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Buttenfield (1988) pioneered research on multiple representations in the dawn of GIScience. Her efforts evoked inquiries into fundamental issues arising from the selective abstractions of infinite geographic complexity in spatial databases, cartography, and application needs for varied geographic details. These fundamental issues posed ontological challenges (e.g. entity classification) and implementational complications (e.g. duplication and inconsistency) in geographic information systems (GIS). Expanding upon Buttenfield's line of research over the last three decades, this study reviewed multiple representations in spatial databases, spatial cognition, and deep learning. Initially perceived as a hindrance to GIS, multiple representations were found to offer new perspectives to encode and decipher geographic complexity. This paper commenced by acknowledging Buttenfield's pivotal contributions to multiple representations in GIScience. Subsequent discussions synthesized the literature to outline cognitive representations of space in the brain's hippocampal formation and feature representations in deep learning. By cross-referencing related concepts of multiple representations in GIScience, the brain's spatial cells, and machine learning algorithms, this review concluded that multiple representations facilitate learning geography for both humans and machines. [ABSTRACT FROM AUTHOR]

Concepts of scale are at the heart of diverse scientific endeavors that seek to understand processes and how observations and analyses influence our understanding. While disciplinary discretions exist, researchers commonly devise spatial, temporal, and organizational scales in scoping phenomena of interest and determining measurements and representational frameworks in research design. The rise of the Fourth Paradigm for science drives data-intensive computing without preconceived notions regarding at what scale the phenomena or processes of interest operate, or at which level of details meaningful patterns may emerge. While scale is the a priori consideration for theory-driven research to seek ontological and relational affirmations, big data analytics and machine learning embed scale in algorithms and model outputs. In this paper, we examine embedded scale in data-driven machine learning research, connect the embedding scale to scale operating in the general theory of geographic representation in GIS and scaffold our arguments with a study of using machine learning to detect archeological features in drone-collected high-density images. [ABSTRACT FROM AUTHOR]

Gravity models, popular for estimating spatial interactions, assume independent spatial interactions from locations to all other locations and, therefore, overlook potential latent multilateral influences in play. This research combines self‐organizing map (SOM) and trajectory analysis to classify patterns of spatial interactions and trace changes in the patterns simultaneously across all locations and decipher the development and dependence of multilateral interactions in space and time. We use international trading among 207 countries from 1900 to 2014 to demonstrate the proposed SOM–trajectory analysis. We create SOMs of annual international trade for all countries, trace each country's changes in trading patterns over 115 years, and compare the trajectories for countries. The combined SOM–trajectory analysis elicits the hidden trading development and geographic influences brought into play. The proposed method is adaptable to other domains of spatial interactions (e.g., immigration) to characterize the temporal patterns of multilateral spatial interactions at locations individually or contextually. [ABSTRACT FROM AUTHOR]

GIS researchers, too, recognized the importance of micro-environments and individual behaviours and strengthened temporal GIS with time geography to address accessibility, mobility, and environmental exposure. If geography is essential to GIS, we need to embed geographic conceptualization, abstraction, and representation in GIS knowledge production to enforce a sound geographic foundation innate to GIS research. GIS researchers should continue to ponder how GIS epistemology and beliefs in methods with extended data sources have contributed to addressing different types of geographic problems, which should also serve as the guide to GIS education. The tension drove new developments in GIS and society, public participation in GIS, critical GIS, qualitative GIS, volunteered geographic information, GIS as a media, humanities GIS, and geographic imagination systems. [Extracted from the article]

The rise of Geographic Information Systems (GIS) technologies has been producing powerful tools for spatial data processing, management, analysis, modeling, and visualization. While supporting many tasks, GIS technologies have evoked new thinking and advanced intellectual inquiries in geography. Technological advances in other fields often stimulate new research questions and lead to revolutionary discoveries. The Hubble telescope revolutionizes our understanding of the universe, and 3D digital microscopes transform our knowledge of the coordination among biological, neurological, and physiological systems in living organisms. Can GIS claim similar revolutionary effects on geography? The answer is much up for debate. With GIS technologies, geographic studies can explore a broader extent across multiple scales in space and time and tackle problems through increasingly complex spatial statistics, visual analytics, computation, simulation, machine learning, and artificial intelligence. Both the Hubble telescope and 3D digital microscope were built based on scientific research that subserves the technological advances for inquiries into space and life systems. Likewise, GIScience research innovates GIS methods that enable novel geographic investigations and therefore contribute to geographic knowledge production. For the sake of simplicity, the term GIS used here represents both GIScience and GIS technologies. This essay attempts to clarify the intellectual contributions of GIS to geography on the following two questions: (1) What novel geographic thinking is driven by GIS? (2) How may GIS provoke new geographic inquiries and knowledge? Building on Nystuen's notion of four tensions that trigger geographic questions, the essay discusses how GIS innovations mediate historical tension, space‐time tension, dimensional tension and scale tensions. [ABSTRACT FROM AUTHOR]

The United Nations General Assembly adopted the 2030 Agenda for sustainable development goal in 2015 for a prosperous and inclusive future for all. Reflecting on the contributions of IJGIS publications to sustainability, this editorial draws the intellectual relevance of GIScience to the UN sustainable development goals (SDGs). GIScience, with its body of GIScience knowledge and knowledge production, has much to contribute to addressing the shortfalls recognized by the 2019 UN High-Level Political Forum on Sustainable Development. The COVID-19 pandemic threatens sustainability in many ways, but it also signifies the decreases of environmental pollutants by reducing human activities and elicits the latent social and health inequalities. This editorial highlights opportunities for GIScience contributions to the UN SDGs, outlines research challenges of the UN SDGs to GIScience, and welcomes submissions of GIScience research on UN SDGs to IJGIS. [ABSTRACT FROM AUTHOR]

The contrast of space and place has long been an active topic of scholarly discussions in many disciplines. While spatial analysis enjoys a multitude of quantitative methods, the study of place remains mostly conceptual and descriptive. This paper expands upon the rich concepts of place in the literature to propose a quantitative framework for placial analysis based on events. Central to the proposed framework are three assumptions: (1) human experiences transform space to place; (2) events build human experiences in space; and (3) places emerge organically and may change characters, spatial extent and location over time through the shifts in occurrences and types of events in space and time. The proposed framework consists of three elements: clustering events, decomposing event distributions, and identifying the similarity of event clusters. We applied the framework to identify criminological places in the City of Dallas in the United States and the changes of these places from 1 June 2014 to 30 May 2018. [ABSTRACT FROM AUTHOR]

We propose a novel approach to network-based event likelihood modeling that estimates the probabilities of event occurrences on a network and identifies the influences of site and situation characteristics. Our premise is that the occurrences of events that involve human activities are subject to site and situational characteristics, and an understanding of event occurrences serves the basis for preparation or mitigation. Using data from Dallas, Texas, USA, we take the proposed approach to estimate the likelihood of traffic accidents based on binary (event or nonevent) space-time atoms of 100-m road segments and 1-h intervals. We choose 12 variables representing time, site characteristics, and situational conditions based on literature reviews to develop logistic regression and random forest models. The traffic accident data on even days were used for model construction and data on odd days for model testing. Both models result in comparable accuracy at 84.11% (logistic regression) and 85.42% (random forest) with significant differences in the spatial patterns of how site and situation correlate to traffic accidents. The difference signals the dynamic influence of site and situation characteristics on the event likelihood over time. The proposed approach shall be applicable to other point events on a network. [ABSTRACT FROM AUTHOR]

In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R2 value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development. [ABSTRACT FROM AUTHOR]

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings. [ABSTRACT FROM AUTHOR]

Abstract: This article highlights the key intellectual development in human dynamics research, examines the modeling emphases in publications, and argues for research directions in need. Human dynamics research is discussed in two broad directions: spacing time and timing space, to model human activities and interactions. Time is essential to human dynamics research. Space, while often being overlooked, in complement with time is critical to understanding human dynamics because knowing where activities take place is essential to knowing how and why people act and interact. Some interactions allow remote or asynchronized participations, and others require movement to collocate individuals for participating in synchronized activities. A spacing time approach examines the temporal gaps between interactions. A timing space approach investigates the spatial pulses between interactions. Primary research in the spacing time of human dynamics established queueing theories to explain the bursts and heavy‐tailed distribution of human interactions. Although research on the timing space of human dynamics enjoys growing popularity with data from geo‐tagged social media and location‐aware social internet of things (SIoT), its publications remain mostly exploratory. This article suggests a hierarchical framework to systematically study human dynamics and relate findings to build the body of knowledge about human dynamics. [ABSTRACT FROM AUTHOR]

TheInternational Journal of Geographic Information Science (IJGIS), established in 1987, is the first academic journal devoted solely to Geographical Information Science (GIS) research. This editorial highlights milestones of the journal development and its influences on the field.IJGISresearch articles and special issues have been effective in publishing the state of the art and emerging research accomplishments. In light of the changing landscape of GIS,IJGISwelcome papers on meta-analysis studies, literature reviews, and research foresight. This editorial outlines the underlying thinking and expectations for these papers in future volumes.IJGISaspires to publish research of high novelty and broad interest that pushes the boundary of fundamental and applied GIS. As an independent, multidisciplinary journal driven by the community of authors, reviewers, and readers, community support is key to realizing the aspiration of a major influence on GIS research. [ABSTRACT FROM PUBLISHER]

Nuweiba sits on a flood plain between the Sinai Mountain and the Gulf of Aqaba. Flash floods pose significant threats to socio-economic development in the region. This study assessed flash floods risk in the greater Nuweiba area in support of mitigation planning. Our approach evaluated topographic, hydrological, and geological factors holistically to rank areas of high, median, and low flash floods risk along the Wadi Watir. Data from field surveys, Enhanced Thematic Mapper (ETM+) imagery, and Shuttle Radar Terrain Model (SRTM) were analyzed through digital image processing and spatial analysis to characterize lithology, soil types, land cover, and hydrological morphometrics. The morphometric parameters were used to evaluate the most sensitive sub-basins for flooding and create the flash flood susceptibility map. Soil Conservation Service (SCS) Rainfall-Runoff model was used to estimate hydrological responses at ungauged catchments, including runoff depth, lag time, and peak discharge. We spatially integrated all the risk factors and created a flash floods risk map as the practical geospatial assessment of flash flood risk in the greater Nuweiba area. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses reports in the issue on topics including professor David M. Mark's scholarship, his contributions to ethnophysiography research, and landforms.

This paper discusses philosophical and methodological considerations of space–time representation and analytics. Central to our premise is that how we conceptualize space and time has profound influence on the way in which we represent and analyse spatiotemporal data. Four approaches in space–time representation are discussed based on two sets of space–time dichotomies: absolutism versus relationalism and realism versus idealism. We elaborate on the philosophical inquiries related to the dichotomies and discuss approaches taken in GIScience research. Since most GIS studies to date align well with the absolutism–realism approach to space–time representation, we discuss examples that adopt absolutism–idealism, relationalism–realism, and relationalism–idealism approaches. We argue that multiple perspectives of space and time can bring rich insights into understanding geographic phenomena. All four approaches of space–time representation and ensuing analytical methods should be broadly explored in GIScience research. [ABSTRACT FROM AUTHOR]

Development of ecosystem-based fisheries management models depends, to a large extent, on the availability of trophic interaction data. These models could address ecological questions, examine ecosystem trophic structure, and be used in placement analysis for marine protected areas, among other uses. Many studies on fish trophic interactions have been conducted in the Gulf of Mexico over the past 120 yrs, and we are currently compiling data from these studies into a database. Here, we report on a collection of 747 references, investigate spatial and taxonomic distributions of the fish species examined, and identify data gaps. Metadata lite, a condensed version of customary metadata that answers the who, what, where, when, and why, has been collected on all studies, all references have been geocoded, and habitat characterizations have been standardized using the Coastal and Marine Ecological Classification Standard for about 60% of the references. Visualization tools and products to assist in the synthesis, analysis, and interpretation of the data, including maps, network depictions, and dynamic interactions, are discussed. A survey of trophic data available for fisheries management models for managed species in US Gulf waters appear to be adequate for at least 23 of 50 of managed species, while data for at least 14 species remain insufficient for model development assessment. [ABSTRACT FROM AUTHOR]

This research develops a new temporal Geographic Information System (GIS) framework and applies it to compare General Circulation Model (GCM) products and reanalysis datasets in order to discern differences in patterns and locations of change as a proof of concept. The proposed framework incorporates the concept of kinematics to represent the movement of isolines as advection displacement vectors. Comparison of displacement vectors from the four datasets shows differences in isotherm shifts for both median values and interquartile ranges along with several prominent, well defined difference regions across the globe. Among the four datasets used in the study, the two reanalysis datasets have the smallest median difference in displacement vectors indicating the least spatiotemporal difference. As both reanalysis datasets are based on actual observations, this result validates the displacement vector representation. The CNRM CM3 20C3M dataset contains an Antarctic Cooling that led to most of the differences in displacement vectors against the other datasets. The research shows the effectiveness of GIS enabled displacement vectors analysis to elicit spatial differences in climate patterns among GCM data and reanalysis data. While temperature data were used in the case study, the proposed method is based on concepts applicable to other isolines of geographic variables (such as isobars or isohyets). [ABSTRACT FROM AUTHOR]

Central to temporal Geographical Information systems (GIS) research is representation and analysis of temporality in spatial databases. Temporality can be characterized by change, motion, and periodicity, but to date, most temporal GIS research advances contribute to representing and analyzing spatially discrete constructs of amandant vectors, geospatial lifelines, space-time trajectories, events and network flows, none of which accounts for geographic phenomena that are continuously dispersing or propagating in space and time. This article introduces a temporal GIS framework that adopts concepts of fluid kinematics and uses velocity as the basic unit of spatiotemporal representation to identify kinematic flows and features indicative of geographic processes, such as divergence and convergence. Climate projections of IPCC A2 scenario are used to test the proposed kinematic representation by comparing data outputs from two popular General Circulation Models (GCMs): the Center National Weather Research global ocean-atmosphere-coupled system (CNRM-CM3) in France and the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM3) in the United States. The case study shows that fluid kinematics approach offers an alternative means to time-stamped, change-based or event-based representations of geographic phenomena and provides a new foundation for GIS analysis of space-time fields. Specifically, the proposed GIS framework enables direct manipulation of velocity to reveal flows. The case study identifies distinct regions of rapid isotherm movement. By comparison, both GCMs identify similar regions of convergence and divergence except for the following: a high-latitude region in southern Greenland, a band of regions at -15° latitude extending from the middle of South America to east of Madagascar, a region over the Arabian Sea, a region over Indonesia, and several regions in the tropical oceans. [ABSTRACT FROM AUTHOR]

Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo-dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object-oriented data modelling. Geographic dynamics are synthesized in a three-dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation. [ABSTRACT FROM AUTHOR]

The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of geographic phenomena. Much of the new data includes regular snapshots of the environment. Comparison of these snapshots can provide information about changes to the phenomena of interest. However, conventional GIS data models and analytical tools lack capabilities to adequately handle massive multidimensional data. One of the fundamental tools necessary to meet such challenges is query support to retrieve and summarize data according to dynamic geographic phenomena, such as geographic events and processes, of interest. Such query support depends upon abilities to assess spatiotemporal similarity so that data representing geographic events that exhibit the spatiotemporal characteristics of interest can be identified in a GIS database. To this end, this paper introduces a method to assess similarity of geographic events and processes (such as storms) based on their spatiotemporal characteristics (such as distribution of precipitation). We developed six indices to capture static and dynamic characteristics of geographic events and applied the Dynamic Time Warping method to temporal sequences of the six indices to examine the similarity among these events. With a case study, we demonstrated the proposed indices and method capable of comparing spatiotemporal characteristics of events as recorded in a GIS database and categorizing spatiotemporal data into groups of events according to their behavior in space and time. [ABSTRACT FROM AUTHOR]

Evaluates six hypotheses for diversity gradients with 49 New World ant communities, from tundra to rain forest. Goal to understand whether and how processes that generate the gradients of species richness act at different spatial scales; Coaction of two factors linked to high net rates of diversification.

Remote sensing (RS) and geographic information systems (GIS) techniques are applied to high-resolution satellite imagery to determine characteristics of tornado damage from the 3 May 1999 tornado outbreak. Three remote sensing methods, including principal components analysis, normalized difference vegetation index (NDVI) analysis, and NDVI change analysis, elicit tornado damage paths at different levels of detail on the 23.5-m-resolution images captured by the Linear Imaging Self-Scanning III (LISS-3) sensor on the Indian Remote Sensing (IRS) satellite before and after the outbreak. Remote sensing results were spatially overlaid on F-scale contours compiled by the members of Oklahoma Weather Center. Spatial overlays reveal that results from the principal components analysis correlate well with F3 or greater damage. NDVI analysis shows signatures expanding to F2 damage, and NDVI change analysis is capable of detecting F1 damage in some instances. In general, results of these analyses correspond to more severe damage in rural areas than in urban areas. Comparison with detailed ground surveys shows that the spectral signatures of tornado damage are related to vegetation damage and large debris fields. Variations in spectral signatures with Fujita tornado damage intensity suggest that land cover characteristics may be just as important as tornado damage intensity in creating a track detectable by satellite. It is concluded that RS and GIS techniques on IRS LISS-3 imagery (an example of multispectral satellite imagery) can be useful in assessing tornado damage, particularly for extensive and intense events. [ABSTRACT FROM AUTHOR]

As the development of Geographic Information Systems (GIS) proceeds to advanced scientific and societal applications, there is an emerging need to enhance GIS support for complex spatiotemporal queries. Dynamic GIS representations (as opposed to static, map-based representations) that can integrate proper data elements in the production of geographic information are required. This paper demonstrates the use of a three-domain representation that facilitates compilation of higher-level information (such as frequency and rate) from preliminary data records (such as time and location) stored in a database. The three-domain representation is compared with snapshot, space-time composite, and spatiotemporal object models using a sample data set for forest transitions. While the three-domain representation is a normalization of these data models, it offers a conceptual alternative that enables GIS to represent spatiotemporal behaviors of geographic entities, in addition to entities as well as histories at locations as emphasized in most GIS data models. The comparison shows that the three-domain representation has combined the strengths of the space-time composite and spatiotemporal object models. Moreover, it enables aggregations of analytical use along with dynamic mappings between geographic concepts and locations, a distinct capability that takes GIS query processing beyond the level of information support offered by static map-based data models. [ABSTRACT FROM AUTHOR]

Traditional Geographic Information Systems (GIS) represent the environment under reductionist thinking, which disaggregates a geographic environment into independent geographic themes. The reductionist approach makes the spatiotemporal characteristics of geo-features explicit, but neglects the holistic nature of the environment, such as the hierarchical structure and interactions among environmental elements. To fill this gap, we integrate the concept geographic scenario with the fundamental principles of General System Theory to realize the environmental complexity in GIS. With the integration, a geographic scenario constitutes a hierarchy of spatiotemporal frameworks for organizing environmental elements and subserving the exploration of their relationships. Furthermore, we propose geo-characterization with ontological commitments to both static and dynamic properties of a geographic scenario and prescribe spatial, temporal, semantic, interactive, and causal relationships among environmental elements. We have tested the utility of the proposed representation in OWL and the associated reasoning process in Semantic Web Rule Language (SWRL) rules in a case study in Nanjing, China. The case study represents Nanjing and the Nanjing presidential palace to demonstrate the connections among environmental elements in different scenarios and the support for information queries, evolution process simulation, and semantic inferences. The proposed representation encodes geographic knowledge of the environment, makes the interactions among environmental elements explicit, supports geographic process simulation, opens opportunities for deep knowledge mining, and grounds a foundation for GeoAI to discover geographic complexity and dynamics beyond the support of conventional theme-centric inquiries in GIS. [ABSTRACT FROM AUTHOR]

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs. [ABSTRACT FROM AUTHOR]

Once associating another person with an unpleasant smell, how do we perceive and judge this person from that moment on? Here, we used aversive olfactory conditioning followed by a social attribution task during functional magnetic resonance imaging to address this question. After conditioning, where one of two faces was repeatedly paired with an aversive smell, the participants reported negative affect when viewing the smell-conditioned but not the neutral face. When subsequently confronted with the smell-conditioned face (without any smell), the participants tended to judge both positive and negative behaviors as indicative of personality traits rather than related to the situation. This effect was predicted by the degree of the preceding olfactory evaluative conditioning. Whole brain analysis of stimulus by stage interaction indicated differential activation of the ventromedial prefrontal cortex and right angular gyrus to the conditioned versus the neutral person during the attribution phase only. These results suggest that negative smell associations do not simply induce a negative perception of the target person but rather bias the attribution style towards trait attributions. The fact that this bias was evident regardless of behavior valence suggests it may reflect enhanced psychological distance. Thus, the known observation of social rejection triggered by aversive smell may be driven by a shift in social attribution style. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an ontology-enabled framework for a geospatial problem-solving environment (geospatial PSE) that allows collaboration among Web-service providers, domain experts, and solution seekers to semantically discover and use geographic information services (GI services) to solve a target class of geospatial problems. The framework contains (1) ontologies that provide knowledge bases for domain experts to formalize geospatial semantics and conceptually model workflows of geospatial problems for semantic references and inferences, (2) an ontology-enabled catalog (ONTOCAT) that provides an interface for Web-service providers to annotate GI services using formal semantics for semantic discoveries, (3) a Web portal that supplies an interface for solution seekers to submit geospatial problems and evaluate the discovered GI services, and (4) an ontology engine that parses the problems and performs semantic inferences to discover the required GI services based on annotated semantics and conceptualized processes. To validate the applicability of the framework, a prototype was implemented using an earthquake as an example. This paper concludes by not only highlighting the framework, which may lay a foundation for fully integrating ontologies and GI services to solve geospatial problems, but also identifying future directions for the geospatial PSE. [Copyright &y& Elsevier]

Abstract: We use evidence from a quasi-experiment – the shipping of radioactive spent nuclear fuel by train through South Carolina – to assess whether many years of incident-free transport of nuclear waste no longer negatively affects market valuation of properties along the route. Using Charleston County (SC) property sales data over 13 years we find, to the contrary, that the negative impact of the nuclear waste shipments on property values continues to be felt over the long run. The perception of risk from nuclear waste transport appears to be resilient. We contribute methodologically by comparing well-defined treatment and control groups of properties to estimate the average treatment effect of the nuclear waste shipment program. The results are affirmed in both a pooled cross-section sample, as well as a panel data sample of repeated property sales. [Copyright &y& Elsevier]

Contemporary GIS can handle static spatial data for querying and visual representation, but the temporal dimension remains a challenge. This paper addresses the need for a dynamic GIS capable of managing complex data types. The design relies on a representation of the theoretical spatiotemporal primitive known as the 'geo-atom'. This paper proposes a novel and implemented data structure called the space time point (STP) built on this theory. With the STP representation, spatiotemporal data queries can be posed to return useful results about dynamic geographic phenomena and their interaction. Two key challenges addressed in this research are (1) data structures to represent hybrid (object and field) spatiotemporal phenomena and (2) the design of a dynamic GIS interface. These challenges are addressed by the implementation of the system, referred to as 'Extended Dynamic GIS (EDGIS)', that uses the proposed STPs. The EDGIS system is described from theory to its implementation in Java™ and a series of application examples are described followed by performance metrics. The paper concludes with a discussion of areas for further research such as integration of the system with geo-sensor networks, hazards, transportation, and location-based services (LBS). [ABSTRACT FROM AUTHOR]

Sodium (Na) is uncommon in plants but essential to the metabolism of plant consumers, both decomposers and herbivores. One consequence, previously unexplored, is that as Na supplies decrease (e.g., from coastal to inland forests), ecosystem carbon should accumulate as detritus. Here, we show that adding NaCI solution to the leaf litter of an inland Amazon forest enhanced mass loss by 41%, decreased lignin concentrations by 7%, and enhanced decomposition of pure cellulose by up to 50%, compared with stream water alone. These effects emerged after 13-18 days. Termites, a common decomposer, increased 7-fold on +NaCl plots, suggesting an agent for the litter loss. Ants, a common predator, increased 2-fold, suggesting that NaCI effects cascade upward through the food web. Sodium, not chloride, was likely the driver of these patterns for two reasons: two compounds of Na (NaCI and NaPO4) resulted in equivalent cellulose loss, and ants in choice experiments underused Cl (as KCI, MgCI2, and Cad2) relative to NaCI and three other Na compounds (NaNO3, Na3PO4, and Na2SO4). We provide experimental evidence that Na shortage slows the carbon cycle. Because 80% of global landmass lies >100 km inland, carbon stocks and consumer activity may frequently be regulated via Na limitation. [ABSTRACT FROM AUTHOR]

Purpose To investigate the relationship between age-related macular degeneration (AMD) and future development of Alzheimer's disease (AD) or senile dementia. Design A longitudinal case-control study using the Taiwan National Health Insurance Research Database. Participants From 2001 to 2009, the newly diagnosed AMD cases aged ≥65 years in the database were recruited as the AMD cohort (n = 4993). Of those, there were 540 with and 4453 without exudative AMD diagnoses. Subjects without any AMD, matched for age, gender, and time of enrollment, were randomly sampled as the control cohort (n = 24 965) for comparison. Methods Alzheimer's disease/senile dementia-free survival analysis was assessed using a Kaplan–Meier method. Cox proportional hazard regressions were performed to calculate the hazard ratios (HR) of AD or senile dementia for the 2 cohorts after adjusting for preexisting comorbidities and number of clinical visits. Main Outcome Measures The first-ever diagnosis of AD or senile dementia during the observation period. Results Of the 29 958 sampled subjects, 1589 (5.3%) were diagnosed with AD or senile dementia during a mean follow-up period of 4.4 years, including 294 (5.9%) from the AMD cohort and 1295 (5.2%) from the control cohort. The incidence of AD or senile dementia was higher in patients with AMD than in the controls ( P = 0.044), with an HR of 1.44 (95% confidence interval [CI], 1.26–1.64) after adjusting for covariates. The stratified analysis showed that the adjusted HR for AD or senile dementia was 1.35 (95% CI, 0.89–2.06) for exudative AMD versus the controls and 1.44 (95% CI, 1.26–1.65) for nonexudative AMD versus the controls. Conclusions This study provides large-scale, population-based evidence that AMD, especially nonexudative AMD, is independently associated with an increased risk of subsequent AD or senile dementia development. [ABSTRACT FROM AUTHOR]