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Über dieses Buch

This book constitutes the refereed proceedings of the 7th International Conference on Geographic Information Science, GIScience 2012, held in Columbus, OH, USA in September 2012. The 26 full papers presented were carefully reviewed and selected from 57 submissions. While the traditional research topics are well reflected in the papers, emerging topics that involve new research hot-spots such as cyber infrastructure, big data, web-based computing also occupy a significant portion of the volume.



Combining Trip and Task Planning: How to Get from A to Passport

Navigation-tools currently give us directions from location A to B. They help us with the physical process of moving from here to there. Tasks in general, are achieved by the subsequent determination and execution of sub-tasks until the goal is achieved. To help achieve the higher-ranking task, we commonly use so called “personal information management”-tools (PIM-tools). They offer possibilities to manage and organize information about errands that have personal or social implications. Such tasks are described in informal ways, todo-lists for example offer the storage of textual description of an errand, sometimes allowing geographic or temporal information to be added. The paper proposes a formalism that can produce instructions leading from A to the fulfilment of the “task”. Thus connecting the high-level task, that represents intentions, with the physical level of navigation.
Amin Abdalla, Andrew U. Frank

Automated Centerline Delineation to Enrich the National Hydrography Dataset

A common problem in the automated generalization of basemaps is extraction of important features for cartographic visualization purposes. The delineation of a stream network centerline poses unique challenges especially when variables such as stream order, channel depth, or flow rate are not available. This paper presents an algorithm for automated delineation of a continuous cartographic centerline through a flowline network encompassing a single subbasin. Six datasets testing the algorithm are drawn from the U.S. National Hydrography Dataset (NHD) to compare among delineations in landscapes with varying terrain and precipitation regimes. Centerline delineation provides a database enrichment, which adds functionality and enables cartographic generalization. A user-defined cutoff value permits progressively inclusive centerline delineations which may be targeted to multiple map scales and purposes.
Chris Anderson-Tarver, Mike Gleason, Barbara Buttenfield, Larry Stanislawski

Evolution Strategies for Optimizing Rectangular Cartograms

A rectangular cartogram is a type of map where every region is a rectangle. The size of the rectangles is chosen such that their areas represent a geographic variable such as population or GDP. In recent years several algorithms for the automated construction of rectangular cartograms have been proposed, some of which are based on rectangular duals of the dual graph of the input map. In this paper we present a new approach to efficiently search within the exponentially large space of all possible rectangular duals. We employ evolution strategies that find rectangular duals which can be used for rectangular cartograms with correct adjacencies and (close to) zero cartographic error. This is a considerable improvement upon previous methods that have to either relax adjacency requirements or deal with larger errors. We present extensive experimental results for a large variety of data sets.
Kevin Buchin, Bettina Speckmann, Sander Verdonschot

Context-Aware Similarity of Trajectories

The movement of animals, people, and vehicles is embedded in a geographic context. This context influences the movement. Most analysis algorithms for trajectories have so far ignored context, which severely limits their applicability. In this paper we present a model for geographic context that allows us to integrate context into the analysis of movement data. Based on this model we develop simple but efficient context-aware similarity measures. We validate our approach by applying these measures to hurricane trajectories.
Maike Buchin, Somayeh Dodge, Bettina Speckmann

Generating Named Road Vector Data from Raster Maps

Raster maps contain rich road information, such as the topology and names of roads, but this information is “locked” in images and inaccessible in a geographic information system (GIS). Previous approaches for road extraction from raster maps typically handle this problem as raster-to-vector conversion and hence the extracted road vector data are line segments without the knowledge of road names and where a road starts and ends. This paper presents a technique that builds on the results from our previous road vectorization and text recognition work to generate named road vector data from raster maps. This technique first segments road vectorization results using road intersections to determine the lines that represent individual roads in the map. Then the technique exploits spatial relationships between roads and recognized text labels to generate road names for individual road segments. We implemented this approach in our map processing system, called Strabo, and demonstrate that the system generates accurate named road vector data on example maps with 92.83% accuracy.
Yao-Yi Chiang, Craig A. Knoblock

An Ordering of Convex Topological Relations

Topological relativity is a concept of interest in geographic information theory. One way of assessing the importance of topology in spatial reasoning is to analyze commonplace terms from natural language relative to conceptual neighborhood graphs, the alignment structures of choice for topological relations. Sixteen English-language spatial prepositions for region-region relations were analyzed for their corresponding topological relations, each of which was found to represent a convex subset within the conceptual neighborhood graph of the region-region relations, giving rise to the construction of the convex ordering of region-region relations. The resulting lattice of the convex subgraphs enables an algorithmic approach to explaining unknown prepositions.
Matthew P. Dube, Max J. Egenhofer

Toward Web Mapping with Vector Data

Improving the use of vector data in web mapping is often shown as an important challenge. Such shift from raster to vector web maps would open web mapping and GIS to new innovations and new practices. The main obstacle is a performance issue: Vector web maps in nowadays web mapping environments are usually too slow and not usable. Existing techniques for vector web mapping cannot solve alone the performance issue. This article describes a unified framework where some of these techniques are integrated in order to build efficient vector web mapping clients and servers. This framework is composed of the following elements: Specific formats for vector data and symbology, vector tiling, spatial index services, and generalization for multi-scale data. A prototype based on this framework has been implemented and has shown satisfying results. Some principles for future standards to support the development of vector web mapping are given.
Julien Gaffuri

spatial@linkedscience – Exploring the Research Field of GIScience with Linked Data

Metadata for scientific publications contain various explicit and implicit spatio-temporal references. Data on conference locations as well as author and editor affiliations – both changing over time – enable insights into the geographic distribution of scientific fields and particular specializations. At the same time, these byproducts of scientific bibliographies offer a great opportunity to integrate data across different bibliographies to get a more complete picture of a domain. In this paper, we demonstrate how the Linked Data paradigm can assist in enriching and integrating such collections. Starting from the bibliographies of the GIScience, COSIT, ACM GIS, and AGILE conference series, we show how to convert the data to Linked Data and integrate the previously separate datasets. We focus on the spatio-temporal aspects and discuss how they help in matching and disambiguating entities such as authors or universities. We introduce a novel user interface to explore the integrated dataset, demonstrating the potential of Linked Data for innovative applications using spatio-temporal information, and discuss how more complex queries can be addressed. While we focus on bibliographies, the presented work is part of the broader vision of a Linked Science infrastructure for e-Science.
Carsten Keßler, Krzysztof Janowicz, Tomi Kauppinen

Crowdsourcing Satellite Imagery Analysis: Study of Parallel and Iterative Models

In this paper we investigate how a crowdsourcing approach i.e. the involvement of non-experts, could support the effort of experts to analyze satellite imagery e.g. geo-referencing objects. An underlying challenge in crowdsourcing and especially volunteered geographical information (VGI) is the strategy used to allocate the volunteers in order to optimize a set of criteria, especially the quality of data. We study two main strategies of organization: the parallel and iterative models. In the parallel model, a set of volunteers performs independently the same task and an aggregation function is used to generate a collective output. In the iterative model, a chain of volunteers improves the work of previous workers. We first study their qualitative differences. We then introduce the use of Mechanical Turk Service as a simulator in VGI to benchmark both models. We ask volunteers to identify buildings on three maps and investigate the relationship between the amount of non-trained volunteers and the accuracy and consistency of the result. For the parallel model we propose a new clustering algorithm called democratic clustering algorithm DCA taking into account spatial and democratic constraints to form clusters. While both strategies are sensitive to their parameters and implementations we find that parallel model tends to reduce type I errors (less false identification) by filtering only consensual results, while the iterative model tends to reduce type II errors (better completeness) and outperforms the parallel model for difficult/complex areas thanks to knowledge accumulation. However in terms of consistency the parallel model is better than the iterative one. Secondly, the Linus’ law studied for OpenStreetMap [7] (iterative model) is of limited validity for the parallel model: after a given threshold, adding more volunteers does not change the consensual output. As side analysis, we also investigate the use of the spatial inter-agreement as indicator of the intrinsic difficulty to analyse an area.
Nicolas Maisonneuve, Bastien Chopard

Quantifying Resolution Sensitivity of Spatial Autocorrelation: A Resolution Correlogram Approach

Raster spatial datasets are often analyzed at multiple spatial resolutions to understand natural phenomena such as global climate and land cover patterns. Given such datasets, a collection of user defined resolutions and a neighborhood definition, resolution sensitivity analysis (RSA) quantifies the sensitivity of spatial autocorrelation across different resolutions. RSA is important due to applications such as land cover assessment where it may help to identify appropriate aggregations levels to detect patch sizes of different land cover types. However, Quantifying resolution sensitivity of spatial autocorrelation is challenging for two important reasons: (a) absence of a multi-resolution definition for spatial autocorrelation and (b) possible non-monotone sensitivity of spatial autocorrelation across resolutions. Existing work in spatial analysis (e.g. distance based correlograms) focuses on purely graphical methods and analyzes the distance-sensitivity of spatial autocorrelation. In contrast, this paper explores quantitative methods in addition to graphical methods for RSA. Specifically, we formalize the notion of resolution correlograms(RCs) and present new tools for RSA, namely, rapid change resolution (RCR) detection and stable resolution interval (SRI) detection. We propose a new RSA algorithm that computes RCs, discovers interesting RCRs and SRIs. A case study using a vegetation cover dataset from Africa demonstrates the real world applicability of the proposed algorithm.
Pradeep Mohan, Xun Zhou, Shashi Shekhar

LocalAlert: Simulating Decentralized Ad-Hoc Collaboration in Emergency Situations

Today, advances in short-range ad-hoc communication and mobile phone technologies allow people to engage in ad-hoc collaborations based solely on their spatial proximity. These technologies can also be useful to enable a form of timely, self-organizing emergency response. Information about emergency events such as a fire, an accident or a toxic spill is most relevant to the people located nearby, and these people are likely also the first ones to encounter such emergencies. In this paper we explore the concept of decentralized ad-hoc collaboration across a range of emergency scenarios, its feasibility, and potentially effective communication protocols. We introduce the LocalAlert framework, an open source agent simulation framework that we have developed to build and test various form s of decentralized ad-hoc collaboration in different emergency situations. Initial experiments identify a number of parameters that affect the likelihood of a successful response under such scenarios.
Silvia Nittel, Christopher Dorr, John C. Whittier

High-Level Event Detection in Spatially Distributed Time Series

This paper presents an approach for the detection of high-level events from spatially distributed time series. The objective is to detect spatially evolving high-level events as aggregate patterns of primitive events. The approach starts with a segmentation of time series into primitive events as building blocks for high-level events. A high-level event ontology is then used to specify the composition of high-level events of interest in terms of initiating, body forming, and terminating primitive events. We illustrate the approach first with simulated time series data to identify traffic congestion events and then with real data to identify storm events from sensor time series collected as part of an ocean observing system deployed in the Gulf of Maine. Detected storm events are compared against NCDC reported storm events as an evaluation of the approach.
Avinash Rude, Kate Beard

Towards Vague Geographic Data Warehouses

Currently, geographic data warehouses provide a means of carrying out spatial analysis together with agile and flexible multidimensional analytical queries over huge volumes of data. However, they do not enable the representation and neither the analysis over real world phenomena that have uncertain locations or vague boundaries, which are denoted by vague spatial objects. In this paper, we introduce the vague geographic data warehouse (vGDW) and its spatially-enabled components at the logical level: attributes, measures, dimensions, hierarchies and queries. We base the vGDW on exact models to represent vague spatial objects. In addition, we combine the fuzzy model with the exact model in relational vGDW to improve the expressiveness of the queries. Finally, a case study is presented to validate our contributions.
Thiago Luís Lopes Siqueira, Cristina Dutra de Aguiar Ciferri, Valéria Cesário Times, Ricardo Rodrigues Ciferri

Measuring the Influence of Built Environment on Walking Behavior: An Accessibility Approach

Walking behavior has been extensively studied from various perspectives. In this paper, we review the influence of built environment on walking behavior and argue that a longitudinal design with the change of built environment can identify the real influences. We then present a location-based walking accessibility measure for the impact evaluation and describe its methodology with an illustration in a hilly topography community that is experiencing a built environment changes.
Guibo Sun, Hui Lin, Rongrong Li

Social Welfare to Assess the Global Legibility of a Generalized Map

Cartographic generalization seeks to summarize geographical information from a geo-database to produce a less detailed and readable map. The specifications of a legible map are translated into a set of constraints to guide the generalization process and evaluate it. The global evaluation of the map, or of a part of it, consisting in aggregating all the single constraints satisfactions, is still to tackle for the generalization community. This paper deals with the use of the social welfare theory to handle the aggregation of the single satisfactions on the map level. The social welfare theory deals with the evaluation of the economical global welfare of a society, based on the individual welfare. Different social welfare orderings are adapted to generalization, compared and some are chosen for several generalization use cases. Experiments with topographic maps are carried out to validate the choices.
Guillaume Touya

Investigations into the Cognitive Conceptualization and Similarity Assessment of Spatial Scenes

Formally capturing spatial semantics is a challenging and still largely unsolved research endeavor. Qualitative spatial calculi such as RCC-8 and the 9-Intersection model have been employed to capture humans’ commonsense understanding of spatial relations, for instance, in information retrieval approaches. The bridge between commonsense and formal semantics of spatial relations is established using similarities which are, on a qualitative level, typically formalized using the notion of conceptual neighborhoods. While behavioral studies have been carried out on relations between two entities, both static and dynamic, similar experimental work on complex scenes involving three or more entities is still missing. We address this gap by reporting on three experiments on the category construction of spatial scenes involving three entities in three different semantic domains. To reveal the conceptualization of complex spatial scenes, we developed a number of analysis methods. Our results show clearly that (I) categorization of relations in static scenarios is less dependent on domain semantics than in dynamically changing scenarios, that (II) RCC-5 is preferred over RCC-8, and (III) that the complexity of a scene is broken down by selecting a main reference entity.
Jan Oliver Wallgrün, Jinlong Yang, Alexander Klippel, Frank Dylla

A Qualitative Bigraph Model for Indoor Space

Formal models of indoor space for reasoning about navigation tasks should capture key static and dynamic properties and relationships between agents and indoor spaces. This paper presents a method for formally representing indoor environments, key indoor events that occur in them, and their effects on the topological properties and relationships between indoor spaces and mobile entities. Based on Milner’s bigraphical models, our indoor bigraphs provide formal algebraic specifications that independently represent agent and place locality (e.g., building hierarchies) and connectivity (e.g., path based navigation graphs). We illustrate how the model supports the description of scenes and narratives with incomplete information, and provide a set of reaction rules dictating legal system transformations to support goal-directed navigation. Given a starting scene and a particular navigation task we can determine potential sequences of events satisfying a goal (e.g., if a building fire occurs, what actions can an agent take to reach an exit?).
Lisa A. Walton, Michael Worboys

Dynamic Refuse Collection Strategy Based on Adjacency Relationship between Euler Cycles

Our objective is to reduce the risk of overwork in the refuse collection procedure while keeping efficient routes. On optimum routes in refuse collection, vehicles pass through each road segment only once. When we look upon our road network as a graph, the optimum route is Euler graph. Euler graph consists of several Euler cycles. When Euler cycles are exchanged in Euler graph, these cycles are yet Euler cycles if the exchanged cycles are adjacent. Our idea is to construct the cycle graph, which represents cycles as nodes and connective relationships between adjacent cycles as links, from Euler graph. It is guaranteed that the cycle based on links in the cycle graph does not generate the redundancy. In the computer simulation, we conclude that our method is effectively applicable to many kinds of road networks.
Toyohide Watanabe, Kosuke Yamamoto

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

Indoor positioning systems are used as a supplement to GPS where the satellite based technology does not work appropriately. However, positioning accuracy varies among techniques and algorithms used; system performance is also affected by local traffic and environmental structure. A relatively little studied topic is the effect of positioning variance on a user’s opinion or trust of such systems (GPS as well, for that matter). An experiment was designed to examine how trust changes with positioning accuracy and whether trust can be built and maintained over time despite changes in positioning accuracy. We used a simulated version of our existing indoor positioning system to present groups of users with a series of positioning results with varying accuracy. Positions fell into one of three categories: 1. ACCURATE (<5 meters of error), 2. INACCURATE (>15 meters), and 3. WRONG BUILDING (outside current building). When a user experiences a series of accurate results first their trust of later inaccurate positioning is different from users who experience inaccurate locations first.
Ting Wei, Scott Bell

Ontology for the Engineering of Geospatial Systems

In this paper, a metamodel ontology is introduced to describe a domain of data components for geospatial data and query systems. The ontology satisfies the need to model the more complex environment that occurs within a geospatial system. For example, contrary to typical databases, geospatial data have additional metadata files describing the actual data. Also, a geospatial system may have domain ontologies in addition to semantic mappings. Currently, user knowledge is required to know the relationships between all data components (data, metadata, ontologies, mappings, etc.). Contrary to that, we propose a system ontology over which automatic inferencing can be done to determine relationships and meanings among data components. This work fits into the vision of the Semantic Web and interlinked data and knowledge networks and applies these notions to a metamodel for a data system.
Nancy Wiegand

Preserving Detail in a Combined Land Use Ontology

Resolving land use codes between jurisdictions has been an on-going problem due to differences in terms and the nuances of partial similarity of concepts. This paper reports on creating a land use ontology that, contrary to being limited to the highest level of codes or to the most-often used codes, retains all codes. It is also novel in that it records the more subtle relationships between codes rather than just using subclassing. The purpose of creating this comprehensive type of ontology is to provide precise answers to searches of heterogeneous land use codes across jurisdictions. Land use affects important planning decisions, and detail is critical. To query the ontology, custom Java code was written, rather than using SPARQL, to be able to traverse down or up the tree to find the closest matching code when an exact match does not occur.
Nancy Wiegand

The Maptree: A Fine-Grained Formal Representation of Space

This paper introduces a new formal structure, called the maptree, that is shown to uniquely specify, up to homeomorphism, the topological structure of embeddings of graphs in orientable, closed surfaces. A simple modification is made to show that the representation also works for planar embeddings. It is shown that the maptrees are capable of providing a rich representation of the topology of 2D spatial objects and their relationships. The maptree representation is then used to characterize some properties of topological change in these embeddings.
Michael Worboys

Automatic Creation of Crosswalk for Geospatial Metadata Standard Interoperability

Geospatial metadata is very important for describing, managing, querying, retrieving, exchanging and transmitting geospatial data and information resource. As the number, size and complexity of the geospatial metadata standards grow, the task of facilitating greater interoperability between different metadata standards becomes more difficult and important. Crosswalk is the key point to reach interoperability over geospatial metadata standards. Our goal is to provide the automatic creation of crosswalk for heterogeneous geospatial metadata standard interoperability. We introduce a brief but comprehensive overview of the various geospatial metadata standards and describe the related work of geospatial metadata crosswalks. Next, we design a series of formal definitions for geospatial metadata standard mapping. Then, we discuss the multiple attributes similarity of geospatial metadata standard. Next, we introduce the method of automatic creation of crosswalk and mapping based on multiple attribute similarity. Finally we demonstrate our approach and its accuracy using an established crosswalk (CSDGM and ISO 19115).
Hui Yang, Gefei Feng

A Dartboard Network Cut Based Approach to Evacuation Route Planning: A Summary of Results

Given a transportation network, a population, and a set of destinations, the goal of evacuation route planning is to produce routes that minimize the evacuation time for the population. Evacuation planning is essential for ensuring public safety in the wake of man-made or natural disasters (e.g., terrorist acts, hurricanes, and nuclear accidents). The problem is challenging because of the large size of network data, the large number of evacuees, and the need to account for capacity constraints in the road network. Promising methods that incorporate capacity constraints into route planning have been developed but new insights are needed to reduce the high computational costs incurred by these methods with large-scale networks. In this paper, we propose a novel scalable approach that explicitly exploits the spatial structure of road networks to minimize the computational time. Our new approach accelerates the routing algorithm by partitioning the network using dartboard network-cuts and groups node-independent shortest routes to reduce the number of search iterations. Experimental results using a Minneapolis, MN road network demonstrate that the proposed approach outperforms prior work for CCRP computation by orders of magnitude.
KwangSoo Yang, Venkata M. V. Gunturi, Shashi Shekhar

Hybrid Geo-spatial Query Methods on the Semantic Web with a Spatially-Enhanced Index of DBpedia

Semantic Web resources such as DBpedia provide a rich source of structured knowledge about geographical features such as towns, rivers and historical buildings. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment or proximity is not always straightforward because there is considerable inconsistency in the way in which geographical features are referenced to location. In DBpedia some geographical feature instances have point coordinates, some have qualitative properties that provide explicit or implicit locational information via place names, and some have neither of these. Here we show how structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid strategy that exploits both quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators.
Eman M. G. Younis, Christopher B. Jones, Vlad Tanasescu, Alia I. Abdelmoty

Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data

The rapid development of information and communication technologies (ICTs) has provided rich resources for spatio-temporal data mining and knowledge discovery in modern societies. Previous research has focused on understanding aggregated urban mobility patterns based on mobile phone datasets, such as extracting activity hotspots and clusters. In this paper, we aim to go one step further from identifying aggregated mobility patterns. Using hourly time series we extract and represent the dynamic mobility patterns in different urban areas. A Dynamic Time Warping (DTW) algorithm is applied to measure the similarity between these time series, which also provides input for classifying different urban areas based on their mobility patterns. In addition, we investigate the outlier urban areas identified through abnormal mobility patterns. The results can be utilized by researchers and policy makers to understand the dynamic nature of different urban areas, as well as updating environmental and transportation policies.
Yihong Yuan, Martin Raubal


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