On the estimates of the ring current injection and decay

In the context of the space weather predictions, forecasting ring current strength (and of the Dst index) based on the solar wind upstream conditions is of specific interest for predicting the occurrence of geomagnetic storms. In the present paper, we have studied separately its two components: the Dst injection and decay. In particular, we have verified the validity of the Burton's equation for estimating the ring current energy balance using the equatorial electric merging field instead of the original parameter V Bs (V is the solar wind speed and Bs is the southward component of the Interplanetary Magnetic Field, IMF). Then, based on this equation, we have used the phasespace method to determine the best-fit approximations for the ring current injection and decay as functions of the equatorial merging electric field (Em). Results indicate that the interplanetary injection is statistically higher than in previous estimations using V Bs . Specifically, weak but not-null ring current injection can be observed even during northward IMF, when previous studies considered it to be always zero. Moreover, results about the ring current decay indicate that the rate of Dst decay is faster than its predictions derived by using V Bs . In addition, smaller quiet time ring current and solar wind pressure corrections are contributing to Dst estimates obtained by Em instead of V Bs . These effects are compensated, so that the statistical Dst predictions using the equatorial electric merging field or using V Bs are about equivalent

Linking geographic vocabularies through WordNet

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall

Conceptualising the geographic world: the dimensions of negotiation in crowdsourced cartography

In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest, and lake, enabling the communi- cation of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. De- spite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often in- commensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the nego- tiation: (i) ontology, (ii) cartography, (iii) culture and language, (iv) lexical definitions, (v) granularity, and (vi) semantic overload and duplication

An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity

Finding and sharing GIS methods based on the questions they answer

Geographic information has become central for data scientists of many disciplines to put their analyses into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests

Mentalizing under Uncertainty: Dissociated Neural Responses to Ambiguous and Unambiguous Mental State Inferences

The ability to read the minds of others (i.e., to mentalize) requires that perceivers understand a wide range of different kinds of mental states, including not only others’ beliefs and knowledge but also their feelings, desires, and preferences. Moreover, although such inferences may occasionally rely on observable features of a situation, perceivers more typically mentalize under conditions of “uncertainty,” in which they must generate plausible hypotheses about a target's mental state from ambiguous or otherwise underspecified information. Here, we use functional neuroimaging to dissociate the neural bases of these 2 distinct social–cognitive challenges: 1) mentalizing about different types of mental states (beliefs vs. preferences) and 2) mentalizing under conditions of varying ambiguity. Although these 2 aspects of mentalizing have typically been confounded in earlier research, we observed a double dissociation between the brain regions sensitive to type of mental state and ambiguity. Whereas ventral and dorsal aspects of medial prefrontal cortex responded more during ambiguous than unambiguous inferences regardless of the type of mental state, the right temporoparietal junction was sensitive to the distinction between beliefs and preferences irrespective of certainty. These results underscore the emerging consensus that, rather than comprising a single mental operation, social cognition makes flexible use of different processes as a function of the particular demands of the social context