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Since its inception in Savannah, Georgia (USA) in 2000, the highly successful GIScience conferenceseries ( regularlyattractedover250 researchers from all over the world whose common interest lies in advancing the research frontiers of fundamental aspects of the production, dissemination, and use of geographic information. The conference is bi-annual and brings together leading researchers from all cognate disciplines re?ecting the interdisciplinary breadth of GIScience, including (but not limited to) geography, cognitive s- ence, computer science, engineering, information science, mathematics, philo- phy, psychology, social science, and (geo)statistics. Following the, literally breathtaking,conference in Park City, Utah (USA) at 2103m, the sixth GIScience 2010 conference returned to Europe for the second time. The 2010 conference was held in Zurich, Switzerland, a place nominated repeatedly as the world’s most livable (if not cheapest!) city. Zurich is also a GIScience landmark, as in 1990 one of the founders of the GIScience conference series, Dr. Michael Goodchild, delivered a memorable talk setting out how f- damental research on GISystems could turn into GIScience at the very same conference location during the Spatial Data Handling Symposium.



A Conceptual Data Model for Trajectory Data Mining

Data mining has become very popular in the last years, and it is well known that data preprocessing is the most effort and time consuming step in the discovery process. In part, it is because database designers do not think about data mining during the conceptual design of a database, therefore data are not prepared for mining. This problem increases for spatio-temporal data generated by mobile devices, which involve both space and time. In this paper we propose a novel solution to reduce the gap between databases and data mining in the domain of trajectories of moving objects, aiming to reduce the effort for data preprocessing. We propose a general framework for modeling trajectory patterns during the conceptual design of a database. The proposed framework is a result of several works including different data mining case studies and experiments performed by the authors on trajectory data modeling and trajectory data mining. It has been validated with a data mining query language implemented in PostGIS, that allows the user to create, instantiate and query trajectory data and trajectory patterns.
Vania Bogorny, Carlos Alberto Heuser, Luis Otavio Alvares

Time-Geographic Density Estimation for Moving Point Objects

This research presents a time-geographic method of density estimation for moving point objects. The approach integrates traditional kernel density estimation (KDE) with techniques of time geography to generate a continuous intensity surface that characterises the spatial distribution of a moving object over a fixed time frame. This task is accomplished by computing density estimates as a function of a geo-ellipse generated for each consecutive pair of control points in the object’s space-time path and summing those values at each location in a manner similar to KDE. The main advantages of this approach are: (1) that positive intensities are only assigned to locations within a moving object’s potential path area and (2) that it avoids arbitrary parameter selection as the amount of smoothing is controlled by the object’s maximum potential velocity. The time-geographic density estimation technique is illustrated with a sample dataset, and a discussion of limitations and future work is provided.
Joni A. Downs

Microtheories for Spatial Data Infrastructures - Accounting for Diversity of Local Conceptualizations at a Global Level

The categorization of our environment into feature types is an essential prerequisite for cartography, geographic information retrieval, routing applications, spatial decision support systems, and data sharing in general. However, there is no a priori conceptualization of the world and the creation of features and types is an act of cognition. Humans conceptualize their environment based on multiple criteria such as their cultural background, knowledge, motivation, and particularly by space and time. Sharing and making these conceptualizations explicit in a formal, unambiguous way is at the core of semantic interoperability. One way to cope with semantic heterogeneities is by standardization, i.e., by agreeing on a shared conceptualization. This bears the danger of losing local diversity. In contrast, this work proposes the use of microtheories for Spatial Data Infrastructures, such as INSPIRE, to account for the diversity of local conceptualizations while maintaining their semantic interoperability at a global level. We introduce a novel methodology to structure ontologies by spatial and temporal aspects, in our case administrative boundaries, which reflect variations in feature conceptualization. A local, bottom-up approach, based on non-standard inference, is used to compute global feature definitions which are neither too broad nor too specific. Using different conceptualizations of rivers and other geographic feature types, we demonstrate how the present approach can improve the INSPIRE data model and ease its adoption by European member states.
Stephanie Duce, Krzysztof Janowicz

The Family of Conceptual Neighborhood Graphs for Region-Region Relations

This paper revisits conceptual neighborhood graphs for the topological relations between two regions, in order to bridge from the A-B-C neighborhoods defined for interval relations in R1 to region relations in R2 and on the sphere S2. A categorization of deformation types—built from same and different positions, orientations, sizes, and shapes—gives rise to four different neighborhood graphs. They include transitions that are constrained by the regions’ geometry, yielding some directed, not undirected neighborhood graphs. Two of the four neighborhood graphs correspond to type B and C. The lattice of conceptual neighborhood graphs captures the relationships among the graphs, showing completeness under union and intersection.
Max J. Egenhofer

Detecting Road Intersections from GPS Traces

As an alternative to expensive road surveys, we are working toward a method to infer the road network from GPS data logged from regular vehicles. One of the most important components of this problem is to find road intersections. We introduce an intersection detector that uses a localized shape descriptor to represent the distribution of GPS traces around a point. A classifier is trained on the shape descriptor to discriminate intersections from non-intersections, and we demonstrate its effectiveness with an ROC curve. In a second step, we use the GPS data to prune the detected intersections and connect them with geometrically accurate road segments. In the final step, we use the iterative closest point algorithm to more accurately localize the position of each intersection. We train and test our method on GPS data gathered from regular vehicles in the Seattle, WA, USA area. The tests show we can correctly find road intersections.
Alireza Fathi, John Krumm

Semantic Referencing – Determining Context Weights for Similarity Measurement

Semantic similarity measurement is a key methodology in various domains ranging from cognitive science to geographic information retrieval on the Web. Meaningful notions of similarity, however, cannot be determined without taking additional contextual information into account. One way to make similarity measures context-aware is by introducing weights for specific characteristics. Existing approaches to automatically determine such weights are rather limited or require application specific adjustments. In the past, the possibility to tweak similarity theories until they fit a specific use case has been one of the major criticisms for their evaluation. In this work, we propose a novel approach to semi-automatically adapt similarity theories to the user’s needs and hence make them context-aware. Our methodology is inspired by the process of georeferencing images in which known control points between the image and geographic space are used to compute a suitable transformation. We propose to semi-automatically calibrate weights to compute inter-instance and inter-concept similarities by allowing the user to adjust pre-computed similarity rankings. These known control similarities are then used to reference other similarity values.
Krzysztof Janowicz, Benjamin Adams, Martin Raubal

User-Centric Time-Distance Representation of Road Networks

This paper presents a new algorithm for computing time-distance transformations of a road network based on modified multi-dimensional scaling. The algorithm is designed to perform on a real-world road network, and provides alternative visualisations for travel time cognition and route planning. Several extensions are explored, including user-centric and route-centric road map transformations. Our implementation of the algorithm can be applied to any locality where travel time road network data is available. Here, it is illustrated on road network data for a rural region in Ireland. Limitations of the proposed algorithm are examined, and potential solutions are discussed.
Christian Kaiser, Fergal Walsh, Carson J. Q. Farmer, Alexei Pozdnoukhov

Efficient Data Collection and Event Boundary Detection in Wireless Sensor Networks Using Tiny Models

Using wireless geosensor networks (WGSN), sensor nodes often monitor a phenomenon that is both continuous in time and space. However, sensor nodes take discrete samples, and an analytical framework inside or outside the WSN is used to analyze the phenomenon. In both cases, expensive communication is used to stream a large number of data samples to other nodes and to the base station. In this work, we explore a novel alternative that utilizes predictive process knowledge of the observed phenomena to minimize upstream communication. Often, observed phenomena adhere to a process with predictable behavior over time. We present a strategy for developing and running so-called ’tiny models’ on individual sensor nodes that capture the predictable behavior of the phenomenon; nodes now only communicate when unexpected events are observed. Using multiple simulations, we demonstrate that a significant percentage of messages can be reduced during data collection.
Kraig King, Silvia Nittel

Combining Synchronous and Asynchronous Collaboration within 3D City Models

This paper presents an approach for combining spatially distributed synchronous and asynchronous collaboration within 3D city models. Software applications use these models as additional communication medium to facilitate communication of georeferenced and geospatial information. Collaboration tools should support both the communication with other collaborators and their awareness of the current collaboration context. To support collaborative knowledge construction and gathering, we have designed a collaboration system to facilitate (a) creation of annotations that have 3D references to the virtual 3D city model and (b) collection information about the context in which these annotations are created. Our approach supports synchronous collaboration in connection with the creation of non volatile, precisely georeferenced units of information allow for a comprehensible form of cooperation in spatially distributed settings. Storage and retrieval of this information is provided through a Web Feature Service, which eases integration of collaboration data into existing applications. We further introduce a visualization technique that integrates annotations as complex structured data into the 3D visualization. This avoids media breaks and disruptions in working processes and creates a spatial coherence between annotation and annotated feature or geometry.
Jan Klimke, Jürgen Döllner

Cognitive Invariants of Geographic Event Conceptualization: What Matters and What Refines?

Behavioral experiments addressing the conceptualization of geographic events are few and far between. Our research seeks to address this deficiency by developing an experimental framework on the conceptualization of movement patterns. In this paper, we report on a critical experiment that is designed to shed light on the question of cognitively salient invariants in such conceptualization. Invariants have been identified as being critical to human information processing, particularly for the processing of dynamic information. In our experiment, we systematically address cognitive invariants of one class of geographic events: single entity movement patterns. To this end, we designed 72 animated icons that depict the movement patterns of hurricanes around two invariants: size difference and topological equivalence class movement patterns endpoints. While the endpoint hypothesis, put forth by Regier (2007), claims a particular focus of human cognition to ending relations of events, other research suggests that simplicity principles guide categorization and, additionally, that static information is easier to process than dynamic information. Our experiments show a clear picture: Size matters. Nonetheless, we also find categorization behaviors consistent with experiments in both the spatial and temporal domain, namely that topology refines these behaviors and that topological equivalence classes are categorized consistently. These results are critical steppingstones in validating spatial formalism from a cognitive perspective and cognitively grounding work on ontologies.
Alexander Klippel, Rui Li, Frank Hardisty, Chris Weaver

A Visibility and Spatial Constraint-Based Approach for Geopositioning

Over the past decade, automated systems dedicated to geopositioning have been the object of considerable development. Despite the success of these systems for many applications, they cannot be directly applied to qualitative descriptions of space. The research presented in this paper introduces a visibility and constraint-based approach whose objective is to locate an observer from the verbal description of his/her surroundings. The geopositioning process is formally supported by a constraint-satisfaction algorithm. Preliminary experiments are applied to the description of environmental scenes.
Jean-Marie Le Yaouanc, Éric Saux, Christophe Claramunt

Area-Preserving Subdivision Schematization

We describe an area-preserving subdivision schematization algorithm: the area of each region in the input equals the area of the corresponding region in the output. Our schematization is axis-aligned, the final output is a rectilinear subdivision. We first describe how to convert a given subdivision into an area-equivalent rectilinear subdivision. Then we define two area-preserving contraction operations and prove that at least one of these operations can always be applied to any given simple rectilinear polygon. We extend this approach to subdivisions and showcase experimental results. Finally, we give examples for standard distance metrics (symmetric difference, Hausdorff- and Fréchet-distance) that show that better schematizations might result in worse shapes.
Wouter Meulemans, André van Renssen, Bettina Speckmann

Periodic Multi-labeling of Public Transit Lines

We designed and implemented a simple and fast heuristic for placing multiple labels along edges of a planar network. As a testbed, real-world data from Google Transit is taken: our implementation outputs an overlay onto Google Maps, adding route numbers to public transit lines.
Valentin Polishchuk, Arto Vihavainen

Comparing the Effectiveness of GPS-Enhanced Voice Guidance for Pedestrians with Metric- and Landmark-Based Instruction Sets

This paper reports on a field experiment comparing two different kinds of verbal turn instructions in the context of GPS-based pedestrian navigation. The experiment was conducted in the city of Salzburg with 20 participants. Both instruction sets were based on qualitative turn direction concepts. The first one was enhanced with metric distance information and the second one was enhanced with landmark-anchored directions gathered from participants of a previous field experiment. The results show that in context of GPS-enhanced pedestrian navigation both kinds of instruction sets lead to similar navigation performance. Results also demonstrate that effective voice-only guidance of pedestrians in unfamiliar environments at a minimal error rate and without stopping the walk is feasible. Although both kinds of instructions lead to similar navigation performance, participants clearly preferred landmark-enhanced instructions.
Karl Rehrl, Elisabeth Häusler, Sven Leitinger

A Mismatch Description Language for Conceptual Schema Mapping and Its Cartographic Representation

Geospatial data offered by distributed services are often modeled with different conceptual schemas although they cover the same thematic area. To ensure interoperability of geospatial data, the existing heterogeneous conceptual schemas can be mapped to a common conceptual schema. However, the underlying formalized schema mappings are difficult to create, difficult to re-use and often contain mismatches of abstraction level, of scope difference, domain semantics and value semantics of the mapped entities. We have developed a novel approach to document and communicate such mismatches in the form of a Mismatch Description Language (MDL). This MDL can be transformed into various textual and cartographic representations to support users in communicating and understanding mismatches, and to assess the reusability of a mapping.
Thorsten Reitz

Detecting Change in Snapshot Sequences

Wireless sensor networks are deployed to monitor dynamic geographic phenomena, or objects, over space and time. This paper presents a new spatiotemporal data model for dynamic areal objects in sensor networks. Our model supports for the first time the analysis of change in sequences of snapshots that are captured by different granularity of observations, and our model allows both incremental and non-incremental changes. This paper focuses on detecting qualitative spatial changes, such as merge and split of areal objects. A decentralized algorithm is developed, such that spatial changes can be efficiently detected by in-network aggregation of decentralized datasets.
Mingzheng Shi, Stephan Winter

Multi-source Toponym Data Integration and Mediation for a Meta-Gazetteer Service

A variety of gazetteers exist based on administrative or user contributed data. Each of these data sources has benefits for particular geographical analysis and information retrieval tasks but none is a one fit all solution. We present a mediation framework to access and integrate distributed gazetteer resources to build a meta-gazetteer that generates augmented versions of place name information. The approach combines different aspects of place name data from multiple gazetteer sources that refer to the same geographic place and employs several similarity metrics to identify equivalent toponyms.
Philip D. Smart, Christopher B. Jones, Florian A. Twaroch

Qualitative Change to 3-Valued Regions

Regions which evolve over time are a significant aspect of many phenomena in the natural sciences and especially in geographic information science. Examples include areas in which a measured value (e.g. temperature, salinity, height, etc.) exceeds some threshold, as well as moving crowds of people or animals. There is already a well-developed theory of change to regions with crisp boundaries. In this paper we develop a formal model of change for more general 3-valued regions. We extend earlier work which used trees to represent the topological configuration of a system of crisp regions, by introducing trees with an additional node clustering operation. One significant application for the work is to the decentralized monitoring of changes to uncertain regions by wireless sensor networks. Decentralized operations required for monitoring qualitative changes to 3-valued regions are determined and the complexity of the resulting algorithms is discussed.
Matt Duckham, John Stell, Maria Vasardani, Michael Worboys

Collaborative Generalisation: Formalisation of Generalisation Knowledge to Orchestrate Different Cartographic Generalisation Processes

Cartographic generalisation seeks to summarise geographical information from a geographic database to produce a less detailed and readable map. This paper deals with the problem of making different automatic generalisation processes collaborate to generalise a complete map. A model to orchestrate the generalisation of different areas (cities, countryside, mountains) by different adapted processes is proposed. It is based on the formalisation of cartographic knowledge and specifications into constraints and rules sets while processes are described to formalise their capabilities. The formalised knowledge relies on generalisation domain ontology. For each available generalisation process, the formalised knowledge is then translated into process parameters by an adapted translator component. The translators allow interoperable triggers and allow the choice of the proper process to apply on each part of the space. Applications with real processes illustrate the usability of the proposed model.
Guillaume Touya, Cécile Duchêne, Anne Ruas

Automatic Extraction of Destinations, Origins and Route Parts from Human Generated Route Directions

Researchers from the cognitive and spatial sciences are studying text descriptions of movement patterns in order to examine how humans communicate and understand spatial information. In particular, route directions offer a rich source of information on how cognitive systems conceptualize movement patterns by segmenting them into meaningful parts. Route directions are composed using a plethora of cognitive spatial organization principles: changing levels of granularity, hierarchical organization, incorporation of cognitively and perceptually salient elements, and so forth. Identifying such information in text documents automatically is crucial for enabling machine-understanding of human spatial language. The benefits are: a) creating opportunities for large-scale studies of human linguistic behavior; b) extracting and georeferencing salient entities (landmarks) that are used by human route direction providers; c) developing methods to translate route directions to sketches and maps; and d) enabling queries on large corpora of crawled/analyzed movement data. In this paper, we introduce our approach and implementations that bring us closer to the goal of automatically processing linguistic route directions. We report on research directed at one part of the larger problem, that is, extracting the three most critical parts of route directions and movement patterns in general: origin, destination, and route parts. We use machine-learning based algorithms to extract these parts of routes, including, for example, destination names and types. We prove the effectiveness of our approach in several experiments using hand-tagged corpora.
Xiao Zhang, Prasenjit Mitra, Alexander Klippel, Alan MacEachren

Visual Exploration of Eye Movement Data Using the Space-Time-Cube

Eye movement recordings produce large quantities of spatio-temporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatio-temporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed.
Xia Li, Arzu Çöltekin, Menno-Jan Kraak

5D Data Modelling: Full Integration of 2D/3D Space, Time and Scale Dimensions

This paper proposes an approach for data modelling in five dimensions. Apart from three dimensions for geometrical representation and a fourth dimension for time, we identify scale as fifth dimensional characteristic. Considering scale as an extra dimension of geographic information, fully integrated with the other dimensions, is new. Through a formal definition of geographic data in a conceptual 5D continuum, the data can be handled by one integrated approach assuring consistency across scale and time dimensions. Because the approach is new and challenging, we choose to step-wise studying several combinations of the five dimensions, ultimately resulting in the optimal 5D model. We also propose to apply mathematical theories on multidimensional modelling to well established principles of multidimensional modelling in the geo-information domain. The result is a conceptual full partition of the 3Dspace+time+scale space (i.e. no overlaps, no gaps) realised in a 5D data model implemented in a Database Management System.
Peter van Oosterom, Jantien Stoter


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