Space-time Locations Research Articles

Assessing Public Participation for the United Arab Emirates E-Government

Geographic and geospatial information processing represent vital human functions for locating phenomena and navigating in the world around us. Spatial representations as mental and physical maps tie perception to cognitive processes. As a system, digital information and communication technologies (ICT) connect satellites to global positioning systems (GPS) to allow accurate, real-time simulations and representations of space–time locations. Now embedded in a globally linked network of digital devices, these systems offer interactive space–time communication of qualitative (visual) and quantitative (locational coordinates) information. Our global information societies, digital economies, social media, governance, commerce, and industry exist because of these processes and are dependent on them. With potential for active user participation in GIS embedded in billions of digital devices, the primary questions of public participation GIS (PPGIS) investigate the means by which government, commerce, and civil s...

Multimodel framework for characterization of transport in porous media

AbstractWe consider modeling approaches to characterize solute transport in porous media, integrating them into a unique theoretical and experimental framework for model evaluation and data interpretation. To date, development of (conservative and reactive chemical) transport models and formulation of model calibration methods grounded on sensitivity‐based collection of measurements have been pursued in parallel. Key questions that remain include: For a given set of measurements, which conceptual picture of the transport processes, as embodied in a mathematical model or models, is most appropriate? What are the most valuable space‐time locations for solute concentration measurements, depending on the model selected? How is model parameter uncertainty propagated to model output, and how does this propagation affect model calibration? We address these questions by merging parallel streams of research—model formulation, reduction, calibration, sensitivity analysis, and discrimination—offering our view on an emerging framework that guides (i) selection of an appropriate number and location of time‐dependent concentration measurements given a transport model and (ii) assessment (through discrimination criteria) of the relative benefit of applying any particular model from a set of several models. Our strategy is to employ metrics to quantify the relative contribution of each uncertain model parameter to the variability of the model output. We evaluate these metrics through construction of a surrogate (or “meta”) transport model that has the additional benefit of enabling sensitivity analysis and model calibration at a highly reduced computational cost. We demonstrate the applicability of this framework, focusing on transport of reactive chemicals in laboratory‐scale porous media.

Nonstationary matrix covariances: compact support, long range dependence and quasi-arithmetic constructions

Flexible models for multivariate processes are increasingly important for datasets in the geophysical, environmental, economics and health sciences. Modern datasets involve numerous variables observed at large numbers of space–time locations, with millions of data points being common. We develop a suite of stochastic models for nonstationary multivariate processes. The constructions break into three basic categories—quasi-arithmetic, locally stationary covariances with compact support, and locally stationary covariances with possible long-range dependence. All derived models are nonstationary, and we illustrate the flexibility of select choices through simulation.

Reliable Space–Time Prisms Under Travel Time Uncertainty

Time geography is a powerful framework for analyzing human activities under various space–time constraints. At the core of time geography is the concept of the space–time prism, which delimits an individual's potential activity locations in space and time. The classical space–time prism, however, admits only deterministic travel speeds and ignores the stochastic nature of travel environments. In this article, the classical space–time prism model is extended to congested road networks with travel time uncertainty. A reliable space–time prism is proposed to consider explicitly an individual's on-time arrival probability concerns in the face of travel time uncertainty. The reliable space–time prism is defined as the set of space–time locations where an individual can participate in an activity and return to his or her destination with a given on-time arrival probability. To construct such a reliable space–time prism in a road network, a solution algorithm is developed. A case study using real-world traffic information is carried out to demonstrate the applicability of the proposed prism model. The results of the case study indicate that the proposed prism model can represent well individuals’ space–time taking into account various on-time arrival probability concerns.

Comparative analysis of formulations for conservative transport in porous media through sensitivity-based parameter calibration

[1] We apply a general strategy based on Global Sensitivity Analysis (GSA) and model discrimination criteria to (a) calibrate the parameters embedded in competing models employed to interpret laboratory-scale tracer experiments, (b) rank these models, and (c) estimate the relative degree of likelihood of each model through a posterior probability weight. We consider a conservative transport experiment in a uniform porous medium. We apply GSA to three transport models, based on: the classical advection-dispersion equation (ADE), a dual-porosity (DP) formulation with mass transfer between mobile and immobile regions, and the Continuous Time Random Walk (CTRW) approach. GSA is performed through Polynomial Chaos Expansion of the governing equations, treating key model parameters as independent random variables. We show how this approach allows identification of (a) the relative importance of model-dependent parameters, and (b) the space-time locations, where the models are most sensitive to these parameters. GSA is then employed to assist parameter estimates within a Maximum Likelihood framework. Finally, formal model identification criteria are employed to (a) rank the alternative models, and (b) associate each model with a posterior probability weight for the specific case study. The GSA-based calibration of each model returns an acceptable approximation (remarkably accurate in the case of the CTRW model) of all available concentration data, with calibration being performed using minimum sets of observations corresponding to the most sensitive (space-time) locations.

On the identification of Dragon Kings among extreme-valued outliers

Abstract. Extreme values of earth, environmental, ecological, physical, biological, financial and other variables often form outliers to heavy tails of empirical frequency distributions. Quite commonly such tails are approximated by stretched exponential, log-normal or power functions. Recently there has been an interest in distinguishing between extreme-valued outliers that belong to the parent population of most data in a sample and those that do not. The first type, called Gray Swans by Nassim Nicholas Taleb (often confused in the literature with Taleb's totally unknowable Black Swans), is drawn from a known distribution of the tails which can thus be extrapolated beyond the range of sampled values. However, the magnitudes and/or space–time locations of unsampled Gray Swans cannot be foretold. The second type of extreme-valued outliers, termed Dragon Kings by Didier Sornette, may in his view be sometimes predicted based on how other data in the sample behave. This intriguing prospect has recently motivated some authors to propose statistical tests capable of identifying Dragon Kings in a given random sample. Here we apply three such tests to log air permeability data measured on the faces of a Berea sandstone block and to synthetic data generated in a manner statistically consistent with these measurements. We interpret the measurements to be, and generate synthetic data that are, samples from α-stable sub-Gaussian random fields subordinated to truncated fractional Gaussian noise (tfGn). All these data have frequency distributions characterized by power-law tails with extreme-valued outliers about the tail edges.

Earthquake swarms in the Kamchatka subduction zone and estimation of possible tsunami generation locations

An analysis of the space-time locations of earthquake swarms in the Kamchatka subduction zone showed that the source zones of these earthquake swarms, as well as of the epicenters of most tsunami-generating earthquakes, are confined to the seamounts in the barrier ridge between Kamchatka and the deep-sea trench. The “dot clouds” of hypocenters of practically all earthquake swarms dip toward the trench on seismic sections that are oriented across the subduction zone trend; this fits the auxiliary focal solution of tsunami-generating earthquakes as was first noticed by L.M. Balakina and is in agreement with the model experiment carried out by L.I. Lobkovskii et al. We discuss a likely scenario for the generation of reverse-thrust blocks whose movements are accompanied by earthquake swarms and by tsunami-generating earthquakes. We estimate the locations of the most probable tsunami generation.

A pattern theorem for random sorting networks

A sorting network is a shortest path from $12\cdots n$ to $n\cdots 21$ in the Cayley graph of the symmetric group $S_n$ generated by nearest-neighbor swaps. A pattern is a sequence of swaps that forms an initial segment of some sorting network. We prove that in a uniformly random $n$-element sorting network, any fixed pattern occurs in at least $c n^2$ disjoint space-time locations, with probability tending to $1$ exponentially fast as $n\to\infty$. Here $c$ is a positive constant which depends on the choice of pattern. As a consequence, the probability that the uniformly random sorting network is geometrically realizable tends to $0$.

Fine structure constant variation or spacetime anisotropy?

Recent observations on the quasar absorption spectra supply evidence for the variation of the fine structure constant α. In this paper, we propose another interpretation of the observational data on the quasar absorption spectra: a scenario with spacetime inhomogeneity and anisotropy. Maybe the spacetime is characterized by the Finsler geometry instead of the Riemann one. The Finsler geometry admits fewer symmetries than the Riemann geometry does. We investigate the Finslerian geodesic equations in the Randers spacetime (a special Finsler spacetime). It is found that the cosmological redshift in this spacetime deviates from the one in general relativity. The modification term to the redshift could be generally revealed as a monopole plus dipole function of spacetime locations and directions. We suggest that this modification corresponds to the spatial monopole and dipole of α variation in the quasar absorption spectra.

Climate Projections Using Bayesian Model Averaging and Space–Time Dependence

Projections of future climatic changes are a key input to the design of climate change mitigation and adaptation strategies. Current climate change projections are deeply uncertain. This uncertainty stems from several factors, including parametric and structural uncertainties. One common approach to characterize and, if possible, reduce these uncertainties is to confront (calibrate in a broad sense) the models with historical observations. Here, we analyze the problem of combining multiple climate models using Bayesian Model Averaging (BMA) to derive future projections and quantify uncertainty estimates of spatiotemporally resolved temperature hindcasts and projections. One advantage of the BMA approach is that it allows the assessment of the predictive skill of a model using the training data, which can help identify the better models and discard poor models. Previous BMA approaches have broken important new ground, but often neglected space–time dependencies and/or imposed prohibitive computational demands. Here we improve on the current state-of-the-art by incorporating space–time dependence while using historical data to estimate model weights. We achieve computational efficiency using a kernel mixing approach for representing a space–time process. One key advantage of our new approach is that it enables us to incorporate multiple sources of uncertainty and biases, while remaining computationally tractable for large data sets. We introduce and apply our approach using BMA to an ensemble of Global Circulation Model output from the Intergovernmental Panel on Climate Change Fourth Assessment Report of surface temperature on a grid of space–time locations.

Agnesi weighting for the measure problem of cosmology

The measure problem of cosmology is how to assign normalizedprobabilities to observations in a universe so large that it may havemany observations occurring at many different spacetime locations. Ihave previously shown how the Boltzmann brain problem (that observationsarising from thermal or quantum fluctuations may dominate over ordinaryobservations if the universe expands sufficiently and/or lasts longenough) may be ameliorated by volume averaging, but that still leavesproblems if the universe lasts too long. Here a solution is proposedfor that residual problem by a simple weighting factor 1/(1+t2) tomake the time integral convergent. The resulting Agnesi measure appearsto avoid problems other measures may have with vacua of zero or negativecosmological constant.

Massive particles, Kerr–Minkowski fields and singularity removal in relativistic quantum mechanics

This paper concerns the spacetime locations of massive spinning particles in relativistic quantum mechanics. Using techniques from twistor theory and the unitary representations of the Lorentz group, it is shown that, for a particle of mass m and spin j , the radial distance from the particle to the observer is bounded below by . If the particle is the source of a Kerr–Minkowski field, the analogue of a Kerr field in general relativity, it is further shown that quantization removes the ring singularity if j is Fermionic or zero.

A probabilistic framework for representing and simulating uncertain environmental variables

Understanding the limitations of environmental data is important for managing environmental systems effectively and for encouraging the responsible use of uncertain data. Explicit assessment of the uncertainties associated with environmental data, and their storage in a database, are therefore important. This paper presents a statistical framework for representing and simulating uncertain environmental variables. In general terms, an uncertain variable is completely specified by its probability distribution function (pdf). Pdfs are developed for objects with uncertain locations (‘positional uncertainty’) and uncertain attribute values (‘attribute uncertainty’). Objects comprising multiple space–time locations are separated into ‘rigid objects’, where positional uncertainty cannot alter the internal geometry of the object, and ‘deformable’ objects, where positional uncertainty can vary between locations in one object. Statistical dependence is allowed between uncertainties in multiple locations in one object. The uncertainties associated with attribute values are also modelled with pdfs. The type and complexity of these pdfs depend upon the measurement scale and the space–time variability of the uncertain attribute. The framework is illustrated with examples. A prototype software tool for assessing uncertainties in environmental data, storing them within a database, and for generating realizations for use in Monte Carlo studies is also presented.

Hindcasting the Georges Bank circulation, Part II: wind-band inversion

Nonlinear inversion of velocity observations to produce limited-area circulation hindcasts is examined in the context of the USGLOBEC-Georges Bank surveys. Two linear inverses representing tide- and wind-band responses are used serially in an iteration with a nonlinear forward model. Both inverses are linearized about the nonlinear model. The objective is generalized least-squares fit; the controls are sea level elevation on the boundary of a limited-area mesh. The iteration converges rapidly provided a good prior estimate is available. Observational system simulation experiments address prediction skill for an idealized shelf regime and for an isolated wind event on Georges Bank. The idealized shelf inversion shows the limitations of extrapolative skill. For the wind event, skill is sensitive to the space-time locations of the observations. A real Georges Bank cruise, ENDEAVOR 261, is studied and the hindcast based on shipboard ADCP and moored data is assessed. The prior estimate based on climatology plus observed wind captured much of the tidal variability, most of the time-mean circulation, and little of the wind-band variability. The inversion improved the tidal fit, captured the wind-band variability, and did not degrade the mean.

The responsive order: A new empiricism

The uniqueness of logic is upheld and contrasted with twenty roles of a wider “responsive order” that includes us and our procedures. Empirical responses are precise, but different in different approaches. Procedures and findings are independent of (not separable from) “their” concepts. Two-way feedback obviates a top-down derivation of findings from assumptions, hypotheses, history, or language. The postmodern problems of “interpretation,” “conditions of appearances” and relativism involve the ancient error of making perception the model-instance of experience. Instead, bodily interaction functions in language and precedes perception and interpretation. Logic, space time locations and individuated referents involve positional relations derived from comparing. Beyond Kuhn, Feyerabend, Newton and Einstein, if we can give interaction priority over comparing, the “responsive objectivity” of both can be upheld. A new empiricism, neither naive nor constructivist, uses the words “order,” “explication,” “truth,” and “exactly” to build on Wittgenstein and on Dilthey’s hermeneutic. Natural language is metaphor-like, “originally crossed.” Logic must ignore its assumptions. It must render everything as a machine and drop humans and animals out. A new discipline is proposed, to move between the logical and the responsive orders, to deal with the machine/human interface and the social uses of science such as bioengineering.

Spatiotemporal Stochastic Open-Channel Flow. II: Simulation Experiments

Spatiotemporal solutions for open-channel flow are obtained in a stochastic setting using field data on parameter variability. Statistical descriptions of the flow variables are estimated through Monte Carlo simulation using finite difference equations for a 10-km reach of the Columbia River. Results indicate considerable uncertainty in predicted flow behavior: ensemble coefficients of variation at different space-time locations ranged from 0.18–0.60 for flow velocity and from 0.04–0.13 for flow depth. The band widths between the 16% and 84% quantiles were typically 0.6–1.4 m/s and 5–7 m, respectively, for velocity and depth. Probability distributions for predicted velocities were found to be gamma, lognormal, or Weibull, whereas those for depth were normal, gamma, and, in a few cases, lognormal. The various quantiles of the predicted variables are associated with notions of risk, reliability, and variability that influence engineering decisions. Sensitivity of the level of uncertainty in predicted flow v...

Disease cluster statistics for imprecise space-time locations.

Health professionals are investigating an increasing number of possible disease clusters, and statistical tests play an important role in cluster description and analysis. Existing cluster statistics assume precise data, when in reality health events are often imprecise (for example, place of residence is known only to the census district or zip code) and uncertain (for example, 'I first became ill sometime in 1985'). This incompatibility--precise methods used to analyse imprecise data--is largely ignored, resulting in test statistics of unknown accuracy. Most cluster statistics can be written as the cross-product of two matrices where one matrix reflects nearest-neighbour, distance or adjacency relationships and the second matrix is health related (for example, case-control identities). This paper explores a general approach to clustering, which incorporates uncertainty regarding space-time locations into these nearest neighbour, distance or adjacency relationships. Because the approach is general it can be used with almost all existing cluster tests, and, because it accounts for imprecise location data, it is suited to the 'real-world' nature of disease cluster investigations.

Geometro-stochastic quantisation and parallel transport of Fermion fields in curved spacetime

The geometro-stochastic method of quantisation is applied to fields describing massive particles of spin 1 2 moving within a fixed curved pseudo-Riemannian manifold M 4. It is shown that the first-quantised and second-quantised bundles, whose fibres are the carriers of quantum states at mean spacetime locations described by the points of M 4, are Hilbert bundles associated with the principal bundle P( M 4, ISL(2, C)) . The parallel transport within these quantum bundles is studied within the context of connection forms on the principal bundle.

Geometro-stochastic quantization of gravity. I

A general method of geometro-stochastic first quantization is outlined, and then applied to the construction of a graviton bundleE. This quantization is performed by treating the graviton as a massless stringlike exciton of spin 2, whose states can be represented by the vectors in the bundleE over a base manifoldM of mean spacetime locations. The graviton bundleE carries an indefinite inner product, and possesses both external Poincare gauge degrees of freedom, as well as internal gauge degrees of freedom related to general changes of coordinates in an immediate neighbourhood of eachx ∈M.

Helium formation in a class of radiative cosmological models

Helium formation is studied in a class of exact radiation-filled cosmological models in which the space-time geometry is represented by the Friedmann-Robertson-Walker metric and the source of the gravitational field is a non-co-moving imperfect fluid. Using the methods described by Barrow (1984) we find it possible to calculate the predicted helium-4 abundance in the class of radiative models analytically in terms of the results in the corresponding standard model. It is found that different amounts of helium-4 are produced at different space-time locations in the models. It is also found that while there is less helium-4 produced than in the standard model, the models give rise to predictions for the primordial helium-4 abundance within the observed range. In view of current estimates for the pre-galactic helium abundance and the predictions obtained from the standard model, models in which less helium is produced are of particular interest. The techniques employed in the calculation are discussed, and the assumptions and approximations that have been made are described in some detail.