A weighted multi-attribute method for matching user-generated Points of Interest

To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multi-dimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two-step process. First, one has to establish identity relations between information entities across different data sources; and second, attribute values have to be merged according to certain procedures that avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interest (POI) from the Location-based Social Network Foursquare and the local directory service Yelp. While both contain overlapping attributes that can be used for matching, they have specific strengths and weaknesses that make their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.     

Magnetic study of magnetite in the Tagish Lake meteorite

Abstract— The saturation magnetization, saturation remanent magnetization, the coercive, and remanent coercive force were determined at room and liquid nitrogen temperatures for three pieces of the Tagish Lake meteorite. The results are compared to similar data for four other chondrites (Allende, Murray, Orgueil, and Murchison). The data suggests that the Tagish Lake meteorite is magnetically homogeneous, and is not as magnetically hard as the comparison chondrites. The magnetization measurements indicate that it contains about 10–11% multi‐domain magnetite. Magnetic susceptibility measurements on all the samples from 77 K to room temperature showed a Verwey transition for all the samples which contain a significant amount of multi‐domain magnetite. The coercive force data further indicate that the magnetite in Tagish Lake is multi‐domain and that the grain size is small and approximately 4–9 μm.     

Planet formation around M-dwarfs: the moving snow line and super-Earths

Planets result from a series of processes within a circumstellar disk. Evidence comes from the near planar orbits in the Solar System and other planetary systems, observations of newly formed disks around young stars, and debris disks around main-sequence stars. As planet-hunting techniques improve, we approach the ability to detect systems like the Solar System, and place ourselves in context with planetary systems in general. Along the way, new classes of planets with unexpected characteristics are discovered. One of the most recent classes contains super Earth-mass planets orbiting a few AU from low-mass stars. In this contribution, we outline a semi-analytic model for planet formation during the pre-main sequence contraction phase of a low-mass star. As the star contracts, the “snow line”, which separates regions of rocky planet formation from regions of icy planet formation, moves inward. This process enables rapid formation of icy protoplanets that collide and merge into super-Earths before the star reaches the main sequence. The masses and orbits of these super-Earths are consistent with super-Earths detected in recent microlensing experiments.     

Nucleated dwarf elliptical galaxies in the Virgo cluster

Using images from a charge-coupled device survey with the Wide Field Camera on the Isaac Newton Telescope, we performed B - and I -band photometry on 156 Virgo cluster dwarf elliptical (dE) galaxies, 25 candidate new cluster dwarfs, and nine candidate field dwarfs. Galaxies were modelled with Sérsic profiles, using both 1D χ² and 2D cross-correlation methods, with nuclei modelled as point sources. The intensity profiles of 50 galaxies previously classified as dE, dE?, or ? are more accurately fitted if a nucleus is included, and this results in the majority of dwarfs now being classified as nucleated dwarf ellipticals (dE,N). Some faint galaxies with B magnitudes of 18–21 have particularly large relative nuclei, while a small number have apparent central dimmings. For cluster dE,N galaxies the nucleus magnitude is correlated with the magnitude of the host galaxy. The profile parameters of dE and dE,N galaxies are not significantly different, and there is no evident discontinuity in relative nucleus size between non-nucleated and nucleated dwarfs, suggesting that they may form a continuum. Nuclei are on average redder than their underlying galaxies, though a spread of relative colours was found, and two-fifths of nuclei are bluer. Formation mechanisms of nuclei are discussed: at least some appear to have formed in an already existing non-nucleated galaxy, though others may have formed simultaneously with their galaxies and subsequently evolved within them.     

Age constraints on Pleistocene megafauna at Tight Entrance Cave in southwestern Australia

A well-stratified succession of fossiliferous sediments occurs in Tight Entrance Cave, southwestern Australia. These infill deposits contain the remains of megafauna and have accumulated intermittently since the Middle Pleistocene: >137, 137–119 and 50–29 ka, according to the results of ¹⁴C, U–Th, ESR and OSL dating techniques. Megafaunal species richness was highest in the latest part of the penultimate glacial maximum and during the subsequent last interglacial (137–119 ka), but remains are less abundant following an apparent 70 ka depositional hiatus in the sequence. Most megafaunal specimens from the upper (<44 ka) units are fragmentary, and reworking from older strata cannot yet be ruled out. However, one specimen of Simosthenurus occidentalis (a large extinct kangaroo), a pair of articulated dentaries showing no signs of secondary transportation, was found within a sedimentary layer deposited between 48 and 50 ka. This represents one of the youngest demonstrably in situ occurrences of an Australian megafaunal taxon.     

A data-synthesis-driven method for detecting and extracting vague cognitive regions

Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a study published in this journal in 2014, researchers devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundary-drawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from the human-participants study of 2014, identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience.     

Assessing the effectiveness of different visualizations for judgments of positional uncertainty

Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Google’s blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.     

Crowdsensing smart ambient environments and services

Whether it be Smart Cities, Ambient Intelligence, or the Internet of Things, current visions for future urban spaces share a common core, namely the increasing role of distributed sensor networks and the on‐demand integration of their data to power real‐time services and analytics. Some of the greatest hurdles to implementing these visions include security risks, user privacy, scalability, the integration of heterogeneous data, and financial cost. In this work, we propose a crowdsensing mobile‐device platform that empowers citizens to collect and share information about their surrounding environment via embedded sensor technologies. This approach allows a variety of urban areas (e.g., university campuses, shopping malls, city centers, suburbs) to become equipped with a free ad‐hoc sensor network without depending on proprietary instrumentation. We present a framework, namely the GeoTracer application, as a proof‐of‐concept to conduct multiple experiments simulating use‐case scenarios on a university campus. First, we demonstrate that ambient sensors (e.g. temperature, pressure, humidity, magnetism, illuminance, and audio) can help determine a change in environment (e.g. moving from indoors to outdoors, or floor changes inside buildings) more accurately than typical positioning technologies (e.g. global navigation satellite system, Wi‐Fi, etc.). Furthermore, each of these sensors contributes a different amount of data to detecting events. for example, illuminance has the highest information gain when trying to detect changes between indoors and outdoors. Second, we show that through this platform it is possible to detect and differentiate place types on a university campus based on inferences made through ambient sensors. Lastly, we train classifiers to determine the activities that a place can afford at different times (e.g. good for studying or not, basketball courts in use or empty) based on sensor‐driven semantic signatures.     

A model for assessing iceberg hazard

Natural Hazards - With the polar regions opening up to more marine activities but iceberg numbers more likely to increase than decline as a result of global warming, the risk from icebergs to...     

Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change

Land use and management activities have a substantial impact on carbon stocks and associated greenhouse gas emissions and removals. However, it is challenging to discriminate between anthropogenic and non-anthropogenic sources and sinks from land. To address this problem, the Intergovernmental Panel on Climate Change developed a managed land proxy to determine which lands are contributing anthropogenic greenhouse gas emissions and removals. Governments report all emissions and removals from managed land to the United Nations Framework Convention on Climate Change based on this proxy, and policy interventions to reduce emissions from land use are expected to focus on managed lands. Our objective was to review the use of the managed land proxy, and summarize the criteria that governments have applied to classify land as managed and unmanaged. We found that the large majority of governments are not reporting on their application of the managed land proxy. Among the governments that do provide information, most have assigned all area in specific land uses as managed, while designating all remaining lands as unmanaged. This designation as managed land is intuitive for croplands and settlements, which would not exist without management interventions, but a portion of forest land, grassland, and wetlands may not be managed in a country. Consequently, Brazil, Canada and the United States have taken the concept further and delineated managed and unmanaged forest land, grassland and wetlands, using additional criteria such as functional use of the land and accessibility of the land to anthropogenic activity. The managed land proxy is imperfect because reported emissions from any area can include non-anthropogenic sources, such as natural disturbances. However, the managed land proxy does make reporting of GHG emissions and removals from land use more tractable and comparable by excluding fluxes from areas that are not directly influenced by anthropogenic activity. Moreover, application of the managed land proxy can be improved by incorporating additional criteria that allow for further discrimination between managed and unmanaged land.