Experiments to Examine the Situated Nature of Geoscientific Concepts

Brodaric, Boyan; Gahegan, Mark

doi:10.1080/13875860701337934

Cited by 27 publications

(20 citation statements)

References 41 publications

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“…Yet, in contrast to other scientific domains, we cannot define a context-free and canonical representation of geographic features or even their corresponding types (Mark, 1993;Brodaric and Gahegan, 2007). For instance, there is no pre-given and common definition of Forest, Mountain, or Lake (Lund, 03/22/2010;Smith and Mark, 2003;Montello and Sutton, 2006).…”

Section: Motivationmentioning

confidence: 99%

“…However, we are not interested in such stimuli but in what they reveal about the properties of specific entities in the physical world -the features of interest. Such features and their corresponding types, however, do not exist a priori but are an artifact of human cognition and social convention (Brodaric and Gahegan, 2007;Mark, 1993;Lehar, 2003). The extraction of features from sensor observations requires several processing steps that are arbitrary to a certain degree.…”

Section: Establishing Meaningful Urismentioning

confidence: 99%

“…Grounded in spatial and temporal reference systems, the outcomes of a variety of services can be combined into a multi-layered representation of the Earth's surface and help to answer scientific questions or assist in emergency situations. A rigid standardization process as well as conformance tests ensure that a multitude of services can be integrated into SDIs to realize advanced tasks such as predictions.Yet, in contrast to other scientific domains, we cannot define a context-free and canonical representation of geographic features or even their corresponding types (Mark, 1993;Brodaric and Gahegan, 2007). For instance, there is no pre-given and common definition of Forest, Mountain, or Lake (Lund, 03/22/2010;Smith and Mark, 2003;Montello and Sutton, 2006).…”

mentioning

confidence: 99%

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A RESTful proxy and data model for linked sensor data

Janowicz

Bröring

Stasch

et al. 2013

International Journal of Digital Earth

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The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration.. So far, sensor data is stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies towards their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (i) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (ii) how to establish meaningful links between those data sets, i.e., observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.Keywords: Digital Earth, Geospatial data integration, Spatial Data Infrastructure, Earth Observation, Data exchange models, Linked Data, Semantic Enablement * Corresponding author: Krzysztof Janowicz; email: jano@geog.ucsb.edu 1 MotivationThe initial vision of a Digital Earth was first formulated by former US Vice President Al Gore as a multi-resolution, three-dimensional representation of the planet, into which we can embed vast quantities of geo-referenced data (Gore, 1998). Ten years after this speech, Craglia et al. published a position paper to argue that this vision has not yet been achieved (Craglia et al., 2008). In parallel to the growing availability of information, the need to better understand the interplay of environmental and social phenomena has also increased, thus requiring more dynamic systems, new sources of information, and stronger capacities for their integration. The Sensor Web has been identified as a central building block to address these challenges (De Longueville et al., 2010). A digital nervous system for the globe has been suggested as a vibrant approach for the Digital Earth. An implementation based on Spatial Data Infrastructures (SDI), especially on the Sensor Web Enablement (SWE) standards of the Open Geospatial Consortium (OGC), has been proposed. These infrastructures do not only deliver data but also offer geospatial processing capabilities and the final rendering on a virtual globe. Grounded in spatial and temporal reference systems, the outcomes of a variety of services can be combined into a multi-layered representation of the Earth's surface and help to an...

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Section: Motivationmentioning

confidence: 99%

Section: Establishing Meaningful Urismentioning

confidence: 99%

mentioning

confidence: 99%

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A RESTful proxy and data model for linked sensor data

Janowicz

Bröring

Stasch

et al. 2013

International Journal of Digital Earth

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“…Results can be used to construct observation collection responses encoded in Observations and Measurements (O&M) [10]. 5 Based on previous work on SemSOS, Patni built in his M.Sc. thesis [41] a system to convert real-time heterogeneous sensor data into an integrated stream of abstractions (also called features in the thesis), and to reason over them by using background knowledge.…”

Section: Previous Work On Semantic Event Processingmentioning

confidence: 99%

An event abstraction layer for the integration of geosensor data

Llaves¹,

Kuhn²

2014

International Journal of Geographical Information Science

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Abstract. Time series of observations reflect the status of environmental properties. Variations in these properties can be considered events when they potentially affect the stability of the monitored environment. Organisations dedicated to analyse environmental change use institutionalised descriptions of events to define the observable conditions under which events happen. This also applies to the methods used to classify and model changes in environmental monitoring. The heterogeneity of representations often causes interoperability problems when such communities exchange geospatial information. To enhance interoperability among diverse communities, it is required to develop models that do not restrict the representation of events, but allow integrating different perspectives on changes in the environment. The goal of the Event Abstraction Layer is to facilitate the analysis and integration of geosensor data by inferring events from time series of observations. For the analysis of geosensor data, we use event processing to detect event patterns in time series of observations. Spatio-temporal properties of the event are inferred from the geosensor location and the observation timestamps. For the data integration, we represent event-related information extracted from multiples sources under a common event model. Additionally, domain knowledge is modelled in a multilevel ontology structure.

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“…For example, Gabora and collaborators (Gabora et al, 2008) are developing an ecological theory of concepts to account for contextual effects employing ideas from quantum mechanics. In a complementary direction, researchers interested in scientific aspects of classification and conceptualization incorporate context dependent factors (situatedness) to account for alternative conceptualizations of the same part of the world (Brodaric & Gahegan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

Klippel

Weaver²,

Robinson

2011

Cartography and Geographic Information Science

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ABSTRACT. The conceptualization of spatio-temporal information is an interdisciplinary research area. The focus of this article is on human conceptualizations of spatio-temporal geographic phenomena (also referred to as events). Identifying and understanding human conceptualizations is a crucial component in defining the semantics of spatiotemporal information. However, most research focuses primarily on how humans imbue dynamic phenomena with meaning on a general level. In contrast, this article is concerned with contextual factors (specifically: individual differences) that are too often neglected in general theories and in the analysis of behavioral data. In other words, we are interested in individual or group strategies of participants that are not detected by classical analysis methods. Research on individual difference is gaining widespread attention in cognitive and spatial sciences and it is time to consider individual differences in the area of conceptualizing spatio-temporal information. To be able to shed light on individual differences in behavioral data on how people conceptualize events, we have developed software solutions and combined them with established similarity measures. These software solutions allow analysts to effectively explore individual differences. We demonstrate the feasibility of our approach, its usefulness in analyzing behavioral data, and results that can be obtained through this individualized analysis by reanalyzing four sets of experimental data we previously collected.

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Experiments to Examine the Situated Nature of Geoscientific Concepts

Cited by 27 publications

References 41 publications

A RESTful proxy and data model for linked sensor data

A RESTful proxy and data model for linked sensor data

An event abstraction layer for the integration of geosensor data

Analyzing Cognitive Conceptualizations Using Interactive Visual Environments

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