily extract information and knowledge from the data due to the data complexities of high dimensions, heterogeneity, distribution, large amount and frequently updating. This is especially true in climate studies, because climate data with coverage of the entire Earth and a long time period (such as 200 years) are often required to extract useful climate change information and patterns. A well-developed online visual analytical system has the potential to provide an efficient mechanism to bridge this gap. Using performance improving techniques for an online visual analytical system, we researched and developed a high performance Web-based system for spatiotemporal data visual analytics includes the following components: 1) a Spatial Data Registration Center for managing the big spatiotemporal data and enabling researchers to focus on analyses without worrying about data related issues such as format, management and storage; 2) a workflow for pre-generating and caching frequently requested data to reduce the server response time; and 3) a technique of “single data fetch, multiple analyses” to reduce both server response time and client response time; Finally, we demonstrate the effectiveness of the prototype through a few use cases.

Accessibility data/information is necessary to support the everyday mobility of people with special needs. As a means to accommodate mobility of students with special needs, universities and colleges provide maps with accessibility data/information for their campus, where some are static and others are interactive. In this paper, we describe the concept of Personalized Accessibility Maps (PAMs) and discuss the development of a PAM for the University of Pittsburgh’s main campus as a representative PAM. As a result of this development, there is a better understanding of the technologies and techniques needed for PAMs along with challenges and future research directions.

Mobile devices provide an interesting context for map drawing. This paper presents a novel road-selection algorithm based on PageRank, the algorithm famously used by Google to rank web pages by importance. Underlying the PageRank calculation is a probabilistic model of user behavior. We provide suitable generalizations of this model to road networks. Our implementation of the proposed algorithm handles a sizable map in approximately a tenth of a second on a desktop PC. Therefore, our methods should be feasible on modern mobile devices.