Web and Wireless Geographical Information Systems: 14th International Symposium, W2GIS 2015, Grenoble, France, May 21-22, 2015, Proceedings
Abstract
This book constitutes the refereed conference proceedings of the 14th International Symposium, W2GIS 2015, held in Grenoble, France, in May 2015.
The 12 revised full papers presented were carefully selected from 19 submissions. Selected papers cover hot topics related to W2GIS including spatiotemporal data collection, processing and visualization, mobile user generated content, semantic trajectories, locationbased Web search, Cloud computing and VGI approaches.
Chapters (11)
With the advent of Volunteered Geographical Information (VGI), the amount of user-contributed spatial data grows around the world each day. Such spatial data may contain valuable information which may help other research fields, such as the Digital Gazetteers used in Geographic Information Retrieval (GIR), for instance. The Digital Gazetteers have a powerful role in the geoparsing process. They need to keep themselves up-to-date and as complete as possible to enable geoparsers to perform lookup and then resolve toponym recognition precisely over digital texts. In this context, this paper proposes a method of gazetteer enrichment leveraging VGI data sources. Indeed VGI environments are not originally developed to work as gazetteers, however, they often contain more detailed and up-to-date information than gazetteers. Our method is applied in a geoparser environment by adapting its heuristics set besides enriching the corresponding gazetteer. A case study was performed by geoparsing Twitter messages focused solely on the microtexts in order to evaluate the performance of the enriched system. The results obtained were compared with previous results of a case study that used the same dataset and both the gazetteer and the geoparser without improvements.
In this paper, we measure crowd mood and investigate its spatiotemporal distributions in a large-scale urban area through Twitter. In order to exploit tweets as a source to survey crowd mind, we propose two measurements which extract and categorize semantic terms from texts of tweets based on a dictionary of emotional terms. In particular, we focus on how to aggregate crowd mood quantitatively and qualitatively. n the experiment, the proposed methods are applied to a large tweets dataset collected for an urban area in Japan. From the daily tweets, we were able to observe interesting temporal changes in crowd's positive and negative moods and also identified major downtown areas where crowd's emotional tweets are intensively found. In this preliminary work, we confirme the diversity of urban areas in terms of crowd moods which are observed from the crowd-sourced lifelogs on Twitter.
The production of strokes according to the perceptual grouping of arcs in a road network provides a good basis for the generalization of road networks, but a large amount of time is required for their creation and selection, and they lack associations with the map objects along them. In this study, we propose a system for generalizing a guide map with road networks and category-based web search results on demand in response to a user request, or a triplet of an area, a size, and a category. The main features of the proposed system are as follows. (1) It constructs a database of strokes and refines the strokes by considering the actual movements of people. It also introduces a data structure called a “fat-stroke,” which combines web search results with the strokes. Pre-construction of the fat-stroke database facilitates the generalization of a guide map on demand to satisfy a user request. (2) It ranks the strokes in order of significance in a guide map according to the web search results combined with the strokes, as well as their length. (3) It determines the number of strokes that need to be drawn on a map based on the map scale and the proportion of road area relative to the whole area in the map. We developed a prototype of the proposed system and a preliminary evaluation demonstrated that pre-construction of the fat-stroke database reduced the response time for guide map generalization to less than 1 s, and thus it can be applied to web map services.
We present a new method for navigating in a street network using solely data acquired by a (smartphone integrated electronic) compass for self-localization. To make compass-based navigation in street networks practical, it is crucial to deal with all kinds of imprecision and different driving behaviors. We therefore develop a trajectory representation based on so-called inflection points which turns out to be very robust against measurement variability. To enable real-time localization with compass data, we construct a custom-tailored data structure inspired by algorithms for efficient pattern search in large texts. Our experiments reveal that on average already very short sequences of inflection points are unique in a large street network, proving that this representation allows for accurate localization.
To manage the voluminous and complex Steam Assisted Gravity Drainage (SAGD) data and accommodate the spatial and temporal components, a database management system working interactively with a web GIS mapping interface is designed and built. Public and proprietary SAGD data are collected from multiple sources and archived. Multiple spatial layers and flexible spatial queries can help users efficiently target SAGD wells. Furthermore, intuitive and interactive data visualization methods like attribute table, histograms and charts and time-series data viewers, as well as data mining techniques like clustering and association rule mining are implemented in the system for users to explore and comprehend SAGD data and make decisions.
Estimating how many records qualify for a spatial predicate is crucial when choosing a cost-effective query execution plan, especially in presence of extra non-spatial criteria. The challenge is far bigger with geospatial data on the Web, as information is inherently disparate in many sites and effective search should avoid transmission of large datasets. Our idea is that fast, succinct, yet reliable estimates of spatial selectivity could incur significant reduction in query execution costs. Towards this goal, we examine variants of well known spatial indices enhanced with data distribution statistics, essentially building spatial histograms. We compare these methods in terms of performance and estimation accuracy over real datasets and query workloads of varying range. Our empirical study exhibits their pros and cons and confirms the potential of spatial histograms for optimized search on the Web of Data.
Nowadays, the increasing development of positioning and wireless communication technologies favors a better real-time integration and manipulation of large spatial databases. This offers many new opportunities for the development of trajectory databases, but a number of research challenges are still open as the generated information is often unstructured, continuous, large and sometimes unpredictable. The research presented in this paper develops a modeling approach that integrates the semantic, spatial and temporal dimensions when representing spatial trajectories at the abstract and logical levels. A data manipulation language that supports the querying and analysis of large trajectory databases is also proposed. The spatial database model is based on algebraic data types, and a prototype is developed on top of the DBMS PostgreSQL/PostGIS. The whole approach and the prototype development have been experimented and applied to benchmark transportation data derived from an origin-destination survey in the region of Quebec in Canada.
Trajectories have been providing us with a wealth of derived information such as traffic conditions and road network updates. This work focuses on deriving user profiles through spatiotemporal analysis of trajectory data to provide insight into the quality of information provided by users. The presented behavior profiling method assesses user participation characteristics in a treasure-hunt type event. Consisting of an analysis and a profiling phase, analysis involves a timeline and a stay-point analysis, as well as a semantic trajectory inspection relating actual and expected paths. The analysis results are then grouped around profiles that can be used to estimate the user performance in the activity. The proposed profiling method is evaluated by means of a student orientation treasure-hunt activity at the University of Twente, The Netherlands. The profiling method is used to predict the students' gaming behavior by means of a simple team type classification, and a feature-based answer type classification.
Managing the data generated by emerging spatiotemporal data sources, such as geosensor networks, presents a growing challenge to traditional, offline GIS architectures. This paper explores the development of an end-to-end system for near real-time monitoring of environmental variables related to wildfire hazard, called RISER. The system is built upon a geosensor network and web-GIS technologies, connected by a stream-processing system. Aside from exploring the system architecture, this paper focuses specifically on the important role of stream processing as a bridge between data capture and web GIS, and as a spatial analysis engine. The paper highlights the compromise between efficiency and accuracy in spatiotemporal stream processing that must often be struck in the stream operator design. Using the specific example of spatial interpolation operators, the impact of changes to the configurations of spatial and temporal windows on the accuracy and efficiency of different spatial interpolation methods is evaluated.
Crowdsourcing market systems (CMS) are platforms that enable one to publish tasks that others are intended to accomplished. Usually, these are systems where users, called workers, perform tasks using desktop computers. Recently, some CMS have appeared with spatiotemporal tasks that requires a worker to be at a given location within a given time window to be accomplished. In this paper, we introduce the trajectory recommendation problem (or TRP) where a CMS tries to find and recommend a trajectory for a mobile worker that allows him to accomplish tasks he has some affinity with without compromising his arrival in time at destination. We show that TRP is NP-hard and then propose an exact algorithm for solving it. Our experimentation proved that using our algorithm for recommending trajectories is a feasible solution when up to a few hundred tasks must be analyzed to find an optimal solution.
One of the most important aspects to consider when computing large data sets is to distribute and parallelize the analysis algorithms. A distributed system presents a good performance if the workload is properly balanced. It is expected that the computing time is directly related to the processing time on the node where the processing takes longer. This paper aims at proposing a data partitioning strategy that takes into account partition balance and that is generic for spatial data. Our proposed solution is based on a grid model data structure that is further transformed into a graph partitioning problem, where we finally compute the partitions. Our proposed approach is used on the distributed DBSCAN algorithm and it is focused on finding density areas in a large data set using MapReduce. We call our approach G2P (Grid and Graph Partitioning) and we show via massive experiments that G2P presents great quality data partitioning for the distributed DBSCAN algorithm compared to the competitors. We believe that G2P is not only suitable for DBSCAN algorithm, but also to execute spatial join operations and distance based range queries to name to a few.
... Gensel and Tomko [42] integrated users into the landmark selection by introducing a mobile application that enables a user-generated collection of landmarks. They used Wikipedia to determine the cultural and historical significance of the collected landmarks. ...
Landmarks are important for assisting in wayfinding and navigation and for enriching user experience. Although many user-generated geotagged sources exist, landmark entities are still mostly retrieved from authoritative geographic sources. Wikipedia, the world’s largest free encyclopedia, stores geotagged information on many geospatial entities, including a very large and well-founded volume of landmark information. However, not all Wikipedia geotagged landmark entities can be considered valuable and instructive. This research introduces an integrated ranking model for mining landmarks from Wikipedia predicated on estimating and weighting their salience. Other than location, the model is based on the entries’ category and attributed data. Preliminary ranking is formulated on the basis of three spatial descriptors associated with landmark salience, namely permanence, visibility, and uniqueness. This ranking is integrated with a score derived from a set of numerical attributes that are associated with public interest in the Wikipedia page―including the number of redirects and the date of the latest edit. The methodology is comparatively evaluated for various areas in different cities. Results show that the developed integrated ranking model is robust in identifying landmark salience, paving the way for incorporation of Wikipedia’s content into navigation systems.
In Volunteered Geographic Information (VGI) projects, the tagging or annotation of objects is usually performed in a flexible and non-constrained manner. Contributors to a VGI project are normally free to choose whatever tags they feel are appropriate to annotate or describe a particular geographic object or place. In OpenStreetMap (OSM), the Map Features part of the OSM Wiki serves as the de-facto rulebook or ontology for the annotation of features in OSM. Within Map Features, suggestions and guidance on what combinations of tags to use for certain geographic objects are outlined. In this paper, we consider these suggestions and recommendations and analyse the OSM database for 40 cities around the world to ascertain if contributors to OSM in these urban areas are using this guidance in their tagging practices. Overall, we find that compliance with the suggestions and guidance in Map Features is generally average or poor. This leads us to conclude that contributors in these areas do not always tag features with the same level of annotation. Our paper also confirms anecdotal evidence that OSM Map Features is less influential in how OSM contributors tag objects.
ResearchGate has not been able to resolve any references for this publication.