Modeling spatial processes with unknown extremal dependence class
Huser, Raphaël G.
2017-03-17
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.
Testing for spatial error dependence in probit models
Amaral, P. V.; Anselin, L.; Arribas-Bel, D.
2013-01-01
In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the differences between the tests proposed by Pinkse and Slade (J Econom 85(1):125-254, 1998), Pinkse (Asymptotics of the Moran test and a
Spatial dependence of extreme rainfall
Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri
2017-05-01
This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.
Directory of Open Access Journals (Sweden)
Martin Gregory T
2004-11-01
Full Text Available Abstract Background Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach. Methods We represent heat transport processes by using a lattice that represents the Pennes bioheat equation in perfused tissues, and diffusion in nonperfused regions. The three layer skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that is constant or temperature-dependent. Two cases are considered: (1 surface contact heating and (2 spatially distributed heating. The model is relevant to the prediction of the transient and steady state temperature rise for different methods of power deposition within the skin. Accumulated thermal damage is estimated by using an Arrhenius type rate equation at locations where viable tissue temperature exceeds 42°C. Prediction of spatial temperature distributions is also illustrated with a two-dimensional model of skin created from a histological image. Results The transport lattice approach was validated by comparison with an analytical solution for a slab with homogeneous thermal properties and spatially distributed uniform sink held at constant temperatures at the ends. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45°C surface. Spatial heterogeneity in skin thermal properties leads to a non-uniform temperature distribution during a 10 GHz electromagnetic field exposure. A realistic two-dimensional model of the skin shows that tissue heterogeneity does not lead to a significant local temperature increase when heated by a hot wire tip. Conclusions The heat transport system model of the
Spatially-Dependent Modelling of Pulsar Wind Nebula G0.9+0.1
van Rensburg, C.; Krüger, P. P.; Venter, C.
2018-03-01
We present results from a leptonic emission code that models the spectral energy distribution of a pulsar wind nebula by solving a Fokker-Planck-type transport equation and calculating inverse Compton and synchrotron emissivities. We have created this time-dependent, multi-zone model to investigate changes in the particle spectrum as they traverse the pulsar wind nebula, by considering a time and spatially-dependent B-field, spatially-dependent bulk particle speed implying convection and adiabatic losses, diffusion, as well as radiative losses. Our code predicts the radiation spectrum at different positions in the nebula, yielding the surface brightness versus radius and the nebular size as function of energy. We compare our new model against more basic models using the observed spectrum of pulsar wind nebula G0.9+0.1, incorporating data from H.E.S.S. as well as radio and X-ray experiments. We show that simultaneously fitting the spectral energy distribution and the energy-dependent source size leads to more stringent constraints on several model parameters.
The importance of spatial models for estimating the strength of density dependence
DEFF Research Database (Denmark)
Thorson, James T.; Skaug, Hans J.; Kristensen, Kasper
2014-01-01
for an entire population. However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics. We therefore adapt the Gompertz model to approximate local densities over continuous space instead of population-wide abundance...... the California Coast. In this case, the nonspatial model estimates implausible oscillatory dynamics on an annual time scale, while the spatial model estimates strong autocorrelation and is supported by model selection tools. We conclude by discussing the importance of improved data archiving techniques, so...
Modeling spatial-temporal operations with context-dependent associative memories.
Mizraji, Eduardo; Lin, Juan
2015-10-01
We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.
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Łaszkiewicz Edyta
2014-12-01
Full Text Available As is well known, ignoring spatial heterogeneity leads to biased parameter estimates, while omitting the spatial lag of a dependent variable results in biasness and inconsistency (Anselin, 1988. However, the common approach to analysing households’ expenditures is to ignore the potential spatial effects and social dependence. In light of this, the aim of this paper is to examine the consequences of omitting the spatial effects as well as social dependence in households’ expenditures.
A spatial interpretation of the density dependence model in industrial demography
van Wissen, L
2004-01-01
In this paper the density dependence model, which was developed in organizational ecology, is compared to the economic-geographical notion of agglomeration economies. There is a basic resemblance: both involve some form of positive feedback between size of the population and growth. The paper
Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.
Conner, Mary M.; Gross, John E.; Cross, Paul C.; Ebinger, Michael R.; Gillies, Robert; Samuel, Michael D.; Miller, Michael W.
2007-01-01
This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination.
Spatial dependency of action simulation
Horst, A.C. ter; Lier, R.J. van; Steenbergen, B.
2011-01-01
In this study, we investigated the spatial dependency of action simulation. From previous research in the field of single-cell recordings, grasping studies and from crossmodal extinction tasks, it is known that our surrounding space can be divided into a peripersonal space and extrapersonal space.
A Classification for a Geostatistical Index of Spatial Dependence
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Enio Júnior Seidel
Full Text Available ABSTRACT: In geostatistical studies, spatial dependence can generally be described by means of the semivariogram or, in complementary form, with a single index followed by its categorization to classify the degree of such dependence. The objective of this study was to construct a categorization for the spatial dependence index (SDI proposed by Seidel and Oliveira (2014 in order to classify spatial variability in terms of weak, moderate, and strong dependence. Theoretical values were constructed from different degrees of spatial dependence, which served as a basis for calculation of the SDI. In view of the form of distribution and SDI descriptive measures, we developed a categorization for posterior classification of spatial dependence, specific to each semivariogram model. The SDI categorization was based on its median and 3rd quartile, allowing us to classify spatial dependence as weak, moderate, or strong. We established that for the spherical semivariogram: SDISpherical (% ≤ 7 % (weak spatial dependence, 7 % 15 % (strong spatial dependence; for the exponential semivariogram: SDIExponential (% ≤ 6 % (weak spatial dependence, 6 % 13 % (strong spatial dependence; and for the Gaussian semivariogram: SDIGaussian (% ≤ 9 % (weak spatial dependence, 9 % 20 % (strong spatial dependence. The proposed categorization allows the user to transform the numerical values calculated for SDI into categories of variability of spatial dependence, with adequate power for explanation and comparison.
Modeling for spatial multilevel structural data
Min, Suqin; He, Xiaoqun
2013-03-01
The traditional multilevel model assumed independence between groups. However, the datasets grouped by geographical units often has spatial dependence. The individual is influenced not only by its region but also by the adjacent regions, and level-2 residual distribution assumption of traditional multilevel model is violated. In order to deal with such spatial multilevel data, we introduce spatial statistics and spatial econometric models into multilevel model, and apply spatial parameters and adjacency matrix in traditional level-2 model to reflect the spatial autocorrelation. Spatial lag model express spatial effects. We build spatial multilevel model which consider both multilevel thinking and spatial correlation.
Spatial dependency of action simulation.
ter Horst, Arjan C; van Lier, Rob; Steenbergen, Bert
2011-08-01
In this study, we investigated the spatial dependency of action simulation. From previous research in the field of single-cell recordings, grasping studies and from crossmodal extinction tasks, it is known that our surrounding space can be divided into a peripersonal space and extrapersonal space. These two spaces are functionally different at both the behavioral and neuronal level. The peripersonal space can be seen as an action space which is limited to the area in which we can grasp objects without moving the object or ourselves. The extrapersonal space is the space beyond the peripersonal space. Objects situated within peripersonal space are mapped onto an egocentric reference frame. This mapping is thought to be accomplished by action simulation. To provide direct evidence of the embodied nature of this simulated motor act, we performed two experiments, in which we used two mental rotation tasks, one with stimuli of hands and one with stimuli of graspable objects. Stimuli were presented in both peri- and extrapersonal space. The results showed increased reaction times for biomechanically difficult to adopt postures compared to more easy to adopt postures for both hand and graspable object stimuli. Importantly, this difference was only present for stimuli presented in peripersonal space but not for the stimuli presented in extrapersonal space. These results extend previous behavioral findings on the functional distinction between peripersonal- and extrapersonal space by providing direct evidence for the spatial dependency of the use of action simulation. Furthermore, these results strengthen the hypothesis that objects situated within the peripersonal space are mapped onto an egocentric reference frame by action simulation.
One-dimensional spatially dependent solute transport in semi ...
African Journals Online (AJOL)
The present study is an attempt to describe analytical solution of spatially dependent solute transport in one-dimensional semiinfinite homogeneous porous domain. In this mathematical model the dispersion coefficient is considered spatially dependent while seepage velocity is considered exponentially decreasing function ...
One-dimensional spatially dependent solute transport in semi ...
African Journals Online (AJOL)
The present study is an attempt to describe analytical solution of spatially dependent solute transport in one-dimensional semi- infinite homogeneous porous domain. In this mathematical model the dispersion coefficient is considered spatially dependent while seepage velocity is considered exponentially decreasing ...
Lawson, Andrew B
2002-01-01
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...
Dynamic spatial panels : models, methods, and inferences
Elhorst, J. Paul
This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent
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Marion Inostroza
Full Text Available Cognitive impairment is a major concern in temporal lobe epilepsy (TLE. While different experimental models have been used to characterize TLE-related cognitive deficits, little is known on whether a particular deficit is more associated with the underlying brain injuries than with the epileptic condition per se. Here, we look at the relationship between the pattern of brain damage and spatial memory deficits in two chronic models of TLE (lithium-pilocarpine, LIP and kainic acid, KA from two different rat strains (Wistar and Sprague-Dawley using the Morris water maze and the elevated plus maze in combination with MRI imaging and post-morten neuronal immunostaining. We found fundamental differences between LIP- and KA-treated epileptic rats regarding spatial memory deficits and anxiety. LIP-treated animals from both strains showed significant impairment in the acquisition and retention of spatial memory, and were unable to learn a cued version of the task. In contrast, KA-treated rats were differently affected. Sprague-Dawley KA-treated rats learned less efficiently than Wistar KA-treated animals, which performed similar to control rats in the acquisition and in a probe trial testing for spatial memory. Different anxiety levels and the extension of brain lesions affecting the hippocampus and the amydgala concur with spatial memory deficits observed in epileptic rats. Hence, our results suggest that hippocampal-dependent spatial memory is not necessarily affected in TLE and that comorbidity between spatial deficits and anxiety is more related with the underlying brain lesions than with the epileptic condition per se.
Spatial dependences among precipitation maxima over Belgium
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S. Vannitsem
2007-09-01
Full Text Available For a wide range of applications in hydrology, the probability distribution of precipitation maxima represents a fundamental quantity to build dykes, propose flood planning policies, or more generally, to mitigate the impact of precipitation extremes. Classical Extreme Value Theory (EVT has been applied in this context by usually assuming that precipitation maxima can be considered as Independent and Identically Distributed (IID events, which approximately follow a Generalized Extreme Value distribution (GEV at each recording site. In practice, weather stations records can not be considered as independent in space.
Assessing the spatial dependences among precipitation maxima provided by two Belgium measurement networks is the main goal of this work. The pairwise dependences are estimated by a variogram of order one, also called madogram, that is specially tailored to be in compliance with spatial EVT and to capture EVT bivariate structures. Our analysis of Belgium precipitation maxima indicates that the degree of dependence varies greatly according to three factors: the distance between two stations, the season (summer or winter and the precipitation accumulation duration (hourly, daily, monthly, etc.. Increasing the duration (from one hour to 20 days strengthens the spatial dependence. The full independence is reached after about 50 km (100 km for summer (winter for a duration of one hour, while for long durations only after a few hundred kilometers. In addition this dependence is always larger in winter than in summer whatever is the duration. An explanation of these properties in terms of the dynamical processes dominating during the two seasons is advanced.
Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...
Non-Stationary Dependence Structures for Spatial Extremes
Huser, Raphaël
2016-03-03
Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.
Spatial Uncertainty Analysis of Ecological Models
Energy Technology Data Exchange (ETDEWEB)
Jager, H.I.; Ashwood, T.L.; Jackson, B.L.; King, A.W.
2000-09-02
The authors evaluated the sensitivity of a habitat model and a source-sink population model to spatial uncertainty in landscapes with different statistical properties and for hypothetical species with different habitat requirements. Sequential indicator simulation generated alternative landscapes from a source map. Their results showed that spatial uncertainty was highest for landscapes in which suitable habitat was rare and spatially uncorrelated. Although, they were able to exert some control over the degree of spatial uncertainty by varying the sampling density drawn from the source map, intrinsic spatial properties (i.e., average frequency and degree of spatial autocorrelation) played a dominant role in determining variation among realized maps. To evaluate the ecological significance of landscape variation, they compared the variation in predictions from a simple habitat model to variation among landscapes for three species types. Spatial uncertainty in predictions of the amount of source habitat depended on both the spatial life history characteristics of the species and the statistical attributes of the synthetic landscapes. Species differences were greatest when the landscape contained a high proportion of suitable habitat. The predicted amount of source habitat was greater for edge-dependent (interior) species in landscapes with spatially uncorrelated(correlated) suitable habitat. A source-sink model demonstrated that, although variation among landscapes resulted in relatively little variation in overall population growth rate, this spatial uncertainty was sufficient in some situations, to produce qualitatively different predictions about population viability (i.e., population decline vs. increase).
Spatial-dependence recurrence sample entropy
Pham, Tuan D.; Yan, Hong
2018-03-01
Measuring complexity in terms of the predictability of time series is a major area of research in science and engineering, and its applications are spreading throughout many scientific disciplines, where the analysis of physiological signals is perhaps the most widely reported in literature. Sample entropy is a popular measure for quantifying signal irregularity. However, the sample entropy does not take sequential information, which is inherently useful, into its calculation of sample similarity. Here, we develop a method that is based on the mathematical principle of the sample entropy and enables the capture of sequential information of a time series in the context of spatial dependence provided by the binary-level co-occurrence matrix of a recurrence plot. Experimental results on time-series data of the Lorenz system, physiological signals of gait maturation in healthy children, and gait dynamics in Huntington's disease show the potential of the proposed method.
Towards a taxonomy of spatial scale-dependence
DEFF Research Database (Denmark)
Sandel, Brody Steven
2015-01-01
Spatial scale-dependence is a ubiquitous feature of ecological systems. This presents a challenge for ecologists who seek to discern general principles. A solution is to search for generalities in patterns of scale-dependence – that is, what kinds of things are scale-dependent, in what ways...... major types in hopes of moving towards a more formal and unambiguous vocabulary. I argue that three major distinctions are necessary and sufficient for this goal: that between grain size and extent (the scale component), between data and models (the subject), and between true and perceived scale...
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan
2010-01-28
We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.
Archambault, B.; Rivot, E.; Savina, M.; Le Pape, O.
2018-02-01
Exploited coastal-nursery-dependent fish species are subject to various stressors occurring at specific stages of the life cycle: climate-driven variability in hydrography determines the success of the first eggs/larvae stages; coastal nursery habitat suitability controls juvenile growth and survival; and fisheries target mostly adults. A life cycle approach was used to quantify the relative influence of these stressors on the Eastern English Channel (EEC) population of the common sole (Solea solea), a coastal-nursery-dependent flatfish population which sustains important fisheries. The common sole has a complex life cycle: after eggs hatch, larvae spend several weeks drifting in open water. Survivors go on to metamorphose into benthic fish. Juveniles spend the first two years of their life in coastal and estuarine nurseries. Close to maturation, they migrate to deeper areas, where different subpopulations supplied by different nurseries reproduce and are exploited by fisheries. A spatially structured age-and stage-based hierarchical Bayesian model integrating various aspects of ecological knowledge, data sources and expert knowledge was built to quantitatively describe this complex life cycle. The model included the low connectivity among three subpopulations in the EEC, the influence of hydrographic variability, the availability of suitable juvenile habitat and fisheries. Scenarios were designed to quantify the effects of interacting stressors on population renewal. Results emphasized the importance of coastal nursery habitat availability and quality for the population renewal. Realistic restoration scenarios of the highly degraded Seine estuary produced a two-third increase in catch potential for the adjacent subpopulation. Fisheries, however, remained the main source of population depletion. Setting fishing mortality to the maximum sustainable yield led to substantial increases in biomass (+100%) and catch (+33%) at the EEC scale. The approach also showed how
Lam, Chun-Sing; Tipoe, George Lim; So, Kwok-Fai; Fung, Man-Lung
2015-01-01
Chronic intermittent hypoxia (CIH) is a hallmark of obstructive sleep apnea (OSA), which induces hippocampal injuries mediated by oxidative stress. This study aims to examine the neuroprotective mechanism of Lycium barbarum polysaccharides (LBP) against CIH-induced spatial memory deficits. Adult Sprague-Dawley rats were exposed to hypoxic treatment resembling a severe OSA condition for a week. The animals were orally fed with LBP solution (1 mg/kg) daily 2 hours prior to hypoxia or in air for the control. The effect of LBP on the spatial memory and levels of oxidative stress, inflammation, endoplasmic reticulum (ER) stress, apoptosis and neurogenesis in the hippocampus was examined. There was a significant deficit in the spatial memory and an elevated level of malondialdehyde with a decreased expression of antioxidant enzymes (SOD, GPx-1) in the hypoxic group when compared with the normoxic control. In addition, redox-sensitive nuclear factor kappa B (NFКB) canonical pathway was activated with a translocation of NFКB members (p65, p50) and increased expression levels of NFКB-dependent inflammatory cytokines and mediator (TNFα, IL-1β, COX-2); also, a significantly elevated level of ER stress (GRP78/Bip, PERK, CHOP) and autophagic flux in the hypoxic group, leading to neuronal apoptosis in hippocampal subfields (DG, CA1, CA3). Remarkably, LBP administration normalized the elevated level of oxidative stress, neuroinflammation, ER stress, autophagic flux and apoptosis induced by hypoxia. Moreover, LBP significantly mitigated both the caspase-dependent intrinsic (Bax, Bcl2, cytochrome C, cleaved caspase-3) and extrinsic (FADD, cleaved caspase-8, Bid) signaling apoptotic cascades. Furthermore, LBP administration prevented the spatial memory deficit and enhanced the hippocampal neurogenesis induced by hypoxia. Our results suggest that LBP is neuroprotective against CIH-induced hippocampal-dependent spatial memory deficits by promoting hippocampal neurogenesis and
Directory of Open Access Journals (Sweden)
Chun-Sing Lam
Full Text Available Chronic intermittent hypoxia (CIH is a hallmark of obstructive sleep apnea (OSA, which induces hippocampal injuries mediated by oxidative stress. This study aims to examine the neuroprotective mechanism of Lycium barbarum polysaccharides (LBP against CIH-induced spatial memory deficits. Adult Sprague-Dawley rats were exposed to hypoxic treatment resembling a severe OSA condition for a week. The animals were orally fed with LBP solution (1 mg/kg daily 2 hours prior to hypoxia or in air for the control. The effect of LBP on the spatial memory and levels of oxidative stress, inflammation, endoplasmic reticulum (ER stress, apoptosis and neurogenesis in the hippocampus was examined. There was a significant deficit in the spatial memory and an elevated level of malondialdehyde with a decreased expression of antioxidant enzymes (SOD, GPx-1 in the hypoxic group when compared with the normoxic control. In addition, redox-sensitive nuclear factor kappa B (NFКB canonical pathway was activated with a translocation of NFКB members (p65, p50 and increased expression levels of NFКB-dependent inflammatory cytokines and mediator (TNFα, IL-1β, COX-2; also, a significantly elevated level of ER stress (GRP78/Bip, PERK, CHOP and autophagic flux in the hypoxic group, leading to neuronal apoptosis in hippocampal subfields (DG, CA1, CA3. Remarkably, LBP administration normalized the elevated level of oxidative stress, neuroinflammation, ER stress, autophagic flux and apoptosis induced by hypoxia. Moreover, LBP significantly mitigated both the caspase-dependent intrinsic (Bax, Bcl2, cytochrome C, cleaved caspase-3 and extrinsic (FADD, cleaved caspase-8, Bid signaling apoptotic cascades. Furthermore, LBP administration prevented the spatial memory deficit and enhanced the hippocampal neurogenesis induced by hypoxia. Our results suggest that LBP is neuroprotective against CIH-induced hippocampal-dependent spatial memory deficits by promoting hippocampal neurogenesis
An energy dependent spatial approximation for transport deflection calculations
International Nuclear Information System (INIS)
Stankovski, Z.; Sanchez, R.; Roy, R.
1989-01-01
A model for transport depletion calculations based on an energy-dependent spatial representation of the fluxes has been developed. In the case of thermal absorbers, this model allows for regions in the fast range to be less discretized than in the thermal range. When depletion calculations are done to obtain the variation of the isotopic concentration vs. the burnup, the media where several spatial flux representations are used become heterogeneous. In the fast range, prehomogenization of the physical properties is done prior to each transport step. Even when taking into account this prehomogenization step, the computational cost of transport depleted calculations has been cut down significantly, while preserving the overall accuracy. Numerical results are given for a slab core and for a PWR poisoned assembly
Spatial Dependence of Crime in Monterrey, Mexico
Directory of Open Access Journals (Sweden)
Ernesto Aguayo Téllez
2014-06-01
Full Text Available This paper studies the impact that the characteristics of the environment have on crime using neighborhood aggregate data of the Monterrey Metropolitan Area for the year 2010. Data spatial autocorrelation is corroborated, i.e. neighborhoods with high crime rates have a positive impact on the crime rates of its surrounding neighborhoods. Once it was controlled through the bias caused by spatial autocorrelation and data censoring, it is evidenced that the likelihood of being a crime victim and the probability of becoming an offender is positively related to variables such as unemployment, the percentage of young men and the existence of schools, hospitals or markets in the neighborhood.
Using Spatial Gradients to Model Localization Phenomena
Energy Technology Data Exchange (ETDEWEB)
D.J.Bammann; D.Mosher; D.A.Hughes; N.R.Moody; P.R.Dawson
1999-07-01
We present the final report on a Laboratory-Directed Research and Development project, Using Spatial Gradients to Model Localization Phenomena, performed during the fiscal years 1996 through 1998. The project focused on including spatial gradients in the temporal evolution equations of the state variables that describe hardening in metal plasticity models. The motivation was to investigate the numerical aspects associated with post-bifurcation mesh dependent finite element solutions in problems involving damage or crack propagation as well as problems in which strain Localizations occur. The addition of the spatial gradients introduces a mathematical length scale that eliminates the mesh dependency of the solution. In addition, new experimental techniques were developed to identify the physical mechanism associated with the numerical length scale.
Verifying the Dependence of Fractal Coefficients on Different Spatial Distributions
International Nuclear Information System (INIS)
Gospodinov, Dragomir; Marekova, Elisaveta; Marinov, Alexander
2010-01-01
A fractal distribution requires that the number of objects larger than a specific size r has a power-law dependence on the size N(r) = C/r D ∝r -D where D is the fractal dimension. Usually the correlation integral is calculated to estimate the correlation fractal dimension of epicentres. A 'box-counting' procedure could also be applied giving the 'capacity' fractal dimension. The fractal dimension can be an integer and then it is equivalent to a Euclidean dimension (it is zero of a point, one of a segment, of a square is two and of a cube is three). In general the fractal dimension is not an integer but a fractional dimension and there comes the origin of the term 'fractal'. The use of a power-law to statistically describe a set of events or phenomena reveals the lack of a characteristic length scale, that is fractal objects are scale invariant. Scaling invariance and chaotic behavior constitute the base of a lot of natural hazards phenomena. Many studies of earthquakes reveal that their occurrence exhibits scale-invariant properties, so the fractal dimension can characterize them. It has first been confirmed that both aftershock rate decay in time and earthquake size distribution follow a power law. Recently many other earthquake distributions have been found to be scale-invariant. The spatial distribution of both regional seismicity and aftershocks show some fractal features. Earthquake spatial distributions are considered fractal, but indirectly. There are two possible models, which result in fractal earthquake distributions. The first model considers that a fractal distribution of faults leads to a fractal distribution of earthquakes, because each earthquake is characteristic of the fault on which it occurs. The second assumes that each fault has a fractal distribution of earthquakes. Observations strongly favour the first hypothesis.The fractal coefficients analysis provides some important advantages in examining earthquake spatial distribution, which are
Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel
2016-12-19
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
Energy Technology Data Exchange (ETDEWEB)
Qin, Zhangcai [Energy Systems Division, Argonne National Laboratory, 9700 South Cass Avenue Argonne IL 60439 USA; Dunn, Jennifer B. [Energy Systems Division, Argonne National Laboratory, 9700 South Cass Avenue Argonne IL 60439 USA; Kwon, Hoyoung [Environment and Production Technology Division, International Food Policy Research Institute, 2033 K St. NW Washington DC 20006 USA; Mueller, Steffen [Energy Resources Center, University of Illinois at Chicago, 1309 South Halsted Street Chicago IL 60607 USA; Wander, Michelle M. [Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, 1102 South Goodwin Avenue Urbana IL 61801 USA
2016-03-03
Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life-cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life-cycle GHG emissions affect biofuels’ attractiveness and eligibility under a number of renewable fuel policies in the U.S. and abroad. Modeling was used to refine the spatial resolution and depth-extent of domestic estimates of SOC change for land (cropland, cropland pasture, grasslands, and forests) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow). In most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. Results of SOC change were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life-cycle GHG emissions for the biofuels considered. Total LUC GHG emissions (g CO2eq MJ-1) were 2.1–9.3 for corn, -0.7 for corn stover, -3.4–12.9 for switchgrass, and -20.1–-6.2 for Miscanthus; these varied with SOC modeling assumptions applied. Extending soil depth from 30 to 100cm affected spatially-explicit SOC change and overall LUC GHG emissions; however the influence on LUC GHG emissions estimates were less significant in corn and corn stover than cellulosic feedstocks. Total life-cycle GHG emissions (g CO2eq MJ-1, 100cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18-26 for switchgrass ethanol, and -0.6–-7 for Miscanthus ethanol.
Spatial regression-based model specifications for exogenous and endogenous spatial interaction
Manfred M Fischer; James P. LeSage
2014-01-01
Spatial interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination flows ...
Liu, Lin; Park, Jonghyun; Lin, Xianke; Sastry, Ann Marie; Lu, Wei
2014-12-01
The formation of a SEI layer and its growth cause internal resistance increase and capacity loss, leading to performance degradation of lithium-ion batteries. In order to comprehensively investigate the effects of SEI growth on battery performance, a one-dimensional thermal-electrochemical model was developed. This model is equipped with a growth mechanism of the SEI layer coupled with thermal evolution, based on the diffusional process of the solvent through the SEI layer and the kinetic process at the interface between the solid and liquid phases. The model is able to reveal the effects of diffusivity, reaction kinetics and temperature on SEI layer growth and cell capacity fade. We show that depending on the SEI thickness, the growth can be kinetics-limited or diffusion-limited. With the layer becoming thicker, its growth rate slows down gradually due to increased diffusion resistance. The SEI layer grows faster during charge than discharge due to the difference in the electron flux through the SEI layer and the temperature change during cycling. Temperature rise due to reaction and joule heating accelerates the SEI layer growth, leading to more capacity loss. Our model can provide insights on position-dependent SEI growth rate and be used to guide the strategic monitoring location.
Selection of spatial reference frames depends on task's demands
Directory of Open Access Journals (Sweden)
Greeshma Sharma
2016-12-01
Full Text Available Spatial reference frames (SRF are the means of representing spatial relations or locations either in an egocentric coordinate system (centred on navigator or in an allocentric coordinate system (Centred on object. It is necessary to understand when and how spatial representation switches between allocentric and egocentric reference frames in context to spatial tasks. The objective of this study was to explore if the elementary spatial representation does exist, whether it would remain consistent or change under the influence of a task's demand. Also, we explored how the SRF would assist if the environment is enriched with landmarks, having multiple routes for wayfinding. The results showed that the switching of SRF depends not only on the default representation but also on a task's demand. They also demonstrated that participants who were using allocentric representation performed better in the presence of landmarks.
Spatially explicit non-Mendelian diploid model
Lanchier, N.; Neuhauser, C.
2009-01-01
We introduce a spatially explicit model for the competition between type $a$ and type $b$ alleles. Each vertex of the $d$-dimensional integer lattice is occupied by a diploid individual, which is in one of three possible states or genotypes: $aa$, $ab$ or $bb$. We are interested in the long-term behavior of the gene frequencies when Mendel's law of segregation does not hold. This results in a voter type model depending on four parameters; each of these parameters measures the strength of comp...
Spatially explicit modeling in ecology: A review
DeAngelis, Donald L.; Yurek, Simeon
2017-01-01
The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.
Overnight Sleep Enhances Hippocampus-Dependent Aspects of Spatial Memory.
Nguyen, Nam D; Tucker, Matthew A; Stickgold, Robert; Wamsley, Erin J
2013-07-01
Several studies have now demonstrated that spatial information is processed during sleep, and that posttraining sleep is beneficial for human navigation. However, it remains unclear whether the effects of sleep are primarily due to consolidation of cognitive maps, or alternatively, whether sleep might also affect nonhippocampal aspects of navigation (e.g., speed of motion) involved in moving through a virtual environment. Participants were trained on a virtual maze navigation task (VMT) and then given a memory test following either a day of wakefulness or a night of sleep. Subjects reported to the laboratory for training at either 10:00am or 10:00pm, depending on randomly assigned condition, and were tested 11 h later. Overnight subjects slept in the laboratory with polysomnography. A hospital-based academic sleep laboratory. Thirty healthy college student volunteers. N/A. Point-by-point position data were collected from the VMT. Analysis of the movement data revealed a sleep-dependent improvement in maze completion time (P sleep benefitted performance, not because subjects moved faster through the maze, but because they were more accurate in navigating to the goal. These findings suggest that sleep enhances participants' knowledge of the spatial layout of the maze, contributing to the consolidation of hippocampus-dependent spatial information. Nguyen ND; Tucker MA; Stickgold R; Wamsley EJ. Overnight sleep enhances hippocampus-dependent aspects of spatial memory. SLEEP 2013;36(7):1051-1057.
Spatial dependencies between large-scale brain networks.
Directory of Open Access Journals (Sweden)
Robert Leech
Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.
Yokogawa, D.
2016-09-01
Theoretical approach to design bright bio-imaging molecules is one of the most progressing ones. However, because of the system size and computational accuracy, the number of theoretical studies is limited to our knowledge. To overcome the difficulties, we developed a new method based on reference interaction site model self-consistent field explicitly including spatial electron density distribution and time-dependent density functional theory. We applied it to the calculation of indole and 5-cyanoindole at ground and excited states in gas and solution phases. The changes in the optimized geometries were clearly explained with resonance structures and the Stokes shift was correctly reproduced.
Hippocampus-dependent place learning enables spatial flexibility in mice
Directory of Open Access Journals (Sweden)
Karl R. Kleinknecht
2012-12-01
Full Text Available Spatial navigation is a fundamental capability necessary in everyday life to locate food, social partners and shelter. It results from two very different strategies: (i place learning which enables for flexible way finding and (ii response learning that leads to a more rigid ‘route following’. Despite the importance of knockout techniques that are only available in mice, little is known about mice’ flexibility in spatial navigation tasks.Here we demonstrate for C57BL6/N mice in a water-cross maze that only place learning enables spatial flexibility and relearning of a platform position, whereas response learning does not. This capability depends on an intact hippocampal formation, since hippocampus lesions by ibotenic acid disrupted relearning. In vivo manganese-enhanced magnetic resonance imaging revealed a volume loss of ≥ 60% of the hippocampus as a critical threshold for relearning impairments. In particular the changes in the left ventral hippocampus were indicative of relearning deficits.In summary, our findings establish the importance of hippocampus-dependent place learning for spatial flexibility and provide a first systematic analysis on spatial flexibility in mice.
A Spatially Extended Model for Residential Segregation
Directory of Open Access Journals (Sweden)
Antonio Aguilera
2007-01-01
Full Text Available We analyze urban spatial segregation phenomenon in terms of the income distribution over a population, and an inflationary parameter weighting the evolution of housing prices. For this, we develop a discrete spatially extended model based on a multiagent approach. In our model, the mobility of socioeconomic agents is driven only by the housing prices. Agents exchange location in order to fit their status to the cost of their housing. On the other hand, the price of a particular house depends on the status of its tenant, and on the neighborhood mean lodging cost weighted by a control parameter. The agent's dynamics converges to a spatially organized configuration, whose regularity is measured by using an entropy-like indicator. This simple model provides a dynamical process organizing the virtual city, in a way that the population inequality and the inflationary parameter determine the degree of residential segregation in the final stage of the process, in agreement with the segregation-inequality thesis put forward by Douglas Massey.
Spatial-frequency dependent binocular imbalance in amblyopia
Kwon, MiYoung; Wiecek, Emily; Dakin, Steven C.; Bex, Peter J.
2015-01-01
While amblyopia involves both binocular imbalance and deficits in processing high spatial frequency information, little is known about the spatial-frequency dependence of binocular imbalance. Here we examined binocular imbalance as a function of spatial frequency in amblyopia using a novel computer-based method. Binocular imbalance at four spatial frequencies was measured with a novel dichoptic letter chart in individuals with amblyopia, or normal vision. Our dichoptic letter chart was composed of band-pass filtered letters arranged in a layout similar to the ETDRS acuity chart. A different chart was presented to each eye of the observer via stereo-shutter glasses. The relative contrast of the corresponding letter in each eye was adjusted by a computer staircase to determine a binocular Balance Point at which the observer reports the letter presented to either eye with equal probability. Amblyopes showed pronounced binocular imbalance across all spatial frequencies, with greater imbalance at high compared to low spatial frequencies (an average increase of 19%, p amblyopia and as an outcome measure for recovery of binocular vision following therapy. PMID:26603125
Experience-dependent spatial expectations in mouse visual cortex
DEFF Research Database (Denmark)
Fiser, Aris; Mahringer, David; Oyibo, Hassana K.
2016-01-01
primary visual cortex (V1) becomes increasingly informative of spatial location. We found that a subset of V1 neurons exhibited responses that were predictive of the upcoming visual stimulus in a spatially dependent manner and that the omission of an expected stimulus drove strong responses in V1....... Stimulus-predictive responses also emerged in V1-projecting anterior cingulate cortex axons, suggesting that anterior cingulate cortex serves as a source of predictions of visual input to V1. These findings are consistent with the hypothesis that visual cortex forms an internal representation of the visual...
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
Czech Academy of Sciences Publication Activity Database
Valeš, Karel; Rambousek, Lukáš; Holubová, Kristína; Svoboda, Jan; Bubeníková-Valešová, V.; Chodounská, Hana; Vyklický ml., Ladislav; Stuchlík, Aleš
2012-01-01
Roč. 235, č. 1 (2012), s. 82-88 ISSN 0166-4328 R&D Projects: GA MZd(CZ) NS10365 Institutional research plan: CEZ:AV0Z50110509 Institutional support: RVO:67985823 ; RVO:61388963 Keywords : schizophrenia -like behavior * MK-801 * use-dependent * NMDA antagonist * anxiety * pregnanolone glutamate * Carousel maze Subject RIV: FH - Neurology Impact factor: 3.327, year: 2012
A spatial domain optimization method to generate plane dependent masks
Wu, Yifeng
2006-01-01
Stochastic screening technique uses a fixed threshold array to generate halftoned images. When this technique is applied to color images, an important problem is how to generate the masks for different color planes. Ideally, a set of plane dependent color masks should have the following characteristics: a) when total ink coverage is less than 100%, no dots in different colors should overlap from each other. b) for each individual mask, dot distribution should be uniform, c) no visual artifact should be visible due to the low frequency patterns. In this paper, we propose a novel color mask generation method in which the optimal dot placement is searched directly in spatial domain. The advantage of using the spatial domain approach is that we can control directly the dot uniformity during the optimization, and we can also cope with the color plane-dependency by introducing some inter-plane constraints. We will show that using this method, we can generate plane dependent color masks with the characteristics mentioned above.
Parrondo Games with Two-Dimensional Spatial Dependence
Ethier, S. N.; Lee, Jiyeon
Parrondo games with one-dimensional (1D) spatial dependence were introduced by Toral and extended to the two-dimensional (2D) setting by Mihailović and Rajković. MN players are arranged in an M × N array. There are three games, the fair, spatially independent game A, the spatially dependent game B, and game C, which is a random mixture or non-random pattern of games A and B. Of interest is μB (or μC), the mean profit per turn at equilibrium to the set of MN players playing game B (or game C). Game A is fair, so if μB ≤ 0 and μC > 0, then we say the Parrondo effect is present. We obtain a strong law of large numbers (SLLN) and a central limit theorem (CLT) for the sequence of profits of the set of MN players playing game B (or game C). The mean and variance parameters are computable for small arrays and can be simulated otherwise. The SLLN justifies the use of simulation to estimate the mean. The CLT permits evaluation of the standard error of a simulated estimate. We investigate the presence of the Parrondo effect for both small arrays and large ones. One of the findings of Mihailović and Rajković was that “capital evolution depends to a large degree on the lattice size.” We provide evidence that this conclusion is partly incorrect. A paradoxical feature of the 2D game B that does not appear in the 1D setting is that, for fixed M and N, the mean function μB is not necessarily a monotone function of its parameters.
Location Aggregation of Spatial Population CTMC Models
Directory of Open Access Journals (Sweden)
Luca Bortolussi
2016-10-01
Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.
Hierarchical modeling and analysis for spatial data
Banerjee, Sudipto; Gelfand, Alan E
2003-01-01
Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat
Directory of Open Access Journals (Sweden)
Simone Becker Lopes
2014-04-01
Full Text Available Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included. This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.
Models for dependent time series
Tunnicliffe Wilson, Granville; Haywood, John
2015-01-01
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater
Visual dependence and spatial orientation in benign paroxysmal positional vertigo.
Nair, Maitreyi A; Mulavara, Ajitkumar P; Bloomberg, Jacob J; Sangi-Haghpeykar, Haleh; Cohen, Helen S
2018-01-01
People with benign paroxysmal positional vertigo (BPPV) probably have otoconial particles displaced from the utricle into the posterior semicircular canal. This unilateral change in the inertial load distributions of the labyrinth may result in visual dependence and may affect balance control. The goal of this study was to explore the interaction between visual dependence and balance control. We compared 23 healthy controls to 17 people with unilateral BPPV on the Clinical Test of Sensory Interaction and Balance on compliant foam with feet together, the Rod-and-Frame Test and a Mental Rotation Test. In controls, but not BPPV subjects, subjects with poor balance scores had significantly greater visual dependence, indicating that reliance on visual cues can affect balance control. BPPV and control subjects did not differ on the mental rotation task overall but BPPV reaction time was greater at greater orietantions, suggesting that this cognitive function was affected by BPPV. The side of impairment was strongly related to the side of perceived bias in the Earth vertical determined by BPPV subjects, indicating the relationship between the effect of asymmetric otolith unloading with simultaneous canal loading on spatial orientation perception.
The effect of spatial dependence on hazard validation
Iervolino, Iunio; Giorgio, Massimiliano; Cito, Pasquale
2017-06-01
In countries where best-practice probabilistic hazard studies and seismic monitoring networks are available, there is increasing interest in direct validation of hazard maps. It usually means trying to quantitatively understand whether probabilities estimated via hazard analysis are consistent with observed frequencies of exceedance of ground motion intensity thresholds. Because the exceedance events of interest are typically rare with respect to the time span covered by data from seismic networks, a common approach underlying these studies is to pool observations from different sites. The main reason for this is to collect a sample large enough to convincingly perform a statistical analysis. However, this requires accounting for the dependence among the stochastic processes counting exceedances of ground motion intensity measures thresholds at different sites. Neglecting this dependence may lead to potentially fallacious conclusions about inadequateness of probabilistic seismic hazard. This study addresses this issue revisiting a hazard validation exercise for Italy, showing that accounting for this kind of spatial dependence can change the results of formal testing.
Spatial Allocator for air quality modeling
The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.
Spatial dependencies of wind power and interrelations with spot price dynamics
International Nuclear Information System (INIS)
Elberg, Christina; Hagspiel, Simeon
2013-01-01
Wind power has seen a strong growth over the last decade. Due to its high intermittency, spot prices have become more volatile and exhibit correlated behavior with wind power fed into the system. In this paper, we develop a stochastic simulation model that incorporates the spatial dependencies of wind power and its interrelations with spot prices: We employ a structural supply and demand based model for the electricity spot price that takes into account stochastic production quantities of wind power. Spatial dependencies are modeled with the help of copulas, thus linking the single turbine wind power to the aggregated wind power in a market. The model is applied to the German electricity market where wind power already today makes up a significant share of total power production. Revenue distributions and the market value of different wind power plants are analyzed. We find that the specific location of the considered wind turbine, i.e. its spatial dependency with respect to the aggregated wind power in the system, is of high relevance for its market value. Many of the analyzed locations show an upper tail dependence that adversely impacts the market value. This effect becomes more important for increasing levels of wind power penetration.
Evaluating spatial patterns in hydrological modelling
DEFF Research Database (Denmark)
Koch, Julian
is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...... the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...
Modeling structural change in spatial system dynamics: A Daisyworld example.
Neuwirth, C; Peck, A; Simonović, S P
2015-03-01
System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.
Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy
2014-10-01
The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.
A random spatial network model based on elementary postulates
Karlinger, Michael R.; Troutman, Brent M.
1989-01-01
A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.
Statistical inference and visualization in scale-space for spatially dependent images
Vaughan, Amy
2012-03-01
SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests for spatially dependent images. The spatial SiZer utilizes a family of kernel estimates of the image and provides not only exploratory data analysis but also statistical inference with spatial correlation taken into account. It is also capable of comparing the observed image with a specific null model being tested by adjusting the statistical inference using an assumed covariance structure. Pixel locations having statistically significant differences between the image and a given null model are highlighted by arrows. The spatial SiZer is compared with the existing independent SiZer via the analysis of simulated data with and without signal on both planar and spherical domains. We apply the spatial SiZer method to the decadal temperature change over some regions of the Earth. © 2011 The Korean Statistical Society.
Hypothesis Testing Using Spatially Dependent Heavy Tailed Multisensor Data
2014-12-01
of improper models, where tail-dependence was inadequately quantified, was considered to be one of the causes for the financial crisis of 2007-2008... disciplines such as machine learning, information theory, speech processing, finance, and aerospace, among others, has led to a rich body of...vol. 10, no. 7, pp. 215–218, Jul. 2003. [24] P. D. Ditlevsen, “Observation of ↵-stable noise induced millennial climate changes from an ice-core record
Panchromatic SED modelling of spatially resolved galaxies
Smith, Daniel J. B.; Hayward, Christopher C.
2018-05-01
We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.
A latent parameter node-centric model for spatial networks.
Directory of Open Access Journals (Sweden)
Nicholas D Larusso
Full Text Available Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social. We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models.
Spatial Visualization Abilities of Field Dependent/Independent Preservice Teachers
Yazici, Ersen
2014-01-01
Introduction: Spatial skills have been a significant area of research in educational psychology for more years and it has two major dimensions as spatial visualization and spatial orientation. Mathematics educators acknowledge the influence of cognitive styles in the learning of mathematics. There are various recognized cognitive styles in the…
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung
2013-08-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA
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Igor Bogunović
2016-06-01
Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.
Density-dependent home-range size revealed by spatially explicit capture–recapture
Efford, M.G.; Dawson, Deanna K.; Jhala, Y.V.; Qureshi, Q.
2016-01-01
The size of animal home ranges often varies inversely with population density among populations of a species. This fact has implications for population monitoring using spatially explicit capture–recapture (SECR) models, in which both the scale of home-range movements σ and population density D usually appear as parameters, and both may vary among populations. It will often be appropriate to model a structural relationship between population-specific values of these parameters, rather than to assume independence. We suggest re-parameterizing the SECR model using kp = σp √Dp, where kp relates to the degree of overlap between home ranges and the subscript p distinguishes populations. We observe that kp is often nearly constant for populations spanning a range of densities. This justifies fitting a model in which the separate kp are replaced by the single parameter k and σp is a density-dependent derived parameter. Continuous density-dependent spatial variation in σ may also be modelled, using a scaled non-Euclidean distance between detectors and the locations of animals. We illustrate these methods with data from automatic photography of tigers (Panthera tigris) across India, in which the variation is among populations, from mist-netting of ovenbirds (Seiurus aurocapilla) in Maryland, USA, in which the variation is within a single population over time, and from live-trapping of brushtail possums (Trichosurus vulpecula) in New Zealand, modelling spatial variation within one population. Possible applications and limitations of the methods are discussed. A model in which kp is constant, while density varies, provides a parsimonious null model for SECR. The parameter k of the null model is a concise summary of the empirical relationship between home-range size and density that is useful in comparative studies. We expect deviations from this model, particularly the dependence of kp on covariates, to be biologically interesting.
Landscape Modelling and Simulation Using Spatial Data
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Amjed Naser Mohsin AL-Hameedawi
2017-08-01
Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.
Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures
Huser, Raphaël
2017-06-23
Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.
Spatial Modeling for Resources Framework (SMRF)
Spatial Modeling for Resources Framework (SMRF) was developed by Dr. Scott Havens at the USDA Agricultural Research Service (ARS) in Boise, ID. SMRF was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed...
Testing spatial heterogeneity with stock assessment models
DEFF Research Database (Denmark)
Jardim, Ernesto; Eero, Margit; Silva, Alexandra
2018-01-01
This paper describes a methodology that combines meta-population theory and stock assessment models to gain insights about spatial heterogeneity of the meta-population in an operational time frame. The methodology was tested with stochastic simulations for different degrees of connectivity betwee...
The 3-D global spatial data model foundation of the spatial data infrastructure
Burkholder, Earl F
2008-01-01
Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...
Spatial and Temporal Clustering in a Simple Earthquake Asperity Model
Tiampo, K. F.; Kazemian, J.; Dominguez, R.; Klein, W.
2016-12-01
Natural earthquake fault systems are highly heterogeneous in space, the result of inhomogeneities that are a function of the variety of materials of different strengths. However, despite their inhomogeneous nature, real faults are often modeled as spatially homogeneous systems. Here we present a simple earthquake fault model based on the Olami-Feder-Christensen (OFC) and Rundle-Jackson-Brown (RJB) cellular automata models with long-range interactions that incorporates asperities, or stronger sites, into the lattice (Rundle and Jackson, 1977; Olami et al., 1992). These asperity cells are significantly stronger than the surrounding lattice sites but eventually rupture when the applied stress reaches their higher threshold stress. The introduction of these spatial heterogeneities results in spatial and temporal clustering in the model similar to that seen in natural fault systems. We observe sequences of activity that begin with a gradually accelerating number of larger events, or foreshocks, prior to a large event, followed by a tail of decreasing activity, or aftershocks. These recurrent large events occur at regular intervals and the characteristic time between events and their magnitude are a function of the stress dissipation parameter. The relative length of the foreshock to aftershock sequence depends on the amount of stress dissipation in the system. This work provides further evidence that the spatial and temporal patterns observed in natural seismicity are strongly influenced by the underlying physical properties and are not solely the result of a simple cascade mechanism. We find that the scaling depends not only on the amount of damage, but also on the spatial distribution of that damage (Dominguez et al., 2011; Kazemian et al., 2014). Here we compare the modeled sequences to those of natural earthquake sequences from California and around the world in order to investigate the interplay between cascade dynamics and spatial structure.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
. Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Modeling pairwise dependencies in precipitation intensities
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M. Vrac
2007-12-01
Full Text Available In statistics, extreme events are classically defined as maxima over a block length (e.g. annual maxima of daily precipitation or as exceedances above a given large threshold. These definitions allow the hydrologist and the flood planner to apply the univariate Extreme Value Theory (EVT to their time series of interest. But these strategies have two main drawbacks. Firstly, working with maxima or exceedances implies that a lot of observations (those below the chosen threshold or the maximum are completely disregarded. Secondly, this univariate modeling does not take into account the spatial dependence. Nearby weather stations are considered independent, although their recordings can show otherwise.
To start addressing these two issues, we propose a new statistical bivariate model that takes advantages of the recent advances in multivariate EVT. Our model can be viewed as an extension of the non-homogeneous univariate mixture. The two strong points of this latter model are its capacity at modeling the entire range of precipitation (and not only the largest values and the absence of an arbitrarily fixed large threshold to define exceedances. Here, we adapt this mixture and broaden it to the joint modeling of bivariate precipitation recordings. The performance and flexibility of this new model are illustrated on simulated and real precipitation data.
Supplementary Material for: Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel
2016-01-01
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.
Factor copula models for data with spatio-temporal dependence
Krupskii, Pavel
2017-10-13
We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.
Linking spatial and dynamic models for traffic maneuvers
DEFF Research Database (Denmark)
Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal
2015-01-01
For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...
Dinis da Costa, Daniel
2014-01-01
This article presents student-teachers' perceptions of spatial 3D-descriptive geometry education in Mozambique. To interpret a 3D object through 2D projections and vice-versa requires spatial abilities that are deemed crucial for learning in any spatially dependent discipline such as 3D-descriptive geometry, engineering and technical-vocational…
Convergence Hypothesis: Evidence from Panel Unit Root Test with Spatial Dependence
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Lezheng Liu
2006-10-01
Full Text Available In this paper we test the convergence hypothesis by using a revised 4- step procedure of panel unit root test suggested by Evans and Karras (1996. We use data on output for 24 OECD countries over 40 years long. Whether the convergence, if any, is conditional or absolute is also examined. According to a proposition by Baltagi, Bresson, and Pirotte (2005, we incorporate spatial autoregressive error into a fixedeffect panel model to account for not only the heterogeneous panel structure, but also spatial dependence, which might induce lower statistical power of conventional panel unit root test. Our empirical results indicate that output is converging among OECD countries. However, convergence is characterized as conditional. The results also report a relatively lower convergent speed compared to conventional panel studies.
Indoorgml - a Standard for Indoor Spatial Modeling
Li, Ki-Joune
2016-06-01
With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the
Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.
Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid
2015-11-07
Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2). Copyright © 2015 Elsevier Ltd. All rights reserved.
The quantitative modelling of human spatial habitability
Wise, J. A.
1985-01-01
A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).
Modeling mental spatial reasoning about cardinal directions.
Schultheis, Holger; Bertel, Sven; Barkowsky, Thomas
2014-01-01
This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. Copyright © 2014 Cognitive Science Society, Inc.
Spatial Database Modeling for Indoor Navigation Systems
Gotlib, Dariusz; Gnat, Miłosz
2013-12-01
For many years, cartographers are involved in designing GIS and navigation systems. Most GIS applications use the outdoor data. Increasingly, similar applications are used inside buildings. Therefore it is important to find the proper model of indoor spatial database. The development of indoor navigation systems should utilize advanced teleinformation, geoinformatics, geodetic and cartographical knowledge. The authors present the fundamental requirements for the indoor data model for navigation purposes. Presenting some of the solutions adopted in the world they emphasize that navigation applications require specific data to present the navigation routes in the right way. There is presented original solution for indoor data model created by authors on the basis of BISDM model. Its purpose is to expand the opportunities for use in indoor navigation.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species......When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... of different factors influence the time development of the system. This often makes it challenging to construct a mathematical model from which to draw conclusions. One traditional way of capturing the dynamics in a mathematical model is to formulate a set of coupled differential equations for the essential...
Temporal Modulation Detection Depends on Sharpness of Spatial Tuning.
Zhou, Ning; Cadmus, Matthew; Dong, Lixue; Mathews, Juliana
2018-04-25
Prior research has shown that in electrical hearing, cochlear implant (CI) users' speech recognition performance is related in part to their ability to detect temporal modulation (i.e., modulation sensitivity). Previous studies have also shown better speech recognition when selectively stimulating sites with good modulation sensitivity rather than all stimulation sites. Site selection based on channel interaction measures, such as those using imaging or psychophysical estimates of spread of neural excitation, has also been shown to improve speech recognition. This led to the question of whether temporal modulation sensitivity and spatial selectivity of neural excitation are two related variables. In the present study, CI users' modulation sensitivity was compared for sites with relatively broad or narrow neural excitation patterns. This was achieved by measuring temporal modulation detection thresholds (MDTs) at stimulation sites that were significantly different in their sharpness of the psychophysical spatial tuning curves (PTCs) and measuring MDTs at the same sites in monopolar (MP) and bipolar (BP) stimulation modes. Nine postlingually deafened subjects implanted with Cochlear Nucleus® device took part in the study. Results showed a significant correlation between the sharpness of PTCs and MDTs, indicating that modulation detection benefits from a more spatially restricted neural activation pattern. There was a significant interaction between stimulation site and mode. That is, using BP stimulation only improved MDTs at stimulation sites with broad PTCs but had no effect or sometimes a detrimental effect on MDTs at stimulation sites with sharp PTCs. This interaction could suggest that a criterion number of nerve fibers is needed to achieve optimal temporal resolution, and, to achieve optimized speech recognition outcomes, individualized selection of site-specific current focusing strategies may be necessary. These results also suggest that the removal of
Spatial Economics Model Predicting Transport Volume
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Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
A Computational Model of Spatial Development
Hiraki, Kazuo; Sashima, Akio; Phillips, Steven
Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.
Spherical Process Models for Global Spatial Statistics
Jeong, Jaehong
2017-11-28
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.
Latent spatial models and sampling design for landscape genetics
Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
2016-01-01
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
Yang, Wei; Sharp, Basil
2017-04-01
This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.
Osei, Frank B.; Duker, Alfred A.; Augustijn, Ellen-Wien; Stein, Alfred
2010-10-01
Cholera has been a public health burden in Ghana since the early 1970s. Between 1999 and 2005, a total of 25,636 cases and 620 deaths were officially reported to the WHO. In one of the worst affected urban cities, fecal contamination of surface water is extremely high, and the disease is reported to be prevalent among inhabitants living in close proximity to surface water bodies. Surface runoff from dump sites is a major source of fecal and bacterial contamination of rivers and streams in the study area. This study aims to determine (a) the impacts of surface water contamination on cholera infection and (b) detect and map arbitrary shaped clusters of cholera. A Geographic Information System (GIS) based spatial analysis is used to delineate potential reservoirs of the cholera vibrios; possibly contaminated by surface runoff from open space refuse dumps. Statistical modeling using OLS model reveals a significant negative association between (a) cholera prevalence and proximity to all the potential cholera reservoirs ( R2 = 0.18, p < 0.001) and (b) cholera prevalence and proximity to upstream potential cholera reservoirs ( R2 = 0.25, p < 0.001). The inclusion of spatial autoregressive coefficients in the OLS model reveals the dependency of the spatial distribution of cholera prevalence on the spatial neighbors of the communities. A flexible scan statistic identifies a most likely cluster with a higher relative risk (RR = 2.04, p < 0.01) compared with the cluster detected by circular scan statistic (RR = 1.60, p < 0.01). We conclude that surface water pollution through runoff from waste dump sites play a significant role in cholera infection.
Compositional dependability modeling using arcade
Stoelinga, Mariëlle Ida Antoinette; Huisman, Marieke
Dependability is a key concern for today's complex computer and communication systems. To make sure that such an application meets all its dependability requirements, a rigorous and systematic analysis is required. This talk introduces ARCADE, a formally well-rooted and extensible framework for
Stimulus-dependent effects on tactile spatial acuity
Directory of Open Access Journals (Sweden)
Tommerdahl M
2005-10-01
"bilateral" condition, in which 25 Hz flutter was delivered to the two points on the attended hand and a second stimulus (either flutter or vibration was delivered to the unattended hand. The two-point limen was reduced (i.e., spatial acuity was improved under the complex stimulus condition when compared to the control stimulus condition. Specifically, whereas adding vibration to the unilateral two-point flutter stimulus improved spatial acuity by 20 to 25%, the two-point limen was not significantly affected by substantial changes in stimulus amplitude (between 100 – 200 μm. In contrast, simultaneous stimulation of the unattended hand (contralateral to the attended site, impaired spatial acuity by 20% with flutter stimulation and by 30% with vibration stimulation. Conclusion It was found that the addition of 200 Hz vibration to a two-point 25 Hz flutter stimulus significantly improved a subject's ability to discriminate between two points on the skin. Since previous studies showed that 200 Hz vibration preferentially evokes activity in cortical area SII and reduces or inhibits the spatial extent of activity in SI in the same hemisphere, the findings in this paper raise the possibility that although SI activity plays a major role in two-point discrimination on the skin, influences relayed to SI from SII in the same hemisphere may contribute importantly to SI's ability to differentially respond to stimuli applied to closely spaced skin points on the same side of the body midline.
Spatial dependence of color assimilation by the watercolor effect.
Devinck, Frédéric; Delahunt, Peter B; Hardy, Joseph L; Spillmann, Lothar; Werner, John S
2006-01-01
Color assimilation with bichromatic contours was quantified for spatial extents ranging from von Bezold-type color assimilation to the watercolor effect. The magnitude and direction of assimilative hue change was measured as a function of the width of a rectangular stimulus. Assimilation was quantified by hue cancellation. Large hue shifts were required to null the color of stimuli < or = 9.3 min of arc in width, with an exponential decrease for stimuli increasing up to 7.4 deg. When stimuli were viewed through an achromatizing lens, the magnitude of the assimilation effect was reduced for narrow stimuli, but not for wide ones. These results demonstrate that chromatic aberration may account, in part, for color assimilation over small, but not large, surface areas.
The effect of spatial light modulator (SLM) dependent dispersion on spatial beam shaping
CSIR Research Space (South Africa)
Spangenberg, D-M
2013-08-01
Full Text Available . This introduces a phase difference between the different wavelengths of the light thereby causing the different wavelengths to disperse as it propagates through the medium. Spatial dispersion occurs when light with different wavelengths is incident on some mask... SLM and adjusts the wave front of light passing through it by no more than a few wavelengths. The combination of many pixels allows us to generate a mask which causes spatial dispersion to occur. The refractive index of the LC cells of the SLM has a...
Dutke, S.; Rinck, M.
2006-01-01
We investigated how the updating of spatial situation models during narrative comprehension depends on the interaction of cognitive abilities and text characteristics. Participants with low verbal and visuospatial abilities and participants with high abilities read narratives in which the
International Nuclear Information System (INIS)
Guerin, Bastien
2010-01-01
We developed and validated a fast Monte Carlo simulation of PET acquisitions based on the SimSET program modeling accurately the propagation of gamma photons in the patient as well as the block-based PET detector. Comparison of our simulation with another well validated code, GATE, and measurements on two GE Discovery ST PET scanners showed that it models accurately energy spectra (errors smaller than 4.6%), the spatial resolution of block-based PET scanners (6.1%), scatter fraction (3.5%), sensitivity (2.3%) and count rates (12.7%). Next, we developed a novel scatter correction incorporating the energy and position of photons detected in list-mode. Our approach is based on the reformulation of the list-mode likelihood function containing the energy distribution of detected coincidences in addition to their spatial distribution, yielding an EM reconstruction algorithm containing spatial and energy dependent correction terms. We also proposed using the energy in addition to the position of gamma photons in the normalization of the scatter sinogram. Finally, we developed a method for estimating primary and scatter photons energy spectra from total spectra detected in different sectors of the PET scanner. We evaluated the accuracy and precision of our new spatio-spectral scatter correction and that of the standard spatial correction using realistic Monte Carlo simulations. These results showed that incorporating the energy in the scatter correction reduces bias in the estimation of the absolute activity level by ∼ 60% in the cold regions of the largest patients and yields quantification errors less than 13% in all regions. (author)
SPATIALLY SELECTED SPECKLE-CORRELOMETRY OF TEMPERATURE DEPENDENT GELATION KINETICS
Directory of Open Access Journals (Sweden)
Anna A. Isaeva
2017-11-01
Full Text Available The paper presents the application of speckle correlometry method with the spatial ring filtration of back scattered field with the usage of localized radiation source for the study of dynamic thermally activated processes in gel-like structures containing submicron particles and nanoparticles. Speckle-modulated images contain information about the processes taking place inside the investigated medium; therefore, they are effectively used in biomedicine and materials science. The transformation process from lysol to gel was considered in media based on technical gelatin dissolved in water with weight fraction equal to 0.28% containing titanium dioxide nanoparticles TiO2 (volume fraction of particles is equal to 0.1% and 0.01% and media based on food gelatin dissolved in water with weight fraction equal to 0.3% containing titanium dioxide nanoparticles TiO2 (volume fraction of particles is equal to 0.01% and 0.01%. The temperature of the medium during the structural transformation of "sol-gel" system was changed from 50 to 25°C. To estimate the experimentally obtained distribution of space-time intensity fluctuations of backscattered speckle fields, the correlation analysis and the formalism of Kolmogorov structure functions were used. The estimations of activation temperatures for the “sol-gel” transition process for technical and food gelatin were obtained. This approach can be successfully applied for the study of dynamic systems, for example, the demonstration of Brownian particle movements.
Spatial Stochastic Point Models for Reservoir Characterization
Energy Technology Data Exchange (ETDEWEB)
Syversveen, Anne Randi
1997-12-31
The main part of this thesis discusses stochastic modelling of geology in petroleum reservoirs. A marked point model is defined for objects against a background in a two-dimensional vertical cross section of the reservoir. The model handles conditioning on observations from more than one well for each object and contains interaction between objects, and the objects have the correct length distribution when penetrated by wells. The model is developed in a Bayesian setting. The model and the simulation algorithm are demonstrated by means of an example with simulated data. The thesis also deals with object recognition in image analysis, in a Bayesian framework, and with a special type of spatial Cox processes called log-Gaussian Cox processes. In these processes, the logarithm of the intensity function is a Gaussian process. The class of log-Gaussian Cox processes provides flexible models for clustering. The distribution of such a process is completely characterized by the intensity and the pair correlation function of the Cox process. 170 refs., 37 figs., 5 tabs.
Theoretical aspects of spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...
Energy Technology Data Exchange (ETDEWEB)
Yang, Qing; Leung, Lai-Yung R.; Rauscher, Sara; Ringler, Todd; Taylor, Mark
2014-05-01
This study investigates the resolution dependency of precipitation extremes in an aqua-planet framework. Strong resolution dependency of precipitation extremes is seen over both tropics and extra-tropics, and the magnitude of this dependency also varies with dynamical cores. Moisture budget analyses based on aqua-planet simulations with the Community Atmosphere Model (CAM) using the Model for Prediction Across Scales (MPAS) and High Order Method Modeling Environment (HOMME) dynamical cores but the same physics parameterizations suggest that during precipitation extremes moisture supply for surface precipitation is mainly derived from advective moisture convergence. The resolution dependency of precipitation extremes mainly originates from advective moisture transport in the vertical direction. At most vertical levels over the tropics and in the lower atmosphere over the subtropics, the vertical eddy transport of mean moisture field dominates the contribution to precipitation extremes and its resolution dependency. Over the subtropics, the source of moisture, its associated energy, and the resolution dependency during extremes are dominated by eddy transport of eddies moisture at the mid- and upper-troposphere. With both MPAS and HOMME dynamical cores, the resolution dependency of the vertical advective moisture convergence is mainly explained by dynamical changes (related to vertical velocity or omega), although the vertical gradients of moisture act like averaging kernels to determine the sensitivity of the overall resolution dependency to the changes in omega at different vertical levels. The natural reduction of variability with coarser resolution, represented by areal data averaging (aggregation) effect, largely explains the resolution dependency in omega. The thermodynamic changes, which likely result from non-linear feedback in response to the large dynamical changes, are small compared to the overall changes in dynamics (omega). However, after excluding the
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Context dependent DNA evolutionary models
DEFF Research Database (Denmark)
Jensen, Jens Ledet
This paper is about stochastic models for the evolution of DNA. For a set of aligned DNA sequences, connected in a phylogenetic tree, the models should be able to explain - in probabilistic terms - the differences seen in the sequences. From the estimates of the parameters in the model one can...
Whittington, Jesse; Sawaya, Michael A
2015-01-01
Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth
Spatially explicit modelling of cholera epidemics
Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.
2013-12-01
Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.
Modeling strategic investment decisions in spatial markets
International Nuclear Information System (INIS)
Lorenczik, Stefan; Malischek, Raimund
2014-01-01
Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.
The Role of Visuo-Spatial Abilities in Recall of Spatial Descriptions: A Mediation Model
Meneghetti, Chiara; De Beni, Rossana; Pazzaglia, Francesca; Gyselinck, Valerie
2011-01-01
This research investigates how visuo-spatial abilities (such as mental rotation--MR--and visuo-spatial working memory--VSWM--) work together to influence the recall of environmental descriptions. We tested a mediation model in which VSWM was assumed to mediate the relationship between MR and spatial text recall. First, 120 participants were…
SPATIALLY DEPENDENT HEATING AND IONIZATION IN AN ICME OBSERVED BY BOTH ACE AND ULYSSES
Energy Technology Data Exchange (ETDEWEB)
Lepri, Susan T. [Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI 48109-2143 (United States); Laming, J. Martin; Rakowski, Cara E. [Space Science Division, Naval Research Laboratory, Code 7674L, Washington, DC 20375-5321 (United States); Von Steiger, Rudolf [International Space Science Institute, Bern CH-3012 (Switzerland)
2012-12-01
The 2005 January 21 interplanetary coronal mass ejection (ICME) observed by multiple spacecraft at L1 was also observed from January 21-February 4 at Ulysses (5.3 AU). Previous studies of this ICME have found evidence suggesting that the flanks of a magnetic cloud like structure associated with this ICME were observed at L1 while a more central cut through the associated magnetic cloud was observed at Ulysses. This event allows us to study spatial variation across the ICME and relate it to the eruption at the Sun. In order to examine the spatial dependence of the heating in this ICME, we present an analysis and comparison of the heavy ion composition observed during the passage of the ICME at L1 and at Ulysses. Using SWICS, we compare the heavy ion composition across the two different observation cuts through the ICME and compare it with predictions for heating during the eruption based on models of the time-dependent ionization balance throughout the event.
Spatial Situation Models and Text Comprehension.
Haenggi, Dieter; And Others
1995-01-01
Reports findings from three experiments designed to show how readers inferred spatial information relevant to a story character's movements through a previously memorized layout of a fictional building. Examines how inference measures are related to spatial imagery. (HB)
A physically based analytical spatial air temperature and humidity model
Yang Yang; Theodore A. Endreny; David J. Nowak
2013-01-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...
Spectral Modelling for Spatial Network Analysis
Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela
2016-01-01
Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J
2010-12-01
Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies
Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
Directory of Open Access Journals (Sweden)
Xiaodong Huang
Full Text Available Nitrous oxide (N2O is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2 field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.
Spatial Data Web Services Pricing Model Infrastructure
Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.
2013-08-01
most important law with related NSDI is the establishment of General Directorate of Geographic Information System under the Ministry of Environment and Urbanism. due to; to do or to have do works and activities with related to the establishment of National Geographic Information Systems (NGIS), usage of NGIS and improvements of NGIS. Outputs of these projects are served to not only public administration but also to Turkish society. Today for example, TAKBIS data (cadastre services) are shared more than 50 institutions by Web services, Tusaga-Aktif system has more than 3800 users who are having real-time GPS data correction, Orthophoto WMS services has been started for two years as a charge of free. Today there is great discussion about data pricing among the institutions. Some of them think that the pricing is storage of the data. Some of them think that the pricing is value of data itself. There is no certain rule about pricing. On this paper firstly, pricing of data storage and later on spatial data pricing models in different countries are investigated to improve institutional understanding in Turkey.
Reducing Spatial Data Complexity for Classification Models
International Nuclear Information System (INIS)
Ruta, Dymitr; Gabrys, Bogdan
2007-01-01
Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the
Spatial structure arising from neighbour-dependent bias in collective cell movement.
Binny, Rachelle N; Haridas, Parvathi; James, Alex; Law, Richard; Simpson, Matthew J; Plank, Michael J
2016-01-01
Mathematical models of collective cell movement often neglect the effects of spatial structure, such as clustering, on the population dynamics. Typically, they assume that individuals interact with one another in proportion to their average density (the mean-field assumption) which means that cell-cell interactions occurring over short spatial ranges are not accounted for. However, in vitro cell culture studies have shown that spatial correlations can play an important role in determining collective behaviour. Here, we take a combined experimental and modelling approach to explore how individual-level interactions give rise to spatial structure in a moving cell population. Using imaging data from in vitro experiments, we quantify the extent of spatial structure in a population of 3T3 fibroblast cells. To understand how this spatial structure arises, we develop a lattice-free individual-based model (IBM) and simulate cell movement in two spatial dimensions. Our model allows an individual's direction of movement to be affected by interactions with other cells in its neighbourhood, providing insights into how directional bias generates spatial structure. We consider how this behaviour scales up to the population level by using the IBM to derive a continuum description in terms of the dynamics of spatial moments. In particular, we account for spatial correlations between cells by considering dynamics of the second spatial moment (the average density of pairs of cells). Our numerical results suggest that the moment dynamics description can provide a good approximation to averaged simulation results from the underlying IBM. Using our in vitro data, we estimate parameters for the model and show that it can generate similar spatial structure to that observed in a 3T3 fibroblast cell population.
A spatial error model with continuous random effects and an application to growth convergence
Laurini, Márcio Poletti
2017-10-01
We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.
Spatial dependence in wind and optimal wind power allocation: A copula-based analysis
International Nuclear Information System (INIS)
Grothe, Oliver; Schnieders, Julius
2011-01-01
The investment decision on the placement of wind turbines is, neglecting legal formalities, mainly driven by the aim to maximize the expected annual energy production of single turbines. The result is a concentration of wind farms at locations with high average wind speed. While this strategy may be optimal for single investors maximizing their own return on investment, the resulting overall allocation of wind turbines may be unfavorable for energy suppliers and the economy because of large fluctuations in the overall wind power output. This paper investigates to what extent optimal allocation of wind farms in Germany can reduce these fluctuations. We analyze stochastic dependencies of wind speed for a large data set of German on- and offshore weather stations and find that these dependencies turn out to be highly nonlinear but constant over time. Using copula theory we determine the value at risk of energy production for given allocation sets of wind farms and derive optimal allocation plans. We find that the optimized allocation of wind farms may substantially stabilize the overall wind energy supply on daily as well as hourly frequency. - Highlights: → Spatial modeling of wind forces in Germany. → A novel way to assess nonlinear dependencies of wind forces by copulas. → Wind turbine allocation by maximizing lower quantiles of energy production. → Optimal results show major increase in reliable part of wind energy.
and density-dependent quark mass model
Indian Academy of Sciences (India)
We report on the study of the mass–radius (–) relation and the radial oscillations of magnetized proto strange stars. For the quark matter we have employed the very recent modiﬁcation, the temperature- and density-dependent quark mass model of the well-known density-dependent quark mass model. We ﬁnd that the ...
Directory of Open Access Journals (Sweden)
Kostas Alexandridis
2013-06-01
Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.
Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.
2014-11-01
A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.
Spatial Econometric data analysis: moving beyond traditional models
Florax, R.J.G.M.; Vlist, van der A.J.
2003-01-01
This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling
Using IBMs to Investigate Spatially-dependent Processes in Landscape Genetics Theory
Much of landscape and conservation genetics theory has been derived using non-spatialmathematical models. Here, we use a mechanistic, spatially-explicit, eco-evolutionary IBM to examine the utility of this theoretical framework in landscapes with spatial structure. Our analysis...
A gender- and sexual orientation-dependent spatial attentional effect of invisible images
Jiang, Yi; Costello, Patricia; Fang, Fang; Huang, Miner; He, Sheng
2006-01-01
Human observers are constantly bombarded with a vast amount of information. Selective attention helps us to quickly process what is important while ignoring the irrelevant. In this study, we demonstrate that information that has not entered observers' consciousness, such as interocularly suppressed (invisible) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their ...
Time dependent analysis of Xenon spatial oscillations in small power reactors
International Nuclear Information System (INIS)
Decco, Claudia Cristina Ghirardello
1997-01-01
This work presents time dependent analysis of xenon spatial oscillations studying the influence of the power density distribution, type of reactivity perturbation, power level and core size, using the one-dimensional and three-dimensional analysis with the MID2 and citation codes, respectively. It is concluded that small pressurized water reactors with height smaller than 1.5 m are stable and do not have xenon spatial oscillations. (author)
Alaskan soil carbon stocks: spatial variability and dependence on environmental factors
Directory of Open Access Journals (Sweden)
U. Mishra
2012-09-01
Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m^{−2}, 2–166 kg m^{−2}, and 0–232 kg m^{−2}, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high-resolution mapping of soil carbon stock reveals the
Spatially explicit fate modelling of nanomaterials in natural waters
Quik, J.T.K.; Klein, de J.J.M.; Koelmans, A.A.
2015-01-01
Site specific exposure assessments for engineered nanoparticles (ENPs) require spatially explicit fate models, which however are not yet available. Here we present an ENP fate model (NanoDUFLOW) that links ENP specific process descriptions to a spatially explicit hydrological model. The link enables
Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence
Directory of Open Access Journals (Sweden)
Liying Wang
2017-01-01
Full Text Available Star repairable systems with spatial dependence consist of a center component and several peripheral components. The peripheral components are arranged around the center component, and the performance of each component depends on its spatial “neighbors.” Vector-Markov process is adapted to describe the performance of the system. The state space and transition rate matrix corresponding to the 6-component star Markov repairable system with spatial dependence are presented via probability analysis method. Several reliability indices, such as the availability, the probabilities of visiting the safety, the degradation, the alert, and the failed state sets, are obtained by Laplace transform method and a numerical example is provided to illustrate the results.
Modeling spatial variation in avian survival and residency probabilities
Saracco, James F.; Royle, J. Andrew; DeSante, David F.; Gardner, Beth
2010-01-01
The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.
Spatial data modelling and maximum entropy theory
Czech Academy of Sciences Publication Activity Database
Klimešová, Dana; Ocelíková, E.
2005-01-01
Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information
Shen, Yuan; Mayhew, Stephen D; Kourtzi, Zoe; Tiňo, Peter
2014-01-01
Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's "region of influence" through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the
OECD/NEA benchmark for time-dependent neutron transport calculations without spatial homogenization
Energy Technology Data Exchange (ETDEWEB)
Hou, Jason, E-mail: jason.hou@ncsu.edu [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Ivanov, Kostadin N. [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Boyarinov, Victor F.; Fomichenko, Peter A. [National Research Centre “Kurchatov Institute”, Kurchatov Sq. 1, Moscow (Russian Federation)
2017-06-15
Highlights: • A time-dependent homogenization-free neutron transport benchmark was created. • The first phase, known as the kinetics phase, was described in this work. • Preliminary results for selected 2-D transient exercises were presented. - Abstract: A Nuclear Energy Agency (NEA), Organization for Economic Co-operation and Development (OECD) benchmark for the time-dependent neutron transport calculations without spatial homogenization has been established in order to facilitate the development and assessment of numerical methods for solving the space-time neutron kinetics equations. The benchmark has been named the OECD/NEA C5G7-TD benchmark, and later extended with three consecutive phases each corresponding to one modelling stage of the multi-physics transient analysis of the nuclear reactor core. This paper provides a detailed introduction of the benchmark specification of Phase I, known as the “kinetics phase”, including the geometry description, supporting neutron transport data, transient scenarios in both two-dimensional (2-D) and three-dimensional (3-D) configurations, as well as the expected output parameters from the participants. Also presented are the preliminary results for the initial state 2-D core and selected transient exercises that have been obtained using the Monte Carlo method and the Surface Harmonic Method (SHM), respectively.
Ermolieva, T.Y.; Fischer, G.; Obersteiner, M.
2003-01-01
This paper discusses an integrated model capable of dealing with spatial and temporal heterogeneities induced by extreme events, in particular weather related catastrophes. The model can be used for quite different problems which take explicitly into account the specifics of catastrophic risks: highly mutually dependent losses, inherent capacity of information, the need for long-term perspectives (temporal heterogeneity) and geographically explicit analyses (spatial heterogeneity) with respec...
Adaptive Gaussian Predictive Process Models for Large Spatial Datasets
Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.
2011-01-01
Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952
Topological models and frameworks for 3D spatial objects
Zlatanova, Siyka; Rahman, Alias Abdul; Shi, Wenzhong
2004-05-01
Topology is one of the mechanisms to describe relationships between spatial objects. Thus, it is the basis for many spatial operations. Models utilizing the topological properties of spatial objects are usually called topological models, and are considered by many researchers as the best suited for complex spatial analysis (i.e., the shortest path search). A number of topological models for two-dimensional and 2.5D spatial objects have been implemented (or are under consideration) by GIS and DBMS vendors. However, when we move to one more dimension (i.e., three-dimensions), the complexity of the relationships increases, and this requires new approaches, rules and representations. This paper aims to give an overview of the 3D topological models presented in the literature, and to discuss generic issues related to 3D modeling. The paper also considers models in object-oriented (OO) environments. Finally, future trends for research and development in this area are highlighted.
Modeling multisite streamflow dependence with maximum entropy copula
Hao, Z.; Singh, V. P.
2013-10-01
Synthetic streamflows at different sites in a river basin are needed for planning, operation, and management of water resources projects. Modeling the temporal and spatial dependence structure of monthly streamflow at different sites is generally required. In this study, the maximum entropy copula method is proposed for multisite monthly streamflow simulation, in which the temporal and spatial dependence structure is imposed as constraints to derive the maximum entropy copula. The monthly streamflows at different sites are then generated by sampling from the conditional distribution. A case study for the generation of monthly streamflow at three sites in the Colorado River basin illustrates the application of the proposed method. Simulated streamflow from the maximum entropy copula is in satisfactory agreement with observed streamflow.
Uncertainties in spatially aggregated predictions from a logistic regression model
Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.
2002-01-01
This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model, i.e. each new point is generated given the previous points. Under this model...... points is such that the dependent cluster point is likely to occur closely to a previous cluster point. We demonstrate the flexibility of the model for producing point patterns with linear structures, and propose to use the model as the likelihood in a Bayesian setting when analyzing a spatial point...... pattern exhibiting linear structures but where the exact mechanism responsible for the formations of lines is unknown. We illustrate this methodology by analyzing two spatial point pattern data sets (locations of bronze age graves in Denmark and locations of mountain tops in Spain) without knowing which...
Getis, Arthur
1997-01-01
In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.
Spatial assignment of emissions using a new locomotive emissions model.
Gould, Gregory M; Niemeier, Deb A
2011-07-01
Estimates of fuel use and air pollutant emissions from freight rail currently rely highly on aggregate methods and largely obsolete data which offer little insight into contemporary air quality problems. Because the freight industry is for the most part privately held and data are closely guarded for competitive reasons, the challenge is to produce robust estimates using current reporting requirements, while accurately portraying the spatial nature of freight rail impacts. This research presents a new spatially resolved model for estimating air pollutant emissions (hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter less than 10 μm in diameter, sulfur dioxide, and carbon dioxide) from locomotives. Emission estimates are based on track segment level data including track grade, type of train traffic (bulk, intermodal, or manifest) and the local locomotive fleet (EPA tier certification level and fuel efficiency). We model the California Class I freight rail system and compare our results to regional estimates from the California Air Resources Board and to estimates following U.S. Environmental Protection Agency guidance. We find that our results vary considerably from the other methods depending on the region or corridor analyzed. We also find large differences in fuel and emission intensity for individual rail corridors.
Need for Space: The Key Distance Effect Depends on Spatial Stimulus Configurations
Stephan, Julia; Franz, Volker H.
2014-01-01
In numerous psychological experiments, participants classify stimuli by pressing response keys. According to Lakens, Schneider, Jostmann, and Schubert (2011), classification performance is affected by physical distance between response keys – indicating a cognitive tendency to represent categories in spatial code. However, previous evidence for a key distance effect (KDE) from a color-naming Stroop task is inconclusive as to whether: (a) key separation automatically leads to an internal spatial representation of non-spatial stimulus characteristics in participants, or if the KDE rather depends on physical spatial characteristics of the stimulus configuration; (b) the KDE attenuates the Stroop interference effect. We therefore first adopted the original Stroop task in Experiment 1, confirming that wider key distance facilitated responses, but did not modulate the Stroop effect as was previously found. In Experiments 2 and 3 we controlled potential mediator variables in the original design. When we did not display instructions about stimulus-response mappings, thereby removing the unintended spatial context from the Stroop stimuli, no KDE emerged. Presenting the instructions at a central position in Experiment 4 confirmed that key separation alone is not sufficient for a KDE, but correspondence between spatial configurations of stimuli and responses is also necessary. Evidence indicates that the KDE on Stroop performance is due to known mechanisms of stimulus-response compatibility and response discriminability. The KDE does, however, not demonstrate a general disposition to represent any stimulus in spatial code. PMID:24642888
Bayesian spatial modeling of HIV mortality via zero-inflated Poisson models.
Musal, Muzaffer; Aktekin, Tevfik
2013-01-30
In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications. Copyright © 2012 John Wiley & Sons, Ltd.
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...
A spatially structured metapopulation model within a stochastic environment.
Smith, Andrew G
2017-09-01
Populations often exist, either by choice or by external pressure, in a fragmented way, referred to as a metapopulation. Typically, the dynamics accounted for within metapopulation models are assumed to be static. For example, patch occupancy models often assume that the colonisation and extinction rates do not change, while spatially structured models often assume that the rates of births, deaths and migrations do not depend on time. While some progress has been made when these dynamics are changing deterministically, less is known when the changes are stochastic. It can be quite common that the environment a population inhabits determines how these dynamics change over time. Changes to this environment can have a large impact on the survival probability of a population and such changes will often be stochastic. The typical metapopulation model allows for catastrophes that could eradicate most, if not all, individuals on an entire patch. It is this type of phenomenon that this article addresses. A Markov process is developed that models the number of individuals on each patch within a metapopulation. An approximation for the original model is presented in the form of a piecewise-deterministic Markov process and the approximation is analysed to present conditions for extinction. Copyright © 2017 Elsevier Inc. All rights reserved.
Spatial and Temporal Low-Dimensional Models for Fluid Flow
Kalb, Virginia
2008-01-01
A document discusses work that obtains a low-dimensional model that captures both temporal and spatial flow by constructing spatial and temporal four-mode models for two classic flow problems. The models are based on the proper orthogonal decomposition at two reference Reynolds numbers. Model predictions are made at an intermediate Reynolds number and compared with direct numerical simulation results at the new Reynolds number.
Spatial memory impairments in a prediabetic rat model.
Soares, E; Prediger, R D; Nunes, S; Castro, A A; Viana, S D; Lemos, C; De Souza, C M; Agostinho, P; Cunha, R A; Carvalho, E; Fontes Ribeiro, C A; Reis, F; Pereira, F C
2013-10-10
Diabetes is associated with an increased risk for brain disorders, namely cognitive impairments associated with hippocampal dysfunction underlying diabetic encephalopathy. However, the impact of a prediabetic state on cognitive function is unknown. Therefore, we now investigated whether spatial learning and memory deficits and the underlying hippocampal dysfunction were already present in a prediabetic animal model. Adult Wistar rats drinking high-sucrose (HSu) diet (35% sucrose solution during 9 weeks) were compared to controls' drinking water. HSu rats exhibited fasting normoglycemia accompanied by hyperinsulinemia and hypertriglyceridemia in the fed state, and insulin resistance with impaired glucose tolerance confirming them as a prediabetic rodent model. HSu rats displayed a poorer performance in hippocampal-dependent short- and long-term spatial memory performance, assessed with the modified Y-maze and Morris water maze tasks, respectively; this was accompanied by a reduction of insulin receptor-β density with normal levels of insulin receptor substrate-1 pSer636/639, and decreased hippocampal glucocorticoid receptor levels without changes of the plasma corticosterone levels. Importantly, HSu animals exhibited increased hippocampal levels of AMPA and NMDA receptor subunits GluA1 and GLUN1, respectively, whereas the levels of protein markers related to nerve terminals (synaptophysin) and oxidative stress/inflammation (HNE, RAGE, TNF-α) remained unaltered. These findings indicate that 9 weeks of sucrose consumption resulted in a metabolic condition suggestive of a prediabetic state, which translated into short- and long-term spatial memory deficits accompanied by alterations in hippocampal glutamatergic neurotransmission and abnormal glucocorticoid signaling. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
Spatial modeling of potential woody biomass flow
Woodam Chung; Nathaniel Anderson
2012-01-01
The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...
Neutrino flavor instabilities in a time-dependent supernova model
Directory of Open Access Journals (Sweden)
Sajad Abbar
2015-12-01
Full Text Available A dense neutrino medium such as that inside a core-collapse supernova can experience collective flavor conversion or oscillations because of the neutral-current weak interaction among the neutrinos. This phenomenon has been studied in a restricted, stationary supernova model which possesses the (spatial spherical symmetry about the center of the supernova and the (directional axial symmetry around the radial direction. Recently it has been shown that these spatial and directional symmetries can be broken spontaneously by collective neutrino oscillations. In this letter we analyze the neutrino flavor instabilities in a time-dependent supernova model. Our results show that collective neutrino oscillations start at approximately the same radius in both the stationary and time-dependent supernova models unless there exist very rapid variations in local physical conditions on timescales of a few microseconds or shorter. Our results also suggest that collective neutrino oscillations can vary rapidly with time in the regimes where they do occur which need to be studied in time-dependent supernova models.
Spatial Fleming-Viot models with selection and mutation
Dawson, Donald A
2014-01-01
This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Badger, Daniel; Barnden, Leighton
2014-09-01
The aim of this study is to determine the dependence of single photon emission computed tomography (SPECT) spatial resolution on the content of images for iterative reconstruction with distance dependent resolution (DDR) correction. An experiment was performed using a perturbation technique to measure change in resolution of line sources in simple and complex images with iterative reconstruction with increasing iteration. Projections of the line sources were reconstructed alone and again after the addition of projections of a uniform flood or a complex phantom. An alternative experiment used images of a realistic brain phantom and evaluated an effective spatial resolution by matching the images to the digital version of the phantom convolved with 3D Gaussian kernels. The experiments were performed using ordered subset expectation maximisation iterative reconstruction with and without the use of DDR correction. The results show a significant difference in reconstructed resolution between images of line sources depending on the content of the added image. The full width at half maximum of images of a line source reconstructed using DDR correction increased by 20-30 % when the added image was complex. Without DDR this difference was much smaller and disappeared with increasing iteration. Reported SPECT resolution should be taken as indicative only with regard to clinical imaging if the measurement is made using a point or line source alone and an iterative reconstruction algorithm is used.
Modelling dependable systems using hybrid Bayesian networks
International Nuclear Information System (INIS)
Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter
2008-01-01
A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems
Free-streaming radiation in cosmological models with spatial curvature
Wilson, M. L.
1982-01-01
The effects of spatial curvature on radiation anisotropy are examined for the standard Friedmann-Robertson-Walker model universes. The effect of curvature is found to be very important when considering fluctuations with wavelengths comparable to the horizon. It is concluded that the behavior of radiation fluctuations in models with spatial curvature is quite different from that in spatially flat models, and that models with negative curvature are most strikingly different. It is therefore necessary to take the curvature into account in careful studies of the anisotropy of the microwave background.
Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics
Ningrum, Windy Setia; Widyaningsih, Yekti; Indra, Tito Latif
2017-03-01
The Citarum watershed is the longest and the largest watershed in West Java, Indonesia, located at 106°51'36''-107°51' E and 7°19'-6°24'S across 10 districts, and serves as the water supply for over 15 million people. In this area, the water criticality index is concerned to reach the balance between water supply and water demand, so that in the dry season, the watershed is still able to meet the water needs of the society along the Citarum river. The objective of this research is to evaluate the water criticality index of Citarum watershed area using spatial model to overcome the spatial dependencies in the data. The result of Lagrange multiplier diagnostics for spatial dependence results are LM-err = 34.6 (p-value = 4.1e-09) and LM-lag = 8.05 (p-value = 0.005), then modeling using Spatial Lag Model (SLM) and Spatial Error Model (SEM) were conducted. The likelihood ratio test show that both of SLM dan SEM model is better than OLS model in modeling water criticality index in Citarum watershed. The AIC value of SLM and SEM model are 78.9 and 51.4, then the SEM model is better than SLM model in predicting water criticality index in Citarum watershed.
Directory of Open Access Journals (Sweden)
Cheng-Xiang Wang
2007-02-01
Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.
Pattern formation through spatial interactions in a modified Daisyworld model
Alberti, Tommaso; Primavera, Leonardo; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo
2015-04-01
The Daisyworld model is based on a hypothetical planet, like the Earth, which receives the radiant energy coming from a Sun-like star, and populated by two kinds of identical plants differing by their colour: white daisies reflecting light and black daisies absorbing light. The interactions and feedbacks between the collective biota of the planet and the incoming radiation form a self-regulating system where the conditions for life are maintained. We investigate a modified version of the Daisyworld model where a spatial dependency on latitude is introduced, and both a variable heat diffusivity along latitude and a simple greenhouse model are included. We show that the spatial interactions between the variables of the system can generate some equilibrium patterns which can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate new equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions. The extension to spatial Daisyworld gives room to the possibility of inhomogeneous solar forcing in a curved planet, with explicit differences between poles and equator and the direct use of the heat diffusion equation. As a first approach, to describe a spherical planet, we consider the temperature T(θ,t) and the surface coverage as depending only on time and on latitude θ (-90° ≤ θ ≤ 90°). A second step is the introduction of the greenhouse effect in the model, the process by which outgoing infrared radiation is partly screened by greenhouse gases. This effect can be described by relaxing the black-body radiation hypothesis and by introducing a grayness function g(T) in the heat equation. As a third step, we consider a latitude dependence of the Earth's conductivity, χ = χ(θ). Considering these terms, using spherical coordinates and symmetry with respect to θ, the modified Daisyworld equations reduce to the
International Nuclear Information System (INIS)
Yoriyaz, H.
1986-01-01
In this work a spatial burnup scheme and feedback effects has been implemented into the FERM ( 'Finite Element Response Matrix' )program. The spatially dependent neutronic parameters have been considered in three levels: zonewise calculation, assembly wise calculation and pointwise calculation. Flux and power distributions and the multiplication factor were calculated and compared with the results obtained by CITATIOn program. These comparisons showed that processing time in the Ferm code has been hundred of times shorter and no significant difference has been observed in the assembly average power distribution. (Author) [pt
Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor
Directory of Open Access Journals (Sweden)
Jae-Han Park
2012-06-01
Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Spatial uncertainty model for visual features using a Kinect™ sensor.
Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong
2012-01-01
This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Directory of Open Access Journals (Sweden)
Chengcheng Xu
2017-08-01
Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model
Russell A. Parsons
2006-01-01
Efforts to quantitatively evaluate the effectiveness of fuels treatments are hampered by inconsistencies between the spatial scale at which fuel treatments are implemented and the spatial scale, and detail, with which we model fire and fuel interactions. Central to this scale inconsistency is the resolution at which variability within the fuel bed is considered. Crown...
Stochastic Spatial Models in Ecology: A Statistical Physics Approach
Pigolotti, Simone; Cencini, Massimo; Molina, Daniel; Muñoz, Miguel A.
2017-11-01
Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.
A Spatial Model for the Instantaneous Estimation of Wind Power at a Large Number of Unobserved Sites
DEFF Research Database (Denmark)
Lenzi, Amanda; Guillot, Gilles; Pinson, Pierre
2015-01-01
We propose a hierarchical Bayesian spatial model to obtain predictive densities of wind power at a set of un-monitored locations. The model consists of a mixture of Gamma density for the non-zero values and degenerated distributions at zero. The spatial dependence is described through a common Ga...
Updates to the Demographic and Spatial Allocation Models to ...
EPA announced the availability of the draft report, Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) for a 30-day public comment period. The ICLUS version 2 (v2) modeling tool furthered land change modeling by providing nationwide housing development scenarios up to 2100. ICLUS V2 includes updated population and land use data sets and addressing limitations identified in ICLUS v1 in both the migration and spatial allocation models. The companion user guide describes the development of ICLUS v2 and the updates that were made to the original data sets and the demographic and spatial allocation models. [2017 UPDATE] Get the latest version of ICLUS and stay up-to-date by signing up to the ICLUS mailing list. The GIS tool enables users to run SERGoM with the population projections developed for the ICLUS project and allows users to modify the spatial allocation housing density across the landscape.
An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun
2006-01-01
The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...... and can help reduce the complexity of algorithms making them easier to maintain and develop. This paper propose an object-oriented API for context models of the physical environment and extensions to a location modeling approach called geometric space trees for it to provide adequate support for location...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms....
Gustafson, E.J.; Knutson, M.G.; Niemi, G.J.; Friberg, M.
2002-01-01
We constructed alternative spatial models at two scales to predict Brown-headed Cowbird (Molothrus ater) parasitism rates from land cover maps. The local-scale models tested competing hypotheses about the relationship between cowbird parasitism and distance of host nests from a forest edge (forest-nonforest boundary). The landscape models tested competing hypotheses about how landscape features (e.g., forests, agricultural fields) interact to determine rates of cowbird parasitism. The models incorporate spatial neighborhoods with a radius of 2.5 km in their formulation, reflecting the scale of the majority of cowbird commuting activity. Field data on parasitism by cowbirds (parasitism rate and number of cowbird eggs per nest) were collected at 28 sites in the Driftless Area Ecoregion of Wisconsin, Minnesota, and Iowa and were compared to the predictions of the alternative models. At the local scale, there was a significant positive relationship between cowbird parasitism and mean distance of nest sites from the forest edge. At the landscape scale, the best fitting models were the forest-dependent and forest-fragmentation-dependent models, in which more heavily forested and less fragmented landscapes had higher parasitism rates. However, much of the explanatory power of these models results from the inclusion of the local-scale relationship in these models. We found lower rates of cowbird parasitism than did most Midwestern studies, and we identified landscape patterns of cowbird parasitism that are opposite to those reported in several other studies of Midwestern songbirds. We caution that cowbird parasitism patterns can be unpredictable, depending upon ecoregional location and the spatial extent, and that our models should be tested in other ecoregions before they are applied there. Our study confirms that cowbird biology has a strong spatial component, and that improved spatial models applied at multiple spatial scales will be required to predict the effects of
MODELING PRECIPITATION DEPENDENT FOREST RESILIENCE IN INDIA
Directory of Open Access Journals (Sweden)
P. Das
2018-04-01
Full Text Available The impact of long term climate change that imparts stress on forest could be perceived by studying the regime shift of forest ecosystem. With the change of significant precipitation, forest may go through density change around globe at different spatial and temporal scale. The 100 class high resolution (60 meter spatial resolution Indian vegetation type map was used in this study recoded into four broad categories depending on phrenology as (i forest, (ii scrubland, (iii grassland and (iv treeless area. The percentage occupancy of forest, scrub, grass and treeless were observed as 19.9 %, 5.05 %, 1.89 % and 7.79 % respectively. Rest of the 65.37 % land area was occupied by the cropland, built-up, water body and snow covers. The majority forest cover were appended into a 5 km × 5 km grid, along with the mean annual precipitation taken from Bioclim data. The binary presence and absence of different vegetation categories in relates to the annual precipitation was analyzed to calculate their resilience expressed in probability values ranging from 0 to 1. Forest cover observed having resilience probability (Pr < 0.3 in only 0.3 % (200 km2 of total forest cover in India, which was 4.3 % < 0.5 Pr. Majority of the scrubs and grass (64.92 % Pr < 0.5 from North East India which were the shifting cultivation lands showing low resilience, having their high tendency to be transform to forest. These results have spatial explicitness to highlight the resilient and non-resilient distribution of forest, scrub and grass, and treeless areas in India.
Applications of spatial statistical network models to stream data
Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal
2014-01-01
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas...
Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.
2015-01-01
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Integrating models that depend on variable data
Banks, A. T.; Hill, M. C.
2016-12-01
Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log
Directory of Open Access Journals (Sweden)
Karsten Schönrogge
Full Text Available Revealing the interactions between alien species and native communities is central to understanding the ecological consequences of range expansion. Much has been learned through study of the communities developing around invading herbivorous insects. Much less, however, is known about the significance of such aliens for native vertebrate predators for which invaders may represent a novel food source. We quantified spatial patterns in native bird predation of invading gall-inducing Andricus wasps associated with introduced Turkey oak (Quercus cerris at eight sites across the UK. These gallwasps are available at high density before the emergence of caterpillars that are the principle spring food of native insectivorous birds. Native birds showed positive spatial density dependence in gall attack rates at two sites in southern England, foraging most extensively on trees with highest gall densities. In a subsequent study at one of these sites, positive spatial density dependence persisted through four of five sequential week-long periods of data collection. Both patterns imply that invading galls are a significant resource for at least some native bird populations. Density dependence was strongest in southern UK bird populations that have had longest exposure to the invading gallwasps. We hypothesise that this pattern results from the time taken for native bird populations to learn how to exploit this novel resource.
Voutilainen, Ari; Tolppanen, Anna-Maija; Vehviläinen-Julkunen, Katri; Sherwood, Paula R
2014-01-01
Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165(th) PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r (2) = 0.579). PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of
How does spatial study design influence density estimates from spatial capture-recapture models?
Directory of Open Access Journals (Sweden)
Rahel Sollmann
Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.
Spatial Modeling Tools for Cell Biology
2006-10-01
of the cells total volume. The cytosol contains thousands of enzymes that are responsible for the catalyzation of glycolysis and gluconeogenesis ... dog , swine and pig models [Pantely, 1990, 1991; Stanley 1992]. In these studies, blood flow through the left anterior descending (LAD) coronary...perfusion. In conclusion, even thought our model falls within the (rather large) error bounds of experimental dog , pig and swine models, the
Directory of Open Access Journals (Sweden)
Jianmin Liu
2016-01-01
Full Text Available Based on panel data covering the period from 2003 to 2012 in China’s 281 prefecture-level cities, we use superefficiency SBM model to measure regional financial efficiency and empirically test the spatial effects of fiscal decentralization on regional financial efficiency with SDM. The estimated results indicate that there exist significant spatial spillover effects among regional financial efficiency with the features of time inertia and spatial dependence. The positive promoting effect of fiscal decentralization on financial efficiency in local region depends on the symmetry between fiscal expenditure decentralization and revenue decentralization. Additionally, there exists inconsistency in the spatial effects of fiscal expenditure decentralization and revenue decentralization on financial efficiency in neighboring regions. The negative effect of fiscal revenue decentralization on financial efficiency in neighboring regions is more significant than that of fiscal expenditure decentralization.
Directory of Open Access Journals (Sweden)
Alessandro Dal'Col Lúcio
2016-04-01
Full Text Available ABSTRACT The productive variability in horticultural crops affects the planning and quality of the experiments, leading to wrong conclusions. The objectives of this study were to verify the spatial dependence of the fresh biomass of snap beans and to dimension the number of plants and harvests that are necessary to improve experimental accuracy in trials. The data of the fresh biomass of snap beans from uniformity trials carried out in a greenhouse and in the field with semivariograms were created with data transformed into indicators. Thus, they were combined on scenarios of plot size and harvest grouping, and they were adjusted to the spherical, exponential and Gaussian models. A response surface was also applied, with the variation coefficient as a dependent variable and the numbers of plants per plot and harvests as independent variables. The estimates of the semivariogram models parameters indicated a weak spatial dependence. The average of the fresh biomass of snap beans is distributed randomly in the trials, and it is not influenced by the number of plants per plot or by the number of grouped harvests. The best combinations between the number of plants per plot and harvest, for the smaller variation coefficients, are plots of 24 plants for plastic greenhouse and field, and 28 plants for plastic tunnel, in the autumn-winter, combined with the grouping of all harvests. In the spring-summer the number of plants per plot was 30 for plastic tunnel and field, also combined with the grouping of all harvests.
Spatial modelling with R-INLA: A review
Bakka, Haakon
2018-02-18
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\\\\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.
Mohebbi, Mohammadreza; Wolfe, Rory; Jolley, Damien
2011-10-03
Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.
Directory of Open Access Journals (Sweden)
Jolley Damien
2011-10-01
Full Text Available Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs of esophageal cancer (EC from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1 Poisson regression with agglomeration-specific nonspatial random effects; (2 Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC, Akaike's information criterion (AIC and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.
Spatial emission modelling for residential wood combustion in Denmark
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Nielsen, Ole-Kenneth; Brandt, Jørgen
2016-01-01
model with the developed weighting factors (76 ton PM2.5) is in good agreement with the case study (95 ton PM2.5), and that the new model has improved the spatial emission distribution significantly compared to the previous model (284 ton PM2.5). Additionally, a sensitivity analysis was done...
Can spatial statistical river temperature models be transferred between catchments?
Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.
2017-09-01
There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across
Neural correlates of reward-based spatial learning in persons with cocaine dependence.
Tau, Gregory Z; Marsh, Rachel; Wang, Zhishun; Torres-Sanchez, Tania; Graniello, Barbara; Hao, Xuejun; Xu, Dongrong; Packard, Mark G; Duan, Yunsuo; Kangarlu, Alayar; Martinez, Diana; Peterson, Bradley S
2014-02-01
Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.
Zhou, Yixuan; E., Yiwen; Xu, Xinlong; Li, Weilong; Wang, Huan; Zhu, Lipeng; Bai, Jintao; Ren, Zhaoyu; Wang, Li
2016-01-01
Spatial dispersion effect of aligned carbon nanotubes (CNTs) in the terahertz (THz) region has significance for both theoretical and applied consideration due to the unique intrinsically anisotropic physical properties of CNTs. Herein, we report the angular dependent reflection of p-polarized THz wave from vertically aligned multi-walled CNT arrays in both experiment and theory. The spectra indicate that the reflection depends on the film thickness of vertically aligned CNTs, the incident angle, and the frequency. The calculation model is based on the spatial dispersion effect of aligned CNTs and performed with effective impedance method and the Maxwell-Garnett approximation. The results fit well with the experiment when the thickness of CNT film is thin, which reveals a coherent superposition mechanism of the CNT surface reflection and CNTs/Si interface reflection. For thick CNT films, the CNTs/Si interface response determines the reflection at small incident angles, while the CNTs surface effect dominates at large incident angles. This work investigates the spatial dispersion effect of vertically aligned CNT arrays in the THz region, and paves a way for potential anisotropic THz applications based on CNTs with oblique incidence requirements. PMID:27966549
Was Thebes Necessary? Contingency in Spatial Modelling
Evans, Tim S.; Rivers, Ray J.
2016-01-01
When data are poor, we resort to theory modeling. This is a two-step process. We have first to identify the appropriate type of model for the system under consideration and then to tailor it to the specifics of the case. To understand settlement formation, which is the concern of this article, this involves choosing not only input parameter values such as site separations but also input functions that characterizes the ease of travel between sites. Although the generic behavior of the model i...
Magnetic field dependence of spatial frequency encoding NMR as probed on an oligosaccharide.
Pitoux, D; Hu, Z; Plainchont, B; Merlet, D; Farjon, J; Bonnaffé, D; Giraud, N
2015-10-01
The magnetic field dependence of spatial frequency encoding NMR techniques is addressed through a detailed analysis of (1)H NMR spectra acquired under spatial frequency encoding on an oligomeric saccharide sample. In particular, the influence of the strength of the static magnetic field on spectral and spatial resolutions that are key features of this method is investigated. For this purpose, we report the acquisition of correlation experiments implementing broadband homodecoupling or J-edited spin evolutions, and we discuss the resolution enhancements that are provided by these techniques at two different magnetic fields. We show that performing these experiments at higher field improves the performance of high resolution NMR techniques based on a spatial frequency encoding. The significant resolution enhancements observed on the correlation spectra acquired at very high field make them valuable analytical tools that are suitable for the assignment of (1)H chemical shifts and scalar couplings in molecules with highly crowded spectrum such as carbohydrates. Copyright © 2015 John Wiley & Sons, Ltd.
Herborn, Katherine; Alexander, Lucille; Arnold, Kathryn E
2011-03-01
Using featural cues such as colour to identify ephemeral food can increase foraging efficiency. Featural cues may change over time however; therefore, animals should use spatial cues to relocate food that occurs in a temporally stable position. We tested this hypothesis by measuring the cue preferences of captive greenfinches Carduelis chloris when relocating food hidden in a foraging tray. In these standardised associative learning trials, greenfinches favoured colour cues when returning to a foraging context that they had encountered before only once ("one-trial test") but switched to spatial cues when they had encountered that scenario on ten previous occasions ("repeated-trial test"). We suggest that repeated encounters generated a context in which individuals had a prior expectation of temporal stability, and hence context-dependent cue selection. Next, we trained birds to find food in the absence of colour cues but tested them in the presence of visual distracters. Birds were able to learn spatial cues after one encounter, but only when visual distracters were identical in colouration. When a colourful distracter was present in the test phase, cue selection was random. Unlike the first one-trial test, birds were not biased towards this colourful visual distracter. Together, these results suggest that greenfinches are able to learn both cue types, colour cue biases represent learning, not simply distraction, and spatial cues are favoured over colour cues only in temporally stable contexts. © Springer-Verlag 2010
Modelling malaria incidence by an autoregressive distributed lag model with spatial component.
Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne
2017-08-01
The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.
Sensor placement for calibration of spatially varying model parameters
Nath, Paromita; Hu, Zhen; Mahadevan, Sankaran
2017-08-01
This paper presents a sensor placement optimization framework for the calibration of spatially varying model parameters. To account for the randomness of the calibration parameters over space and across specimens, the spatially varying parameter is represented as a random field. Based on this representation, Bayesian calibration of spatially varying parameter is investigated. To reduce the required computational effort during Bayesian calibration, the original computer simulation model is substituted with Kriging surrogate models based on the singular value decomposition (SVD) of the model response and the Karhunen-Loeve expansion (KLE) of the spatially varying parameters. A sensor placement optimization problem is then formulated based on the Bayesian calibration to maximize the expected information gain measured by the expected Kullback-Leibler (K-L) divergence. The optimization problem needs to evaluate the expected K-L divergence repeatedly which requires repeated calibration of the spatially varying parameter, and this significantly increases the computational effort of solving the optimization problem. To overcome this challenge, an approximation for the posterior distribution is employed within the optimization problem to facilitate the identification of the optimal sensor locations using the simulated annealing algorithm. A heat transfer problem with spatially varying thermal conductivity is used to demonstrate the effectiveness of the proposed method.
Brede, Jens; Atodiresei, Nicolae; Kuck, Stefan; Lazić, Predrag; Caciuc, Vasile; Morikawa, Yoshitada; Hoffmann, Germar; Blügel, Stefan; Wiesendanger, Roland
2010-07-23
We investigate the spin- and energy-dependent tunneling through a single organic molecule (CoPc) adsorbed on a ferromagnetic Fe thin film, spatially resolved by low-temperature spin-polarized scanning tunneling microscopy. Interestingly, the metal ion as well as the organic ligand show a significant spin dependence of tunneling current flow. State-of-the-art ab initio calculations including also van der Waals interactions reveal a strong hybridization of molecular orbitals and substrate 3d states. The molecule is anionic due to a transfer of one electron, resulting in a nonmagnetic (S=0) state. Nevertheless, tunneling through the molecule exhibits a pronounced spin dependence due to spin-split molecule-surface hybrid states.
Empirical spatial econometric modelling of small scale neighbourhood
Gerkman, Linda
2012-07-01
The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.
Modeling Concept Dependencies for Event Detection
2014-04-04
Modeling Concept Dependencies for Event Detection Ethem F. Can Center for Intelligent Information Retrieval (CIIR) School of Computer Science UMass...Amherst, MA, 01002 efcan@cs.umass.edu R. Manmatha Center for Intelligent Information Retrieval (CIIR) School of Computer Science UMass Amherst, MA...necessarily involve any actions while other events such as “ Parkour ” may involve multiple atomic ac- tions. Further, videos may vary widely in length and
Directory of Open Access Journals (Sweden)
Werneck Guilherme L.
2002-01-01
Full Text Available Most ecologic studies use geographical areas as units of observation. Because data from areas close to one another tend to be more alike than those from distant areas, estimation of effect size and confidence intervals should consider spatial autocorrelation of measurements. In this report we demonstrate a method for modeling spatial autocorrelation within a mixed model framework, using data on environmental and socioeconomic determinants of the incidence of visceral leishmaniasis (VL in the city of Teresina, Piauí, Brazil. A model with a spherical covariance structure indicated significant spatial autocorrelation in the data and yielded a better fit than one assuming independent observations. While both models showed a positive association between VL incidence and residence in a favela (slum or in areas with green vegetation, values for the fixed effects and standard errors differed substantially between the models. Exploration of the data's spatial correlation structure through the semivariogram should precede the use of these models. Our findings support the hypothesis of spatial dependence of VL rates and indicate that it might be useful to model spatial correlation in order to obtain more accurate point and standard error estimates.
An Evolutionary Model of Spatial Competition
DEFF Research Database (Denmark)
Knudsen, Thorbjørn; Winter, Sidney G.
to environmental change. Formally, the model builds on the NK framework for organizational analysis, with firm policy choices and environmental conditions represented by segments of a string of N bits; it joins this structure to an abstract representation of space based on the idea of a cellular automaton...... This paper sets forth an evolutionary model in which diverse businesses, with diverse offerings, compete in a stylized physical space. When a business firm attempts to expand its activity, so as to profit further from the capabilities it has developed, it necessarily does so in a "new location......" - sometimes close-by existing activity, but often not. The model representation reflects the fact that the physical space in which economic activity takes place is far from homogeneous. The firm then confronts both the challenge of replicating its routines and the hazard that existing routines may not work...
The role of spatial topology in a toy model of classical electrodynamics in (1+1) dimensions
International Nuclear Information System (INIS)
Boozer, A.D.
2010-01-01
We discuss the role of spatial topology in a toy model of classical electrodynamics in (1+1) dimensions. The model describes a collection of Newtonian point particles coupled to a pair of scalar fields E(t,x) and B(t,x), which mediate forces between the particles and support freely propagating radiation. We formulate the model on both a line and a circle, and show that the behavior of the model strongly depends on the choice of spatial topology.
On spatial mutation-selection models
Energy Technology Data Exchange (ETDEWEB)
Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Minlos, Robert, E-mail: minl@iitp.ru; Pirogov, Sergey, E-mail: pirogov@proc.ru [IITP, RAS, Bolshoi Karetnyi 19, Moscow (Russian Federation)
2013-11-15
We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.
Panchromatic SED modelling of spatially-resolved galaxies
Smith, Daniel J. B.; Hayward, Christopher C.
2018-02-01
We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially-resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226,950 MAGPHYS SED fits to regions between 0.2 kpc and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.
Colour dependence of zodiacal light models
Giese, R. H.; Hanner, M. S.; Leinert, C.
1973-01-01
Colour models of the zodiacal light in the ecliptic have been calculated for both dielectric and metallic particles in the sub-micron and micron size range. Two colour ratios were computed, a blue ratio and a red ratio. The models with a size distribution proportional to s to the -2.5 power ds (where s is the particle radius) generally show a colour close to the solar colour and almost independent of elongation. Especially in the blue colour ratio there is generally no significant dependence on the lower cutoff size (0.1-1 micron). The main feature of absorbing particles is a reddening at small elongations. The models for size distributions proportional to s to the -4 power ds show larger departures from solar colour and more variation with model parameters. Colour measurements, including red and near infra-red, therefore are useful to distinguish between flat and steep size spectra and to verify the presence of slightly absorbing particles.
Properties of spatial Cox process models
DEFF Research Database (Denmark)
Møller, Jesper
Probabilistic properties of Cox processes of relevance for statistical modelling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties...... and point process operations such as thinning, displacements, and superpositioning. We also discuss how to simulate specific Cox processes....
Modelling spatial density using continuous wavelet transforms
Indian Academy of Sciences (India)
Space debris; wavelets; Mexican hat; Laplace distribution; random search; parameter estimation. ... Author Affiliations. D Sudheer Reddy1 N Gopal Reddy2 A K Anilkumar3. Digital Mapping and Modelling Division, Advanced Data Processing Research Institute, Secunderabad 500 009, India; Department of Mathematics, ...
Modelling spatial density using continuous wavelet transforms
Indian Academy of Sciences (India)
A K ANILKUMAR3. 1Digital Mapping and Modelling Division, Advanced Data Processing Research .... probability of conjunction is very high and the miss distance between active satellite and debri object is less ... particularly helpful in tackling problems involving signal identification and detection of hidden transients (hard ...
A statistical model for spatial patterns of Buruli ulcer in the Amansie West district, Ghana
Duker, Alfred A.; Stein, Alfred; Hale, Martin
2006-06-01
Buruli ulcer (BU), a skin ulceration caused by Mycobacterium ulcerans (MU), is the second most widespread mycobacterium infection in Ghana. Its infection pathway is possibly related to the potable and agricultural water supply. This study aims to identify environmental factors that influence infection in a part of Ghana. It examines the significance of contaminated surface drainage channels and groundwater using conditional autoregressive (CAR) statistical modelling. This type of modelling implies that the spatial pattern of BU incidence in one community depends on the influence of the environment in neighbouring communities. Covariates were included to assess the spatial relationship between environmental risk factors and BU incidence in the study area. The study reveals an association between (a) the mean As content of soil and spatial distribution of BU and (b) the distance to sites of gold mining and spatial distribution of BU. We conclude that both arsenic in the natural environment and gold mining influence BU infection.
A gender- and sexual orientation-dependent spatial attentional effect of invisible images.
Jiang, Yi; Costello, Patricia; Fang, Fang; Huang, Miner; He, Sheng
2006-11-07
Human observers are constantly bombarded with a vast amount of information. Selective attention helps us to quickly process what is important while ignoring the irrelevant. In this study, we demonstrate that information that has not entered observers' consciousness, such as interocularly suppressed (invisible) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their gender and sexual orientation. While unaware of the suppressed pictures, heterosexual males' attention was attracted to invisible female nudes, heterosexual females' attention was attracted to invisible male nudes, gay males behaved similarly to heterosexual females, and gay/bisexual females performed in-between heterosexual males and females.
Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D
2017-09-11
Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.
Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography
Muller, Leah; Hamilton, Liberty S.; Edwards, Erik; Bouchard, Kristofer E.; Chang, Edward F.
2016-10-01
Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and
Uncertainty in a spatial evacuation model
Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De
2017-08-01
Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.
A Unified 3D Spatial Data Model for Surface and Subsurface Spatial ...
African Journals Online (AJOL)
A simulation of the above, on and below 3D spatial models for man-made constructions at differ-ent LoDs is presented. A simulation of this with regards to mining and cadastre is also presented. The model presented can be adopted in realising 3D GIS for mining and 3D cadastre can be realised in Ghana. Further work is ...
Cosmological backreaction within the Szekeres model and emergence of spatial curvature
Bolejko, Krzysztof
2017-06-01
This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature ΩScript R (in the FLRW limit ΩScript R → Ωk). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from ΩScript R =0 at the CMB to ΩScript R ~ 0.1 at 0z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ωk ≠ 0, even if in the early Universe Ωk = 0) and therefore when analysing low-z cosmological data one should keep Ωk as a free parameter and independent from the CMB constraints.
Cosmological backreaction within the Szekeres model and emergence of spatial curvature
Energy Technology Data Exchange (ETDEWEB)
Bolejko, Krzysztof, E-mail: krzysztof.bolejko@sydney.edu.au [Sydney Institute for Astronomy, School of Physics A28, The University of Sydney, Sydney, NSW, 2006 (Australia)
2017-06-01
This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.
Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof
2017-08-01
The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.
Stochastic Dynamics on Hypergraphs and the Spatial Majority Rule Model
Lanchier, N.; Neufer, J.
2013-04-01
This article starts by introducing a new theoretical framework to model spatial systems which is obtained from the framework of interacting particle systems by replacing the traditional graphical structure that defines the network of interactions with a structure of hypergraph. This new perspective is more appropriate to define stochastic spatial processes in which large blocks of vertices may flip simultaneously, which is then applied to define a spatial version of the Galam's majority rule model. In our spatial model, each vertex of the lattice has one of two possible competing opinions, say opinion 0 and opinion 1, as in the popular voter model. Hyperedges are updated at rate one, which results in all the vertices in the hyperedge changing simultaneously their opinion to the majority opinion of the hyperedge. In the case of a tie in hyperedges with even size, a bias is introduced in favor of type 1, which is motivated by the principle of social inertia. Our analytical results along with simulations and heuristic arguments suggest that, in any spatial dimensions and when the set of hyperedges consists of the collection of all n×⋯× n blocks of the lattice, opinion 1 wins when n is even while the system clusters when n is odd, which contrasts with results about the voter model in high dimensions for which opinions coexist. This is fully proved in one dimension while the rest of our analysis focuses on the cases when n=2 and n=3 in two dimensions.
Appropriatie spatial scales to achieve model output uncertainty goals
Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun
2008-01-01
Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between
Sang, Huiyan
2011-12-01
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.
Spatially adaptive mixture modeling for analysis of FMRI time series.
Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe
2010-04-01
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM
Distributed multi-criteria model evaluation and spatial association analysis
Scherer, Laura; Pfister, Stephan
2015-04-01
Model performance, if evaluated, is often communicated by a single indicator and at an aggregated level; however, it does not embrace the trade-offs between different indicators and the inherent spatial heterogeneity of model efficiency. In this study, we simulated the water balance of the Mississippi watershed using the Soil and Water Assessment Tool (SWAT). The model was calibrated against monthly river discharge at 131 measurement stations. Its time series were bisected to allow for subsequent validation at the same gauges. Furthermore, the model was validated against evapotranspiration which was available as a continuous raster based on remote sensing. The model performance was evaluated for each of the 451 sub-watersheds using four different criteria: 1) Nash-Sutcliffe efficiency (NSE), 2) percent bias (PBIAS), 3) root mean square error (RMSE) normalized to standard deviation (RSR), as well as 4) a combined indicator of the squared correlation coefficient and the linear regression slope (bR2). Conditions that might lead to a poor model performance include aridity, a very flat and steep relief, snowfall and dams, as indicated by previous research. In an attempt to explain spatial differences in model efficiency, the goodness of the model was spatially compared to these four phenomena by means of a bivariate spatial association measure which combines Pearson's correlation coefficient and Moran's index for spatial autocorrelation. In order to assess the model performance of the Mississippi watershed as a whole, three different averages of the sub-watershed results were computed by 1) applying equal weights, 2) weighting by the mean observed river discharge, 3) weighting by the upstream catchment area and the square root of the time series length. Ratings of model performance differed significantly in space and according to efficiency criterion. The model performed much better in the humid Eastern region than in the arid Western region which was confirmed by the
International Nuclear Information System (INIS)
Fu, Jin; Wu, Sheng; Li, Hong; Petzold, Linda R.
2014-01-01
The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy
Energy Technology Data Exchange (ETDEWEB)
Fu, Jin, E-mail: iamfujin@hotmail.com [Department of Computer Science, University of California, Santa Barbara (United States); Wu, Sheng, E-mail: sheng@cs.ucsb.edu [Department of Computer Science, University of California, Santa Barbara (United States); Li, Hong, E-mail: hong.li@teradata.com [Teradata Inc., El Segundo, California (United States); Petzold, Linda R., E-mail: petzold@cs.ucsb.edu [Department of Computer Science, University of California, Santa Barbara (United States)
2014-10-01
The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy.
International Nuclear Information System (INIS)
Kostic, Lj.
1973-01-01
Specially constructed fast reactivity oscillator was stimulating the zero power reactor by a stimulus which caused pseudo-random reactivity changes. Measuring system included stochastic oscillator BCR-1 supplied by pseudo-random pulses from noise generator GBS-16, instrumental tape-recorder, system for data acquisition and digital computer ZUSE-Z-23. For measuring the spatially dependent transfer function, reactor response was measured at a number of different positions of stochastic oscillator and ionization chamber. In order to keep the reactor system linear, experiment was limited to small reactivity fluctuations. Experimental results were compared to theoretical ones
A Statistical Toolbox For Mining And Modeling Spatial Data
Directory of Open Access Journals (Sweden)
D’Aubigny Gérard
2016-12-01
Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.
A spatial model of mosquito host-seeking behavior.
Directory of Open Access Journals (Sweden)
Bree Cummins
Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.
Toward micro-scale spatial modeling of gentrification
O'Sullivan, David
A simple preliminary model of gentrification is presented. The model is based on an irregular cellular automaton architecture drawing on the concept of proximal space, which is well suited to the spatial externalities present in housing markets at the local scale. The rent gap hypothesis on which the model's cell transition rules are based is discussed. The model's transition rules are described in detail. Practical difficulties in configuring and initializing the model are described and its typical behavior reported. Prospects for further development of the model are discussed. The current model structure, while inadequate, is well suited to further elaboration and the incorporation of other interesting and relevant effects.
A spatially-averaged mathematical model of kidney branching morphogenesis
Zubkov, V.S.
2015-08-01
© 2015 Published by Elsevier Ltd. Kidney development is initiated by the outgrowth of an epithelial ureteric bud into a population of mesenchymal cells. Reciprocal morphogenetic responses between these two populations generate a highly branched epithelial ureteric tree with the mesenchyme differentiating into nephrons, the functional units of the kidney. While we understand some of the mechanisms involved, current knowledge fails to explain the variability of organ sizes and nephron endowment in mice and humans. Here we present a spatially-averaged mathematical model of kidney morphogenesis in which the growth of the two key populations is described by a system of time-dependant ordinary differential equations. We assume that branching is symmetric and is invoked when the number of epithelial cells per tip reaches a threshold value. This process continues until the number of mesenchymal cells falls below a critical value that triggers cessation of branching. The mathematical model and its predictions are validated against experimentally quantified C57Bl6 mouse embryonic kidneys. Numerical simulations are performed to determine how the final number of branches changes as key system parameters are varied (such as the growth rate of tip cells, mesenchyme cells, or component cell population exit rate). Our results predict that the developing kidney responds differently to loss of cap and tip cells. They also indicate that the final number of kidney branches is less sensitive to changes in the growth rate of the ureteric tip cells than to changes in the growth rate of the mesenchymal cells. By inference, increasing the growth rate of mesenchymal cells should maximise branch number. Our model also provides a framework for predicting the branching outcome when ureteric tip or mesenchyme cells change behaviour in response to different genetic or environmental developmental stresses.
Modeling Urban Spatial Growth in Mountainous Regions of Western China
Directory of Open Access Journals (Sweden)
Guoping Huang
2017-08-01
Full Text Available The scale and speed of urbanization in the mountainous regions of western China have received little attention from researchers. These cities are facing rapid population growth and severe environmental degradation. This study analyzed historical urban growth trends in this mountainous region to better understand the interaction between the spatial growth pattern and the mountainous topography. Three major factors—slope, accessibility, and land use type—were studied in light of their relationships with urban spatial growth. With the analysis of historical data as the basis, a conceptual urban spatial growth model was devised. In this model, slope, accessibility, and land use type together create resistance to urban growth, while accessibility controls the sequence of urban development. The model was tested and evaluated using historical data. It serves as a potential tool for planners to envision and assess future urban growth scenarios and their potential environmental impacts to make informed decisions.
Spatial distribution of emissions to air – the SPREAD model
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Gyldenkærne, Steen
to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously......The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark’s obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long......-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according...
Image categorization based on spatial visual vocabulary model
Wang, Yuxin; He, Changqin; Guo, He; Feng, Zhen; Jia, Qi
2010-08-01
In this paper, we propose an approach to recognize scene categories by means of a novel method named spatial visual vocabulary. Firstly, we hierarchically divide images into sub regions and construct the spatial visual vocabulary by grouping the low-level features collected from every corresponding spatial sub region into a specified number of clusters using k-means algorithm. To recognize the category of a scene, the visual vocabulary distributions of all spatial sub regions are concatenated to form a global feature vector. The classification is obtained using LIBSVM, a support vector machine classifier. Our goal is to find a universal framework which is applicable to various types of features, so two kinds of features are used in the experiments: "V1-like" filters and PACT features. In almost all experimental cases, the proposed model achieves superior results. Source codes are available by email.
Spatial econometrics using microdata
Dubé, Jean
2014-01-01
This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr
DEFF Research Database (Denmark)
Lacevic, N.; Starr, F. W.; Schrøder, Thomas
2003-01-01
Relaxation in supercooled liquids above their glass transition and below the onset temperature of "slow" dynamics involves the correlated motion of neighboring particles. This correlated motion results in the appearance of spatially heterogeneous dynamics or "dynamical heterogeneity." Traditional...... two-point time-dependent density correlation functions, while providing information about the transient "caging" of particles on cooling, are unable to provide sufficiently detailed information about correlated motion and dynamical heterogeneity. Here, we study a four-point, time-dependent density......-q behavior of S4(q,t) provides an estimate of the range of correlated particle motion. We find that xi4(t) has a maximum as a function of time t, and that the value of the maximum of xi4(t) increases steadily from less than one particle diameter to a value exceeding nine particle diameters in the temperature...
Nethery, Rachel C.; Warren, Joshua L.; Herring, Amy H.; Moore, Kari A.B.; Evenson, Kelly R.; Diez-Roux, Ana V.
2015-01-01
The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes. PMID:26372887
Spatial capture-recapture models for search-encounter data
Royle, J. Andrew; Kery, Marc; Guelat, Jerome
2011-01-01
1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.
Directory of Open Access Journals (Sweden)
M. Liu
2012-02-01
Full Text Available In this paper, simulations with the Soil Water Atmosphere Plant (SWAP model are performed to quantify the spatial variability of both potential and actual evapotranspiration (ET, and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. To obtain the spatially distributed ET/SMC patterns, the field scale SWAP model is applied in a distributed way for both pointwise and catchment wide simulations. An adapted radiation model from r.sun and the physically-based meso-scale wind model METRAS PC are applied to obtain the spatial radiation and wind patterns respectively, which show significant spatial variation and correlation with aspect and elevation respectively. Such topographic dependences and spatial variations further propagate to ET/SMC. A strong spatial, seasonal-dependent, scale-relevant intra-catchment variability in daily/annual ET and less variability in SMC can be observed from the numerical experiments. The study concludes that topography has a significant effect on ET/SMC in the humid region where ET is a energy limited rather than water availability limited process. It affects the spatial runoff generation through spatial radiation and wind, therefore should be applied to inform hydrological model development. In addition, the methodology used in the study can serve as a general method for physically-based ET estimation for data sparse regions.
Analysing earthquake slip models with the spatial prediction comparison test
Zhang, L.
2014-11-10
Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Steger, Doris; Berry, David; Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander
2011-01-01
The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.
Directory of Open Access Journals (Sweden)
Doris Steger
Full Text Available The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained.This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias.Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.
Analysis of the impact of immigration on labour market using spatial models
Polonyankina, Tatiana
2017-07-01
This paper investigates the impact of immigration on employment and unemployment of a host country. The question to answer is: How does employment/unemployment in the host country change after an increase in number of immigrants? The analysis is taking into account only legal immigrants in recession period. The model is combining classical regression of cross-sectional data with spatial econometrics models where cross-section dependencies are captured by a spatial matrix. The intention is by using spatial models analyse the sensitivity of employment/unemployment rate on change in a share of immigration in a region. The used panel data are based on the Labour force survey and on available macro data in Eurostat for 3 European countries (Germany, Austria and Czech Republic) grouped into cells by NUTS regions in a recession period.
Modelling the distribution of fish accounting for spatial correlation and overdispersion
DEFF Research Database (Denmark)
Lewy, Peter; Kristensen, Kasper
2009-01-01
The spatial distribution of cod (Gadus morhua) in the North Sea and the Skagerrak was analysed over a 24-year period using the Log Gaussian Cox Process (LGCP). In contrast to other spatial models of the distribution of fish, LGCP avoids problems with zero observations and includes the spatial...... correlation between observations. It is therefore possible to predict and interpolate unobserved densities at any location in the area. This is important for obtaining unbiased estimates of stock concentration and other measures depending on the distribution in the entire area. Results show that the spatial...... correlation and dispersion of cod catches remained unchanged during winter throughout the period, in spite of a drastic decline in stock abundance and a movement of the centre of gravity of the distribution towards the northeast in the same period. For the age groups considered, the concentration of the stock...
An image-computable psychophysical spatial vision model.
Schütt, Heiko H; Wichmann, Felix A
2017-10-01
A large part of classical visual psychophysics was concerned with the fundamental question of how pattern information is initially encoded in the human visual system. From these studies a relatively standard model of early spatial vision emerged, based on spatial frequency and orientation-specific channels followed by an accelerating nonlinearity and divisive normalization: contrast gain-control. Here we implement such a model in an image-computable way, allowing it to take arbitrary luminance images as input. Testing our implementation on classical psychophysical data, we find that it explains contrast detection data including the ModelFest data, contrast discrimination data, and oblique masking data, using a single set of parameters. Leveraging the advantage of an image-computable model, we test our model against a recent dataset using natural images as masks. We find that the model explains these data reasonably well, too. To explain data obtained at different presentation durations, our model requires different parameters to achieve an acceptable fit. In addition, we show that contrast gain-control with the fitted parameters results in a very sparse encoding of luminance information, in line with notions from efficient coding. Translating the standard early spatial vision model to be image-computable resulted in two further insights: First, the nonlinear processing requires a denser sampling of spatial frequency and orientation than optimal coding suggests. Second, the normalization needs to be fairly local in space to fit the data obtained with natural image masks. Finally, our image-computable model can serve as tool in future quantitative analyses: It allows optimized stimuli to be used to test the model and variants of it, with potential applications as an image-quality metric. In addition, it may serve as a building block for models of higher level processing.
Context-dependent effects of background colour in free recall with spatially grouped words.
Sakai, Tetsuya; Isarida, Toshiko K; Isarida, Takeo
2010-10-01
Three experiments investigated context-dependent effects of background colour in free recall with groups of items. Undergraduates (N=113) intentionally studied 24 words presented in blocks of 6 on a computer screen with two different background colours. The two background colours were changed screen-by-screen randomly (random condition) or alternately (alternation condition) during the study period. A 30-second filled retention interval was imposed before an oral free-recall test. A signal for free recall was presented throughout the test on one of the colour background screens presented at study. Recalled words were classified as same- or different-context words according to whether the background colours at study and test were the same or different. The random condition produced significant context-dependent effects, whereas the alternation condition showed no context-dependent effects, regardless of whether the words were presented once or twice. Furthermore, the words presented on the same screen were clustered in recall, whereas the words presented against the same background colour but on different screens were not clustered. The present results imply: (1) background colours can cue spatially massed words; (2) background colours act as temporally local context; and (3) predictability of the next background colour modulates the context-dependent effect.
ALADYN - a spatially explicit, allelic model for simulating adaptive dynamics.
Schiffers, Katja H; Travis, Justin Mj
2014-12-01
ALADYN is a freely available cross-platform C++ modeling framework for stochastic simulation of joint allelic and demographic dynamics of spatially-structured populations. Juvenile survival is linked to the degree of match between an individual's phenotype and the local phenotypic optimum. There is considerable flexibility provided for the demography of the considered species and the genetic architecture of the traits under selection. ALADYN facilitates the investigation of adaptive processes to spatially and/or temporally changing conditions and the resulting niche and range dynamics. To our knowledge ALADYN is so far the only model that allows a continuous resolution of individuals' locations in a spatially explicit landscape together with the associated patterns of selection.
A spatial and temporal continuous surface-subsurface hydrologic model
Xiao, Qing-Fu; Ustin, Susan L.; Wallender, Wesley W.
1996-12-01
A hydrologic model integrating surface-subsurface processes was developed based on spatial and temporal continuity theory. The raster-based mass balance hydrologic model consists of several submodels which determine spatial and temporal patterns in precipitation, surface flow, infiltration, subsurface flow, and the linkages between these submodels. Model parameters and variables are derived directly or indirectly from satellite remote sensing data, topographic maps, soil maps, literature, and weather station data and are stored in a Geographic Information System (GIS) database used for visualization. Surface resolution of cells in the model is 20 m by 20 m (pixel resolution of the Systeme Probatoire d'Observation de la Terre (SPOT) satellite image) over a 2511 km2 study area around the Crazy Mountains, Alaska, a watershed on the Arctic Circle draining into the Yukon River. The outputs from this model illustrate the interaction of physical and biologic factors on the partitioning of hydrologic components in a complex landscape.
A spatial mark–resight model augmented with telemetry data
Sollmann, Rachel; Gardner, Beth; Parsons, Arielle W.; Stocking, Jessica J.; McClintock, Brett T.; Simons, Theodore R.; Pollock, Kenneth H.; O’Connell, Allan F.
2013-01-01
Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad-hoc approaches. We develop a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows 2. The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.
Lattice Three-Species Models of the Spatial Spread of Rabies among FOXES
Benyoussef, A.; Boccara, N.; Chakib, H.; Ez-Zahraouy, H.
Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible (S), infected or incubating (I), and infectious or rabid (R). They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.
Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models
Directory of Open Access Journals (Sweden)
Chuanglin Fang
2015-11-01
Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.
Environmental Impacts of Large Scale Biochar Application Through Spatial Modeling
Huber, I.; Archontoulis, S.
2017-12-01
In an effort to study the environmental (emissions, soil quality) and production (yield) impacts of biochar application at regional scales we coupled the APSIM-Biochar model with the pSIMS parallel platform. So far the majority of biochar research has been concentrated on lab to field studies to advance scientific knowledge. Regional scale assessments are highly needed to assist decision making. The overall objective of this simulation study was to identify areas in the USA that have the most gain environmentally from biochar's application, as well as areas which our model predicts a notable yield increase due to the addition of biochar. We present the modifications in both APSIM biochar and pSIMS components that were necessary to facilitate these large scale model runs across several regions in the United States at a resolution of 5 arcminutes. This study uses the AgMERRA global climate data set (1980-2010) and the Global Soil Dataset for Earth Systems modeling as a basis for creating its simulations, as well as local management operations for maize and soybean cropping systems and different biochar application rates. The regional scale simulation analysis is in progress. Preliminary results showed that the model predicts that high quality soils (particularly those common to Iowa cropping systems) do not receive much, if any, production benefit from biochar. However, soils with low soil organic matter ( 0.5%) do get a noteworthy yield increase of around 5-10% in the best cases. We also found N2O emissions to be spatial and temporal specific; increase in some areas and decrease in some other areas due to biochar application. In contrast, we found increases in soil organic carbon and plant available water in all soils (top 30 cm) due to biochar application. The magnitude of these increases (% change from the control) were larger in soil with low organic matter (below 1.5%) and smaller in soils with high organic matter (above 3%) and also dependent on biochar
DEFF Research Database (Denmark)
Antón Castro, Francesc/François; Musiige, Deogratius; Mioc, Darka
2016-01-01
This paper presents a case study for comparing different multidimensional mathematical modeling methodologies used in multidimensional spatial big data modeling and proposing a new technique. An analysis of multidimensional modeling approaches (neural networks, polynomial interpolation and homoto...
Function modeling improves the efficiency of spatial modeling using big data from remote sensing
John Hogland; Nathaniel Anderson
2017-01-01
Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...
A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon.
Directory of Open Access Journals (Sweden)
Arun S Moorthy
Full Text Available A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed.
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
Lateral specialization in unilateral spatial neglect: a cognitive robotics model.
Conti, Daniela; Di Nuovo, Santo; Cangelosi, Angelo; Di Nuovo, Alessandro
2016-08-01
In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.
Rockfall hazard analysis using LiDAR and spatial modeling
Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho
2010-05-01
Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.
New advances in spatial network modelling: towards evolutionary algorithms
Reggiani, A; Nijkamp, P.; Sabella, E.
2001-01-01
This paper discusses analytical advances in evolutionary methods with a view towards their possible applications in the space-economy. For this purpose, we present a brief overview and illustration of models actually available in the spatial sciences which attempt to map the complex patterns of
Classifying and comparing spatial models of fire dynamics
Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan
2007-01-01
Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...
Modelling spatial anisotropy of gold concentration data using GIS ...
Indian Academy of Sciences (India)
linear trends are interpreted to represent major fault zones that exerted a prinicipal control on gold mineralization and therefore ... concentration data are particularly useful in the field of mineral exploration. Keywords. Structural control .... the variogram is the most com- monly used tool for modelling spatial structure and.
Individual based model of slug population and spatial dynamics
Choi, Y.H.; Bohan, D.A.; Potting, R.P.J.; Semenov, M.A.; Glen, D.M.
2006-01-01
The slug, Deroceras reticulatum, is one of the most important pests of agricultural and horticultural crops in UK and Europe. In this paper, a spatially explicit individual based model (IbM) is developed to study the dynamics of a population of D. reticulatum. The IbM establishes a virtual field
Spatial modeling on the nutrient retention of an estuary wetland
Li, X.; Xiao, D.; Jongman, R.H.G.; Harms, W.B.; Bregt, A.K.
2003-01-01
There is a great potential to use the estuary wetland as a final filter for nutrient enriched river water, and reduce the possibility of coastal water eutrophication. Based upon field data, spatial models were designed on a stepwise basis to simulate the nutrient reduction function of the wetland in
Wu, Wei; Wang, Jin
2013-09-28
We established a potential and flux field landscape theory to quantify the global stability and dynamics of general spatially dependent non-equilibrium deterministic and stochastic systems. We extended our potential and flux landscape theory for spatially independent non-equilibrium stochastic systems described by Fokker-Planck equations to spatially dependent stochastic systems governed by general functional Fokker-Planck equations as well as functional Kramers-Moyal equations derived from master equations. Our general theory is applied to reaction-diffusion systems. For equilibrium spatially dependent systems with detailed balance, the potential field landscape alone, defined in terms of the steady state probability distribution functional, determines the global stability and dynamics of the system. The global stability of the system is closely related to the topography of the potential field landscape in terms of the basins of attraction and barrier heights in the field configuration state space. The effective driving force of the system is generated by the functional gradient of the potential field alone. For non-equilibrium spatially dependent systems, the curl probability flux field is indispensable in breaking detailed balance and creating non-equilibrium condition for the system. A complete characterization of the non-equilibrium dynamics of the spatially dependent system requires both the potential field and the curl probability flux field. While the non-equilibrium potential field landscape attracts the system down along the functional gradient similar to an electron moving in an electric field, the non-equilibrium flux field drives the system in a curly way similar to an electron moving in a magnetic field. In the small fluctuation limit, the intrinsic potential field as the small fluctuation limit of the potential field for spatially dependent non-equilibrium systems, which is closely related to the steady state probability distribution functional, is
Directory of Open Access Journals (Sweden)
Guanpeng Dong
Full Text Available This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group with proximity effects (some sort of joint dependency that emerges between neighbours. To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.
Spatial variability and parametric uncertainty in performance assessment models
International Nuclear Information System (INIS)
Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo
2011-01-01
The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)
INHOMOGENEITY IN SPATIAL COX POINT PROCESSES – LOCATION DEPENDENT THINNING IS NOT THE ONLY OPTION
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Michaela Prokešová
2010-11-01
Full Text Available In the literature on point processes the by far most popular option for introducing inhomogeneity into a point process model is the location dependent thinning (resulting in a second-order intensity-reweighted stationary point process. This produces a very tractable model and there are several fast estimation procedures available. Nevertheless, this model dilutes the interaction (or the geometrical structure of the original homogeneous model in a special way. When concerning the Markov point processes several alternative inhomogeneous models were suggested and investigated in the literature. But it is not so for the Cox point processes, the canonical models for clustered point patterns. In the contribution we discuss several other options how to define inhomogeneous Cox point process models that result in point patterns with different types of geometric structure. We further investigate the possible parameter estimation procedures for such models.
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Marzieh Mokarrama
2018-04-01
Full Text Available The purpose of the present study is preparing a landform classification by using digital elevation model (DEM which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30 m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER and a 90 m (the Shuttle Radar Topography Mission; (SRTM. In the attraction model, scale factors of (S = 2, S = 3, and S = 4 with two neighboring methods of touching (T = 1 and quadrant (T = 2 are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S = 2 gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of
Modern methodology and applications in spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...
Spatial Development Modeling Methodology Application Possibilities in Vilnius
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Lina Panavaitė
2017-05-01
Full Text Available In order to control the continued development of high-rise buildings and their irreversible visual impact on the overall silhouette of the city, the great cities of the world introduced new methodological principles to city’s spatial development models. These methodologies and spatial planning guidelines are focused not only on the controlled development of high-rise buildings, but on the spatial modelling of the whole city by defining main development criteria and estimating possible consequences. Vilnius city is no exception, however the re-establishment of independence of Lithuania caused uncontrolled urbanization process, so most of the city development regulations emerged as a consequence of unmanaged processes of investors’ expectations legalization. The importance of consistent urban fabric as well as conservation and representation of city’s most important objects gained attention only when an actual threat of overshadowing them with new architecture along with unmanaged urbanization in the city center or urban sprawl at suburbia, caused by land-use projects, had emerged. Current Vilnius’ spatial planning documents clearly define urban structure and key development principles, however the definitions are relatively abstract, causing uniform building coverage requirements for territories with distinct qualities and simplifying planar designs which do not meet quality standards. The overall quality of urban architecture is not regulated. The article deals with current spatial modeling methods, their individual parts, principles, the criteria for quality assessment and their applicability in Vilnius. The text contains an outline of possible building coverage regulations and impact assessment criteria for new development. The article contains a compendium of requirements for high-quality spatial planning and building design.
Spatial modelling of assumption of tourism development with geographic IT using
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Jitka Machalová
2010-01-01
Full Text Available The aim of this article is to show the possibilities of spatial modelling and analysing of assumptions of tourism development in the Czech Republic with the objective to make decision-making processes in tourism easier and more efficient (for companies, clients as well as destination managements. The development and placement of tourism depend on the factors (conditions that influence its application in specific areas. These factors are usually divided into three groups: selective, localization and realization. Tourism is inseparably connected with space – countryside. The countryside can be modelled and consecutively analysed by the means of geographical information technologies. With the help of spatial modelling and following analyses the localization and realization conditions in the regions of the Czech Republic have been evaluated. The best localization conditions have been found in the Liberecký region. The capital city of Prague has negligible natural conditions; however, those social ones are on a high level. Next, the spatial analyses have shown that the best realization conditions are provided by the capital city of Prague. Then the Central-Bohemian, South-Moravian, Moravian-Silesian and Karlovarský regions follow. The development of tourism destination is depended not only on the localization and realization factors but it is basically affected by the level of local destination management. Spatial modelling can help destination managers in decision-making processes in order to optimal use of destination potential and efficient targeting their marketing activities.
Kim, Yura; Jun, Mikyoung; Min, Seung-Ki; Suh, Myoung-Seok; Kang, Hyun-Suk
2016-05-01
CORDEX-East Asia, a branch of the coordinated regional climate downscaling experiment (CORDEX) initiative, provides high-resolution climate simulations for the domain covering East Asia. This study analyzes temperature data from regional climate models (RCMs) participating in the CORDEX - East Asia region, accounting for the spatial dependence structure of the data. In particular, we assess similarities and dissimilarities of the outputs from two RCMs, HadGEM3-RA and RegCM4, over the region and over time. A Bayesian functional analysis of variance (ANOVA) approach is used to simultaneously model the temperature patterns from the two RCMs for the current and future climate. We exploit nonstationary spatial models to handle the spatial dependence structure of the temperature variable, which depends heavily on latitude and altitude. For a seasonal comparison, we examine changes in the winter temperature in addition to the summer temperature data. We find that the temperature increase projected by RegCM4 tends to be smaller than the projection of HadGEM3-RA for summers, and that the future warming projected by HadGEM3-RA tends to be weaker for winters. Also, the results show that there will be a warming of 1-3°C over the region in 45 years. More specifically, the warming pattern clearly depends on the latitude, with greater temperature increases in higher latitude areas, which implies that warming may be more severe in the northern part of the domain.
Huser, Raphaël
2018-01-09
Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability of the dependence does not prevail in finite samples. This issue is particularly serious when data are asymptotically independent, such that the dependence strength weakens and eventually vanishes as events become more extreme. We here aim to provide flexible sub-asymptotic models for spatially indexed block maxima, which more realistically account for discrepancies between data and asymptotic theory. We develop models pertaining to the wider class of max-infinitely divisible processes, extending the class of max-stable processes while retaining dependence properties that are natural for maxima: max-id models are positively associated, and they yield a self-consistent family of models for block maxima defined over any time unit. We propose two parametric construction principles for max-id models, emphasizing a point process-based generalized spectral representation, that allows for asymptotic independence while keeping the max-stable extremal-$t$ model as a special case. Parameter estimation is efficiently performed by pairwise likelihood, and we illustrate our new modeling framework with an application to Dutch wind gust maxima calculated over different time units.
Choudhury, B. J.
1983-01-01
A soil plant atmosphere model for corn (Zea mays L.) together with the scaling theory for soil hydraulic heterogeneity are used to study the sensitivity of spatial variation of canopy temperature to field averaged soil texture and crop rooting characteristics. The soil plant atmosphere model explicitly solves a continuity equation for water flux resulting from root water uptake, changes in plant water storage and transpirational flux. Dynamical equations for root zone soil water potential and the plant water storage models the progressive drying of soil, and day time dehydration and night time hydration of the crop. The statistic of scaling parameter which describes the spatial variation of soil hydraulic conductivity and matric potential is assumed to be independent of soil texture class. The field averaged soil hydraulic characteristics are chosen to be representative of loamy sand and clay loam soils. Two rooting characteristics are chosen, one shallow and the other deep rooted. The simulation shows that the range of canopy temperatures in the clayey soil is less than 1K, but for the sandy soil the range is about 2.5 and 5.0 K, respectively, for the shallow and deep rooted crops.
Ostoja, Steven M.; Schupp, Eugene W.; Klinger, Rob
2013-01-01
Granivore foraging decisions affect consumer success and determine the quantity and spatial pattern of seed survival. These decisions are influenced by environmental variation at spatial scales ranging from landscapes to local foraging patches. In a field experiment, the effects of seed patch variation across three spatial scales on seed removal by western harvester ants Pogonomyrmex occidentalis were evaluated. At the largest scale we assessed harvesting in different plant communities, at the intermediate scale we assessed harvesting at different distances from ant mounds, and at the smallest scale we assessed the effects of interactions among seed species in local seed neighborhoods on seed harvesting (i.e. resource–consumer interface). Selected seed species were presented alone (monospecific treatment) and in mixture with Bromus tectorum (cheatgrass; mixture treatment) at four distances from P. occidentalis mounds in adjacent intact sagebrush and non-native cheatgrass-dominated communities in the Great Basin, Utah, USA. Seed species differed in harvest, with B. tectorum being least preferred. Large and intermediate scale variation influenced harvest. More seeds were harvested in sagebrush than in cheatgrass-dominated communities (largest scale), and the quantity of seed harvested varied with distance from mounds (intermediate-scale), although the form of the distance effect differed between plant communities. At the smallest scale, seed neighborhood affected harvest, but the patterns differed among seed species considered. Ants harvested fewer seeds from mixed-seed neighborhoods than from monospecific neighborhoods, suggesting context dependence and potential associational resistance. Further, the effects of plant community and distance from mound on seed harvest in mixtures differed from their effects in monospecific treatments. Beyond the local seed neighborhood, selection of seed resources is better understood by simultaneously evaluating removal at
Spatial Temporal Modelling of Particulate Matter for Health Effects Studies
Hamm, N. A. S.
2016-10-01
Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.
Modelling the Spatial Distribution of Wind Energy Resources in Latvia
Aniskevich, S.; Bezrukovs, V.; Zandovskis, U.; Bezrukovs, D.
2017-12-01
The paper studies spatial wind energy flow distribution in Latvia based on wind speed measurements carried out at an altitude of 10 m over a period of two years, from 2015 to 2016. The measurements, with 1 min increments, were carried out using certified measuring instruments installed at 22 observation stations of the Latvian National Hydrometeorological and Climatological Service of the Latvian Environment, Geology and Meteorology Centre (LEGMC). The models of the spatial distribution of averaged wind speed and wind energy density were developed using the method of spatial interpolation based on the historical measurement results and presented in the form of colour contour maps with a 1×1 km resolution. The paper also provides the results of wind speed spatial distribution modelling using a climatological reanalysis ERA5 at the altitudes of 10, 54, 100 and 136 m with a 31×31 km resolution. The analysis includes the comparison of actual wind speed measurement results with the outcomes of ERA5 modelling for meteorological observation stations in Ainazi, Daugavpils, Priekuli, Saldus and Ventspils.
Spatial Linear Mixed Models with Covariate Measurement Errors.
Li, Yi; Tang, Haicheng; Lin, Xihong
2009-01-01
Spatial data with covariate measurement errors have been commonly observed in public health studies. Existing work mainly concentrates on parameter estimation using Gibbs sampling, and no work has been conducted to understand and quantify the theoretical impact of ignoring measurement error on spatial data analysis in the form of the asymptotic biases in regression coefficients and variance components when measurement error is ignored. Plausible implementations, from frequentist perspectives, of maximum likelihood estimation in spatial covariate measurement error models are also elusive. In this paper, we propose a new class of linear mixed models for spatial data in the presence of covariate measurement errors. We show that the naive estimators of the regression coefficients are attenuated while the naive estimators of the variance components are inflated, if measurement error is ignored. We further develop a structural modeling approach to obtaining the maximum likelihood estimator by accounting for the measurement error. We study the large sample properties of the proposed maximum likelihood estimator, and propose an EM algorithm to draw inference. All the asymptotic properties are shown under the increasing-domain asymptotic framework. We illustrate the method by analyzing the Scottish lip cancer data, and evaluate its performance through a simulation study, all of which elucidate the importance of adjusting for covariate measurement errors.
Spatial modelling and ecology of Echinococcus multilocularis transmission in China.
Danson, F Mark; Giraudoux, Patrick; Craig, Philip S
2006-01-01
Recent research in central China has suggested that the most likely transmission mechanism for Echinococcus multilocularis to humans is via domestic dogs which are allowed to roam freely and hunt (infected) small mammals within areas close to villages or in areas of tented pasture. This assertion has led to the hypothesis that there is a landscape control on transmission risk since the proximity of suitable habitat for susceptible small mammals appears to be the key. We have tested this hypothesis in a number of endemic areas in China, notably south Gansu Province and the Tibetan region of western Sichuan Province. The fundamental landscape control is its effect at a regional scale on small mammal species assemblages (susceptible species are not ubiquitous) and, at a local scale, the spatial distributions of small mammal populations. To date the research has examined relationships between landscape composition and patterns of human infection, landscape and small mammal distributions and recently the relationships between landscape and dog infection rates. The key tool to characterize landscape is satellite remote sensing and these data are used as inputs to drive spatial models of transmission risk. This paper reviews the progress that has been made so far in spatial modeling of the ecology of E. multilocularis with particular reference to China, outlines current research issues, and describes a framework for building a spatial-temporal model of transmission ecology.
Scaling-up spatially-explicit ecological models using graphics processors
Koppel, Johan van de; Gupta, Rohit; Vuik, Cornelis
2011-01-01
How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-org...
Exploring the inequality-mortality relationship in the US with Bayesian spatial modeling
Yang, Tse-Chuan; Jensen, Leif
2014-01-01
While there is evidence to suggest that socioeconomic inequality within places is associated with mortality rates among people living within them, the empirical connection between the two remains unsettled as potential confounders associated with racial and social structure are overlooked. This study seeks to test this relationship, to determine whether it is due to differential levels of deprivation and social capital, and does so with intrinsically conditional autoregressive Bayesian spatial modeling that effectively addresses the bias introduced by spatial dependence. We find that deprivation and social capital partly but not completely account for why inequality is positively associated with mortality and that spatial modeling generates more accurate predictions than does the traditional approach. We advance the literature by unveiling the intervening roles of social capital and deprivation in the inequality-mortality relationship and offering new evidence that inequality matters in US county mortality rates. PMID:26166920
The Employment of spatial autoregressive models in predicting demand for natural gas
International Nuclear Information System (INIS)
Castro, Jorge Henrique de; Silva, Alexandre Pinto Alves da
2010-01-01
Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)
Space in multi-agent systems modelling spatial processes
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Petr Rapant
2007-06-01
Full Text Available Need for modelling of spatial processes arise in the spehere of geoinformation systems in the last time. Some processes (espetially natural ones can be modeled by means of using external tools, e. g. for modelling of contaminant transport in the environment. But in the case of socio-economic processes suitable tools interconnected with GIS are still in quest of reserch and development. One of the candidate technologies are so called multi-agent systems. Their theory is developed quite well, but they lack suitable means for dealing with space. This article deals with this problem and proposes solution for the field of a road transport modelling.
Exploring regional economic convergence in Romania. A spatial modeling approach
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Zizi GOSCHIN
2017-12-01
Full Text Available This paper explores spatial economic convergence in Romania, from the perspective of real GDP/capita, and examines how the shock of the recent economic crisis has affected the convergence process. Given the presence of spatial autocorrelation in the values of GDP per capita, we address the question of convergence in terms of both classic and spatial regression models, thus filling a gap in the Romanian literature on this topic. The empirical results seem to provide support for both absolute and relative beta divergence in GDP/capita, as well as sigma divergence among Romanian counties on the long run. This is the consequence of the two-speed regional development, with the capital region and some large cities thriving by attracting human capital and FDIs, while the lagging regions are systematically left behind. Failing to validate the neoclassical approach on convergence, our results rather support the new divergence theory based on polarization and centre-periphery inequality.
Modelling spatial patterns of urban growth in Africa
Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius
2013-01-01
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552
TIME-DEPENDENT MODELS OF FLARES FROM SAGITTARIUS A*
International Nuclear Information System (INIS)
Dodds-Eden, Katie; Genzel, Reinhard; Gillessen, Stefan; Eisenhauer, Frank; Sharma, Prateek; Quataert, Eliot; Porquet, Delphine
2010-01-01
The emission from Sgr A*, the supermassive black hole in the Galactic Center, shows order of magnitude variability ('flares') a few times a day that is particularly prominent in the near-infrared (NIR) and X-rays. We present a time-dependent model for these flares motivated by the hypothesis that dissipation of magnetic energy powers the flares. We show that episodic magnetic reconnection can occur near the last stable circular orbit in time-dependent magnetohydrodynamic simulations of black hole accretion-the timescales and energetics of these events are broadly consistent with the flares from Sgr A*. Motivated by these results, we present a spatially one-zone time-dependent model for the electron distribution function in flares, including energy loss due to synchrotron cooling and adiabatic expansion. Synchrotron emission from transiently accelerated particles can explain the NIR/X-ray light curves and spectra of a luminous flare observed on 2007 April 4. A significant decrease in the magnetic field strength during the flare (coincident with the electron acceleration) is required to explain the simultaneity and symmetry of the simultaneous light curves. Our models predict that the NIR and X-ray spectral indices are related by Δα ≅ 0.5 (where νF ν ∝ ν α ) and that there is only modest variation in the spectral index during flares. We also explore implications of this model for longer wavelength (radio-submillimeter) emission seemingly associated with X-ray and NIR flares; we argue that a few hour decrease in the submillimeter emission is a more generic consequence of large-scale magnetic reconnection than delayed radio emission from adiabatic expansion.
Neuromorphic model of magnocellular and parvocellular visual paths: spatial resolution
International Nuclear Information System (INIS)
Aguirre, Rolando C; Felice, Carmelo J; Colombo, Elisa M
2007-01-01
Physiological studies of the human retina show the existence of at least two visual information processing channels, the magnocellular and the parvocellular ones. Both have different spatial, temporal and chromatic features. This paper focuses on the different spatial resolution of these two channels. We propose a neuromorphic model, so that they match the retina's physiology. Considering the Deutsch and Deutsch model (1992), we propose two configurations (one for each visual channel) of the connection between the retina's different cell layers. The responses of the proposed model have similar behaviour to those of the visual cells: each channel has an optimum response corresponding to a given stimulus size which decreases for larger or smaller stimuli. This size is bigger for the magno path than for the parvo path and, in the end, both channels produce a magnifying of the borders of a stimulus
Single Canonical Model of Reflexive Memory and Spatial Attention.
Patel, Saumil S; Red, Stuart; Lin, Eric; Sereno, Anne B
2015-10-23
Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey's task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.
Analytical model of reactive transport processes with spatially variable coefficients.
Simpson, Matthew J; Morrow, Liam C
2015-05-01
Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems.
Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models
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Sang Ah Lee
2017-02-01
Full Text Available Research across the cognitive and brain sciences has begun to elucidate some of the processes that guide navigation and spatial memory. Boundary geometry and featural landmarks are two distinct classes of environmental cues that have dissociable neural correlates in spatial representation and follow different patterns of learning. Consequently, spatial navigation depends both on the type of cue available and on the type of learning provided. We investigated this interaction between spatial representation and memory by administering two different tasks (working memory, reference memory using two different environmental cues (rectangular geometry, striped landmark in mouse models of human genetic disorders: Prader-Willi syndrome (PWScrm+/p− mice, n = 12 and Beta-catenin mutation (Thr653Lys-substituted mice, n = 12. This exploratory study provides suggestive evidence that these models exhibit different abilities and impairments in navigating by boundary geometry and featural landmarks, depending on the type of memory task administered. We discuss these data in light of the specific deficits in cognitive and brain function in these human syndromes and their animal model counterparts.
Modified Spatial Channel Model for MIMO Wireless Systems
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Pekka Kyösti
2007-12-01
Full Text Available Ã¯Â»Â¿The third generation partnership Project's (3GPP spatial channel model (SCM is a stochastic channel model for MIMO systems. Due to fixed subpath power levels and angular directions, the SCM model does not show the degree of variation which is encountered in real channels. In this paper, we propose a modified SCM model which has random subpath powers and directions and still produces Laplace shape angular power spectrum. Simulation results on outage MIMO capacity with basic and modified SCM models show that the modified SCM model gives constantly smaller capacity values. Accordingly, it seems that the basic SCM gives too small correlation between MIMO antennas. Moreover, the variance in capacity values is larger using the proposed SCM model. Simulation results were supported by the outage capacity results from a measurement campaign conducted in the city centre of Oulu, Finland.
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.
A general modeling framework for describing spatially structured population dynamics
Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan
2017-01-01
Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance
A general modeling framework for describing spatially structured population dynamics.
Sample, Christine; Fryxell, John M; Bieri, Joanna A; Federico, Paula; Earl, Julia E; Wiederholt, Ruscena; Mattsson, Brady J; Flockhart, D T Tyler; Nicol, Sam; Diffendorfer, Jay E; Thogmartin, Wayne E; Erickson, Richard A; Norris, D Ryan
2018-01-01
Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance
Spatial distribution of emissions to air - the SPREAD model
Energy Technology Data Exchange (ETDEWEB)
Plejdrup, M.S.; Gyldenkaerne, S.
2011-04-15
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)
Directory of Open Access Journals (Sweden)
Erin Peterson
2014-01-01
Full Text Available This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu- late the spatial information needed to fit spatial statistical models to stream network data using the SSN package. The STARS toolset is designed for use with a landscape network (LSN, which is a topological data model produced by the FLoWS ArcGIS geoprocessing toolset. An overview of the FLoWS LSN structure and a few particularly useful tools is also provided so that users will have a clear understanding of the underlying data struc- ture that the STARS toolset depends on. This document may be used as an introduction to new users. The methods used to calculate the spatial information and format the final .ssn object are also explicitly described so that users may create their own .ssn object using other data models and software.
Database modeling to integrate macrobenthos data in Spatial Data Infrastructure
Directory of Open Access Journals (Sweden)
José Alberto Quintanilha
2012-08-01
Full Text Available Coastal zones are complex areas that include marine and terrestrial environments. Besides its huge environmental wealth, they also attracts humans because provides food, recreation, business, and transportation, among others. Some difficulties to manage these areas are related with their complexity, diversity of interests and the absence of standardization to collect and share data to scientific community, public agencies, among others. The idea to organize, standardize and share this information based on Web Atlas is essential to support planning and decision making issues. The construction of a spatial database integrating the environmental business, to be used on Spatial Data Infrastructure (SDI is illustrated by a bioindicator that indicates the quality of the sediments. The models show the phases required to build Macrobenthos spatial database based on Santos Metropolitan Region as a reference. It is concluded that, when working with environmental data the structuring of knowledge in a conceptual model is essential for their subsequent integration into the SDI. During the modeling process it can be noticed that methodological issues related to the collection process may obstruct or prejudice the integration of data from different studies of the same area. The development of a database model, as presented in this study, can be used as a reference for further research with similar goals.
Bijleveld, Allert I; MacCurdy, Robert B; Chan, Ying-Chi; Penning, Emma; Gabrielson, Rich M; Cluderay, John; Spaulding, Eric L; Dekinga, Anne; Holthuijsen, Sander; ten Horn, Job; Brugge, Maarten; van Gils, Jan A; Winkler, David W; Piersma, Theunis
2016-04-13
Negative density-dependence is generally studied within a single trophic level, thereby neglecting its effect on higher trophic levels. The 'functional response' couples a predator's intake rate to prey density. Most widespread is a type II functional response, where intake rate increases asymptotically with prey density; this predicts the highest predator densities at the highest prey densities. In one of the most stringent tests of this generality to date, we measured density and quality of bivalve prey (edible cockles Cerastoderma edule) across 50 km² of mudflat, and simultaneously, with a novel time-of-arrival methodology, tracked their avian predators (red knots Calidris canutus). Because of negative density-dependence in the individual quality of cockles, the predicted energy intake rates of red knots declined at high prey densities (a type IV, rather than a type II functional response). Resource-selection modelling revealed that red knots indeed selected areas of intermediate cockle densities where energy intake rates were maximized given their phenotype-specific digestive constraints (as indicated by gizzard mass). Because negative density-dependence is common, we question the current consensus and suggest that predators commonly maximize their energy intake rates at intermediate prey densities. Prey density alone may thus poorly predict intake rates, carrying capacity and spatial distributions of predators. © 2016 The Author(s).
Energy Technology Data Exchange (ETDEWEB)
Grasselli, Federico, E-mail: federico.grasselli@unimore.it; Goldoni, Guido, E-mail: guido.goldoni@unimore.it [Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena (Italy); CNR-NANO S3, Institute for Nanoscience, Via Campi 213/a, 41125 Modena (Italy); Bertoni, Andrea, E-mail: andrea.bertoni@nano.cnr.it [CNR-NANO S3, Institute for Nanoscience, Via Campi 213/a, 41125 Modena (Italy)
2015-01-21
We study the unitary propagation of a two-particle one-dimensional Schrödinger equation by means of the Split-Step Fourier method, to study the coherent evolution of a spatially indirect exciton (IX) in semiconductor heterostructures. The mutual Coulomb interaction of the electron-hole pair and the electrostatic potentials generated by external gates and acting on the two particles separately are taken into account exactly in the two-particle dynamics. As relevant examples, step/downhill and barrier/well potential profiles are considered. The space- and time-dependent evolutions during the scattering event as well as the asymptotic time behavior are analyzed. For typical parameters of GaAs-based devices, the transmission or reflection of the pair turns out to be a complex two-particle process, due to comparable and competing Coulomb, electrostatic, and kinetic energy scales. Depending on the intensity and anisotropy of the scattering potentials, the quantum evolution may result in excitation of the IX internal degrees of freedom, dissociation of the pair, or transmission in small periodic IX wavepackets due to dwelling of one particle in the barrier region. We discuss the occurrence of each process in the full parameter space of the scattering potentials and the relevance of our results for current excitronic technologies.
Variant 22: Spatially-Dependent: Transient Processes in MOX Fueled Core
Energy Technology Data Exchange (ETDEWEB)
Pavlovichev, A.M.
2001-09-28
This work is a part of Joint U.S./Russian Project with Weapons-Grade Plutonium Disposition in VVER Reactors and presents the results of spatial kinetics calculational benchmarks. The examinations were carried out with the following purposes: to verify one of spatial neutronic kinetics model elaborated in KI, to understand sensibility of the model to neutronics difference of UOX and MOX cores, and to compare in future point and spatial kinetics models (on the base of a set of selected accidents) in view of eventual creation of RELAP option with 3D kinetics. The document contains input data and results of model operation of three emergency dynamic processes in the VVER-1000 core: (1) Central control rod ejection by pressure drop caused by destroying of the moving mechanism cover. (2) Overcooling of the reactor core caused by steam line rupture and non-closure of steam generator stop valve. (3) The boron dilution of coolant in part of the VVER-1000 core caused by penetration of the distillate slug into the core at start up of non-working loop. These accidents have been applied to: (1) Uranium reference core that is the so-called Advanced VVER-1000 core with Zirconium fuel pins claddings and guide tubes. A number of assemblies contained 18 boron BPRs while first year operating. (2) MOX core with about 30% MOX fuel. At a solving it was supposed that MOX-fuel thermophysical characteristics are identical to uranium fuel ones. The calculations were carried out with the help of the program NOSTRA/1/, simulating VVER dynamics that is briefly described in Chapter 1. Chapter 3 contains the description of reference Uranium and MOX cores that are used in calculations. The neutronics calculations of MOX core with about 30% MOX fuel are named ''Variant 2 1''. Chapters 4-6 contain the calculational results of three above mentioned benchmark accidents that compose in a whole the ''Variant 22''.
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
An alternative to the standard spatial econometric approaches in hedonic house price models
DEFF Research Database (Denmark)
Veie, Kathrine Lausted; Panduro, Toke Emil
Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-speciﬁcation or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial ﬁxed eﬀects. However, often spatial correlation...
System reliability time-dependent models
International Nuclear Information System (INIS)
Debernardo, H.D.
1991-06-01
A probabilistic methodology for safety system technical specification evaluation was developed. The method for Surveillance Test Interval (S.T.I.) evaluation basically means an optimization of S.T.I. of most important system's periodically tested components. For Allowed Outage Time (A.O.T.) calculations, the method uses system reliability time-dependent models (A computer code called FRANTIC III). A new approximation, which was called Independent Minimal Cut Sets (A.C.I.), to compute system unavailability was also developed. This approximation is better than Rare Event Approximation (A.E.R.) and the extra computing cost is neglectible. A.C.I. was joined to FRANTIC III to replace A.E.R. on future applications. The case study evaluations verified that this methodology provides a useful probabilistic assessment of surveillance test intervals and allowed outage times for many plant components. The studied system is a typical configuration of nuclear power plant safety systems (two of three logic). Because of the good results, these procedures will be used by the Argentine nuclear regulatory authorities in evaluation of technical specification of Atucha I and Embalse nuclear power plant safety systems. (Author) [es
Approximate Bayesian computation for spatial SEIR(S) epidemic models.
Brown, Grant D; Porter, Aaron T; Oleson, Jacob J; Hinman, Jessica A
2018-02-01
Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas. Copyright © 2017 Elsevier Ltd. All rights reserved.
Spatial and spatio-temporal models with R-INLA.
Blangiardo, Marta; Cameletti, Michela; Baio, Gianluca; Rue, Håvard
2013-12-01
During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.
Representing spatial information in a computational model for network management
Blaisdell, James H.; Brownfield, Thomas F.
1994-01-01
While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.
Unsupervised Posture Modeling Based on Spatial-Temporal Movement Features
Yan, Chunjuan
Traditional posture modeling for human action recognition is based on silhouette segmentation, which is subject to the noise from illumination variation and posture occlusions and shadow interruptions. In this paper, we extract spatial temporal movement features from human actions and adopt unsupervised clustering method for salient posture learning. First, spatial-temporal interest points (STIPs) were extracted according to the properties of human movement, and then, histogram of gradient was built to describe the distribution of STIPs in each frame for a single pose. In addition, the training samples were clustered by non-supervised classification method. Moreover, the salient postures were modeled with GMM according to Expectation Maximization (EM) estimation. The experiment results proved that our method can effectively and accurately recognize human's action postures.
Spatial models of Northern Bobwhite populations for conservation planning
Twedt, Daniel J.; Wilson, R. Randy; Keister, Amy S.
2007-01-01
Since 1980, northern bobwhite (Colinus virginianus) range-wide populations declined 3.9% annually. Within the West Gulf Coastal Plain Bird Conservation Region in the south-central United States, populations of this quail species have declined 6.8% annually. These declines sparked calls for land use change and prompted implementation of various conservation practices. However, to effectively reverse these declines and restore northern bobwhite to their former population levels, habitat conservation and management efforts must target establishment and maintenance of sustainable populations. To provide guidance for conservation and restoration of habitat capable of supporting sustainable northern bobwhite populations in the West Gulf Coastal Plain, we modeled their spatial distribution using landscape characteristics derived from 1992 National Land Cover Data and bird detections, from 1990 to 1994, along 10-stop Breeding Bird Survey route segments. Four landscape metrics influenced detections of northern bobwhite: detections were greater in areas with more grassland and increased aggregation of agricultural lands, but detections were reduced in areas with increased density of land cover edge and grassland edge. Using these landscape metrics, we projected the abundance and spatial distribution of northern bobwhite populations across the entire West Gulf Coastal Plain. Predicted populations closely approximated abundance estimates from a different cadre of concurrently collected data but model predictions did not accurately reflect bobwhite detections along species-specific call-count routes in Arkansas and Louisiana. Using similar methods, we also projected northern bobwhite population distribution circa 1980 based on Land Use Land Cover data and bird survey data from 1976 to 1984. We compared our 1980 spatial projections with our spatial estimate of 1992 populations to identify areas of population change. Additionally, we used our projection of the spatial
An exactly solvable, spatial model of mutation accumulation in cancer
Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej
2016-12-01
One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.
A Matérn model of the spatial covariance structure of point rain rates
Sun, Ying
2014-07-15
It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.
Spatial Model of Deforestation in Kalimantan from 2000 to 2013
Judin Purwanto; Teddy Rusolono; Lilik Budi Prasetyo
2015-01-01
Forestry sector is the biggest carbon emission contributor in Indonesia which is mainly caused by deforestation. A significant area of forest cover still can be found in Kalimantan Island (one of the largest island in Indonesia) although an alarming rates deforestation is also exist. This study was purposed to established spatial model of deforestation in Kalimantan islands. This information is expected to provide options to develop sustainable forest management in Kalimantan trou...
Kenttä, Tuomas; Karsikas, Mari; Kiviniemi, Antti; Tulppo, Mikko; Seppänen, Tapio; Huikuri, Heikki V
2010-07-01
QRS/T angle and the cosine of the angle between QRS and T-wave vectors (TCRT), measured from standard 12-lead electrocardiogram (ECG), have been used in risk stratification of patients. This study assessed the possible rate dependence of these variables during exercise ECG in healthy subjects. Forty healthy volunteers, 20 men and 20 women, aged 34.6 +/- 3.4, underwent an exercise ECG testing. Twelve-lead ECG was recorded from each test subject and the spatial QRS/T angle and TCRT were automatically analyzed in a beat-to-beat manner with custom-made software. The individual TCRT/RR and QRST/RR patterns were fitted with seven different regression models, including a linear model and six nonlinear models. TCRT and QRS/T angle showed a significant rate dependence, with decreased values at higher heart rates (HR). In individual subjects, the second-degree polynomic model was the best regression model for TCRT/RR and QRST/RR slopes. It provided the best fit for both exercise and recovery. The overall TCRT/RR and QRST/RR slopes were similar between men and women during exercise and recovery. However, women had predominantly higher TCRT and QRS/T values. With respect to time, the dynamics of TCRT differed significantly between men and women; with a steeper exercise slope in women (women, -0.04/min vs -0.02/min in men, P exercise. The individual patterns of TCRT and QRS/T angle are affected by HR and gender. Delayed rate adaptation creates hysteresis in the TCRT/RR slopes.
The Uses and Dependency Model of Mass Communication.
Rubin, Alan M.; Windahl, Sven
1986-01-01
Responds to criticism of the uses and gratification model by proposing a modified model integrating the dependency perspective. Suggests that this integrated model broadens the heuristic application of the earlier model. (MS)
A modal approach to modeling spatially distributed vibration energy dissipation.
Energy Technology Data Exchange (ETDEWEB)
Segalman, Daniel Joseph
2010-08-01
The nonlinear behavior of mechanical joints is a confounding element in modeling the dynamic response of structures. Though there has been some progress in recent years in modeling individual joints, modeling the full structure with myriad frictional interfaces has remained an obstinate challenge. A strategy is suggested for structural dynamics modeling that can account for the combined effect of interface friction distributed spatially about the structure. This approach accommodates the following observations: (1) At small to modest amplitudes, the nonlinearity of jointed structures is manifest primarily in the energy dissipation - visible as vibration damping; (2) Correspondingly, measured vibration modes do not change significantly with amplitude; and (3) Significant coupling among the modes does not appear to result at modest amplitudes. The mathematical approach presented here postulates the preservation of linear modes and invests all the nonlinearity in the evolution of the modal coordinates. The constitutive form selected is one that works well in modeling spatially discrete joints. When compared against a mathematical truth model, the distributed dissipation approximation performs well.
Mobility dependent recombination models for organic solar cells
Wagenpfahl, Alexander
2017-09-01
Modern solar cell technologies are driven by the effort to enhance power conversion efficiencies. A main mechanism limiting power conversion efficiencies is charge carrier recombination which is a direct function of the encounter probability of both recombination partners. In inorganic solar cells with rather high charge carrier mobilities, charge carrier recombination is often dominated by energetic states which subsequently trap both recombination partners for recombination. Free charge carriers move fast enough for Coulomb attraction to be irrelevant for the encounter probability. Thus, charge carrier recombination is independent of charge carrier mobilities. In organic semiconductors charge carrier mobilities are much lower. Therefore, electrons and holes have more time react to mutual Coulomb-forces. This results in the strong charge carrier mobility dependencies of the observed charge carrier recombination rates. In 1903 Paul Langevin published a fundamental model to describe the recombination of ions in gas-phase or aqueous solutions, known today as Langevin recombination. During the last decades this model was used to interpret and model recombination in organic semiconductors. However, certain experiments especially with bulk-heterojunction solar cells reveal much lower recombination rates than predicted by Langevin. In search of an explanation, many material and device properties such as morphology and energetic properties have been examined in order to extend the validity of the Langevin model. A key argument for most of these extended models is, that electron and hole must find each other at a mutual spatial location. This encounter may be limited for instance by trapping of charges in trap states, by selective electrodes separating electrons and holes, or simply by the morphology of the involved semiconductors, making it impossible for electrons and holes to recombine at high rates. In this review, we discuss the development of mobility limited
A dependence modelling study of extreme rainfall in Madeira Island
Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra
2016-08-01
The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.
Spatial interpolation schemes of daily precipitation for hydrologic modeling
Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.
2012-01-01
Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.
Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang
2018-02-21
The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.
Modeling the spatial structure of hog production in Denmark
DEFF Research Database (Denmark)
Larue, Solène; Abildtrup, Jens; Schmitt, Bertrand
, the interaction between the location of hog production and slaughterhouses. It is the assumption that the location of slaughterhouses is influenced by the location of the primary producers, implying that this variable is endogenous, whereas the location of primary producers is independent of the location...... of slaughterhouses. This is due to the fact that transportation costs of pigs are paid by the cooperatives owning the slaughterhouses. This assumption is tested applying a spatial econometric model. The model is estimated for 1989, 1999 and 2004. In the latter period, it is the hypothesis that the demand for export...
A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution
Tagle, Felipe
2017-12-06
Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.
Lefort, Stelly; Aumont, Olivier; Bopp, Laurent; Arsouze, Thomas; Gehlen, Marion; Maury, Olivier
2015-01-01
Temperature, oxygen, and food availability directly affect marine life. Climate models project a global warming of the ocean's surface (~+3 °C), a de-oxygenation of the ocean's interior (~-3%) and a decrease in total marine net primary production (~-8%) under the 'business as usual' climate change scenario (RCP8.5). We estimated the effects of these changes on biological communities using a coupled biogeochemical (PISCES)--ecosystems (APECOSM) model forced by the physical outputs of the last generation of the IPSL-CM Earth System Model. The APECOSM model is a size-structured bio-energetic model that simulates the 3D dynamical distributions of three interactive pelagic communities (epipelagic, mesopelagic, and migratory) under the effects of multiple environmental factors. The PISCES-APECOSM model ran from 1850 to 2100 under historical forcing followed by RCP8.5. Our RCP8.5 simulation highlights significant changes in the spatial distribution, biomass, and maximum body-size of the simulated pelagic communities. Biomass and maximum body-size increase at high latitude over the course of the century, reflecting the capacity of marine organisms to respond to new suitable environment. At low- and midlatitude, biomass and maximum body-size strongly decrease. In those regions, large organisms cannot maintain their high metabolic needs because of limited and declining food availability. This resource reduction enhances the competition and modifies the biomass distribution among and within the three communities: the proportion of small organisms increases in the three communities and the migrant community that initially comprised a higher proportion of small organisms is favored. The greater resilience of small body-size organisms resides in their capacity to fulfill their metabolic needs under reduced energy supply and is further favored by the release of predation pressure due to the decline of large organisms. These results suggest that small body-size organisms might be
Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.
2017-09-01
In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.
A theory and a computational model of spatial reasoning with preferred mental models.
Ragni, Marco; Knauff, Markus
2013-07-01
Inferences about spatial arrangements and relations like "The Porsche is parked to the left of the Dodge and the Ferrari is parked to the right of the Dodge, thus, the Porsche is parked to the left of the Ferrari," are ubiquitous. However, spatial descriptions are often interpretable in many different ways and compatible with several alternative mental models. This article suggests that individuals tackle such indeterminate multiple-model problems by constructing a single, simple, and typical mental model but neglect other possible models. The model that first comes to reasoners' minds is the preferred mental model. It helps save cognitive resources but also leads to reasoning errors and illusory inferences. The article presents a preferred model theory and an instantiation of this theory in the form of a computational model, preferred inferences in reasoning with spatial mental models (PRISM). PRISM can be used to simulate and explain how preferred models are constructed, inspected, and varied in a spatial array that functions as if it were a spatial working memory. A spatial focus inserts tokens into the array, inspects the array to find new spatial relations, and relocates tokens in the array to generate alternative models of the problem description, if necessary. The article also introduces a general measure of difficulty based on the number of necessary focus operations (rather than the number of models). A comparison with results from psychological experiments shows that the theory can explain preferences, errors, and the difficulty of spatial reasoning problems. PsycINFO Database Record (c) 2013 APA, all rights reserved.
Spatially balanced topological interaction grants optimal cohesion in flocking models.
Camperi, Marcelo; Cavagna, Andrea; Giardina, Irene; Parisi, Giorgio; Silvestri, Edmondo
2012-12-06
Models of self-propelled particles (SPPs) are an indispensable tool to investigate collective animal behaviour. Originally, SPP models were proposed with metric interactions, where each individual coordinates with neighbours within a fixed metric radius. However, recent experiments on bird flocks indicate that interactions are topological: each individual interacts with a fixed number of neighbours, irrespective of their distance. It has been argued that topological interactions are more robust than metric ones against external perturbations, a significant evolutionary advantage for systems under constant predatory pressure. Here, we test this hypothesis by comparing the stability of metric versus topological SPP models in three dimensions. We show that topological models are more stable than metric ones. We also show that a significantly better stability is achieved when neighbours are selected according to a spatially balanced topological rule, namely when interacting neighbours are evenly distributed in angle around the focal individual. Finally, we find that the minimal number of interacting neighbours needed to achieve fully stable cohesion in a spatially balanced model is compatible with the value observed in field experiments on starling flocks.
Directory of Open Access Journals (Sweden)
Tatem Andrew J
2012-05-01
Full Text Available Abstract The use of Global Positioning Systems (GPS and Geographical Information Systems (GIS in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing
Tatem, Andrew J; Adamo, Susana; Bharti, Nita; Burgert, Clara R; Castro, Marcia; Dorelien, Audrey; Fink, Gunter; Linard, Catherine; John, Mendelsohn; Montana, Livia; Montgomery, Mark R; Nelson, Andrew; Noor, Abdisalan M; Pindolia, Deepa; Yetman, Greg; Balk, Deborah
2012-05-16
The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and
Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui
2018-01-01
Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are
Yun, Seong Do; Gramig, Benjamin M.
2014-01-01
This study develops and solves a stochastic, multi-year, discrete space-time model that allows the comparative analysis between non-spatial and spatially explicit models. The solution to this model implies the Stochastic Space-Time Natural Enemy-adjusted Economic Threshold (SST-NEET) to guide the choice of the optimal level of a pest that warrants management intervention. Using numerical simulation experiments over a generated synthetic geography, we derive three major conclusions. First, a u...
Spatial and spatio-temporal bayesian models with R - INLA
Blangiardo, Marta
2015-01-01
Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr
Spatial succession modeling of biological communities: a multi-model approach.
Zhang, WenJun; Wei, Wu
2009-11-01
Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.
Spatial regulation of the cAMP-dependent protein kinase during chemotactic cell migration.
Howe, Alan K; Baldor, Linda C; Hogan, Brian P
2005-10-04
Historically, the cAMP-dependent protein kinase (PKA) has a paradoxical role in cell motility, having been shown to both facilitate and inhibit actin cytoskeletal dynamics and cell migration. In an effort to understand this dichotomy, we show here that PKA is regulated in subcellular space during cell migration. Immunofluorescence microscopy and biochemical enrichment of pseudopodia showed that type II regulatory subunits of PKA and PKA activity are enriched in protrusive cellular structures formed during chemotaxis. This enrichment correlates with increased phosphorylation of key cytoskeletal substrates for PKA, including the vasodilator-stimulated phosphoprotein (VASP) and the protein tyrosine phosphatase containing a PEST motif. Importantly, inhibition of PKA activity or its ability to interact with A kinase anchoring proteins inhibited the activity of the Rac GTPase within pseudopodia. This effect correlated with both decreased guanine nucleotide exchange factor activity and increased GTPase activating protein activity. Finally, inhibition of PKA anchoring, like inhibition of total PKA activity, inhibited pseudopod formation and chemotactic cell migration. These data demonstrate that spatial regulation of PKA via anchoring is an important facet of normal chemotactic cell movement.
Dependency of energy and spatial distributions of photons on edge of object in brain SPECT
Energy Technology Data Exchange (ETDEWEB)
Deloar, H.M.; Watabe, Hiroshi; Kudomi, Nobuyuki; Kim, Kyeong-Min; Aoi, Toshiyuki; Iida, Hidehiro [National Cardiovascular Center, Suita, Osaka (Japan). Research Inst.
2003-04-01
Accurate {mu} maps are important for quantitative image reconstruction in SPECT. The Compton scatter energy window (CSW) technique has been proposed to define the outline of objects. In this technique, a lower energy window image is acquired in addition to the main photo-peak energy window. The image of the lower energy window is used to estimate the edge of the scanned object to produce a constant attenuation map. The aim of this study was to investigate the dependency of CSW on the spatial and energy distribution of radioisotope to predict the edges of objects. Two particular cases of brain study were considered, namely uniform distribution and non-uniform distribution using Monte Carlo simulation and experiments with uniform cylindrical phantom and hotspot phantom. The phantoms were filled with water and a radioactive solution of {sup 99m}Tc. For each phantom, 20%, 30%, 40% and 50% thresholds of the mean profile were applied to estimate E{sub wt}, the energy window for minimum difference between the estimated and true edge of objects. The E{sub wt}'s were 100-120 keV with a 40% threshold and 92-114 keV with a 30% threshold for uniform and hotspot phantoms, respectively. Edge of the objects with CSW technique varies with energy window and thresholds. Careful setting of the energy window is required to use the CSW technique. (author)
Dependency of energy and spatial distributions of photons on edge of object in brain SPECT
Deloar, H M; Kudomi, N; Kim, K M; Aoi, T; Iida, H
2003-01-01
Accurate mu maps are important for quantitative image reconstruction in SPECT. The Compton scatter energy window (CSW) technique has been proposed to define the outline of objects. In this technique, a lower energy window image is acquired in addition to the main photo-peak energy window. The image of the lower energy window is used to estimate the edge of the scanned object to produce a constant attenuation map. The aim of this study was to investigate the dependency of CSW on the spatial and energy distribution of radioisotope to predict the edges of objects. Two particular cases of brain study were considered, namely uniform distribution and non-uniform distribution using Monte Carlo simulation and experiments with uniform cylindrical phantom and hotspot phantom. The phantoms were filled with water and a radioactive solution of sup 9 sup 9 sup m Tc. For each phantom, 20%, 30%, 40% and 50% thresholds of the mean profile were applied to estimate E sub w sub t , the energy window for minimum difference betwee...
Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach
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Hongqiang Liu
2017-01-01
Full Text Available A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.
Wiens, J. David; Schumaker, Nathan H.; Inman, Richard D.; Esque, Todd C.; Longshore, Kathleen M.; Nussear, Kenneth E
2017-01-01
Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (Aquila chrysaetos). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.
Jin, Ick Hoon; Yuan, Ying; Bandyopadhyay, Dipankar
2016-01-01
Research in dental caries generates data with two levels of hierarchy: that of a tooth overall and that of the different surfaces of the tooth. The outcomes often exhibit spatial referencing among neighboring teeth and surfaces, i.e., the disease status of a tooth or surface might be influenced by the status of a set of proximal teeth/surfaces. Assessments of dental caries (tooth decay) at the tooth level yield binary outcomes indicating the presence/absence of teeth, and trinary outcomes at the surface level indicating healthy, decayed, or filled surfaces. The presence of these mixed discrete responses complicates the data analysis under a unified framework. To mitigate complications, we develop a Bayesian two-level hierarchical model under suitable (spatial) Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. At the first level, we utilize an autologistic model to accommodate the spatial dependence for the tooth-level binary outcomes. For the second level and conditioned on a tooth being non-missing, we utilize a Potts model to accommodate the spatial referencing for the surface-level trinary outcomes. The regression models at both levels were controlled for plausible covariates (risk factors) of caries, and remain connected through shared parameters. To tackle the computational challenges in our Bayesian estimation scheme caused due to the doubly-intractable normalizing constant, we employ a double Metropolis-Hastings sampler. We compare and contrast our model performances to the standard non-spatial (naive) model using a small simulation study, and illustrate via an application to a clinical dataset on dental caries.
Modeling temporal and spatial variability of crop yield
Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.
2014-12-01
In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.
Assessing fit in Bayesian models for spatial processes
Jun, M.
2014-09-16
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
Sustainable Street Vendors Spatial Zoning Models in Surakarta
Rahayu, M. J.; Putri, R. A.; Rini, E. F.
2018-02-01
Various strategies that have been carried out by Surakarta’s government to organize street vendors have not achieved the goal of street vendors’ arrangement comprehensively. The street vendors arrangement strategy consists of physical (spatial) and non-physical. One of the physical arrangements is to define the street vendor’s zoning. Based on the street vendors’ characteristics, there are two alternative locations of stabilization (as one kind of street vendors’ arrangement) that can be used. The aim of this study is to examine those alternative locations to set the street vendor’s zoning models. Quatitative method is used to formulate the spatial zoning model. The street vendor’s zoning models are formulated based on two approaches, which are the distance to their residences and previous trading locations. Geographic information system is used to indicate all street vendors’ residences and trading locations based on their type of goods. Through proximity point distance tool on ArcGIS, we find the closeness of residential location and previous trading location with the alternative location of street vendors’ stabilization. The result shows that the location was chosen by the street vendors to sell their goods mainly consider the proximity to their homes. It also shows street vendor’s zoning models which based on the type of street vendor’s goods.
Towards Quantitative Spatial Models of Seabed Sediment Composition.
Directory of Open Access Journals (Sweden)
David Stephens
Full Text Available There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.
Spatial modelling and mapping of female genital mutilation in Kenya
2014-01-01
Background Female genital mutilation/cutting (FGM/C) is still prevalent in several communities in Kenya and other areas in Africa, as well as being practiced by some migrants from African countries living in other parts of the world. This study aimed at detecting clustering of FGM/C in Kenya, and identifying those areas within the country where women still intend to continue the practice. A broader goal of the study was to identify geographical areas where the practice continues unabated and where broad intervention strategies need to be introduced. Methods The prevalence of FGM/C was investigated using the 2008 Kenya Demographic and Health Survey (KDHS) data. The 2008 KDHS used a multistage stratified random sampling plan to select women of reproductive age (15–49 years) and asked questions concerning their FGM/C status and their support for the continuation of FGM/C. A spatial scan statistical analysis was carried out using SaTScan™ to test for statistically significant clustering of the practice of FGM/C in the country. The risk of FGM/C was also modelled and mapped using a hierarchical spatial model under the Integrated Nested Laplace approximation approach using the INLA library in R. Results The prevalence of FGM/C stood at 28.2% and an estimated 10.3% of the women interviewed indicated that they supported the continuation of FGM. On the basis of the Deviance Information Criterion (DIC), hierarchical spatial models with spatially structured random effects were found to best fit the data for both response variables considered. Age, region, rural–urban classification, education, marital status, religion, socioeconomic status and media exposure were found to be significantly associated with FGM/C. The current FGM/C status of a woman was also a significant predictor of support for the continuation of FGM/C. Spatial scan statistics confirm FGM clusters in the North-Eastern and South-Western regions of Kenya (p < 0.001). Conclusion This suggests that the
Spatial extrapolation of light use efficiency model parameters to predict gross primary production
Directory of Open Access Journals (Sweden)
Karsten Schulz
2011-12-01
Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.
Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County
International Nuclear Information System (INIS)
Coker, Eric; Ghosh, Jokay; Jerrett, Michael; Gomez-Rubio, Virgilio; Beckerman, Bernardo; Cockburn, Myles; Liverani, Silvia; Su, Jason; Li, Arthur; Kile, Molly L; Ritz, Beate; Molitor, John
2015-01-01
multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM 2.5 effects on term low birth weight (TLBW). • PM 2.5 effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM 2.5 health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM 2.5 effects.
Investigation of Spatial Variation of Sea States Offshore of Humboldt Bay CA Using a Hindcast Model.
Energy Technology Data Exchange (ETDEWEB)
Dallman, Ann Renee; Neary, Vincent Sinclair
2014-10-01
Spatial variability of sea states is an important consideration when performing wave resource assessments and wave resource characterization studies for wave energy converter (WEC) test sites and commercial WEC deployments. This report examines the spatial variation of sea states offshore of Humboldt Bay, CA, using the wave model SWAN . The effect of depth and shoaling on bulk wave parameters is well resolved using the model SWAN with a 200 m grid. At this site, the degree of spatial variation of these bulk wave parameters, with shoaling generally perpendicular to the depth contours, is found to depend on the season. The variation in wave height , for example, was higher in the summer due to the wind and wave sheltering from the protruding land on the coastline north of the model domain. Ho wever, the spatial variation within an area of a potential Tier 1 WEC test site at 45 m depth and 1 square nautical mile is almost negligible; at most about 0.1 m in both winter and summer. The six wave characterization parameters recommended by the IEC 6 2600 - 101 TS were compared at several points along a line perpendicular to shore from the WEC test site . As expected, these parameters varied based on depth , but showed very similar seasonal trends.
Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media: SCALE-DEPENDENT UQ
Energy Technology Data Exchange (ETDEWEB)
Tartakovsky, A. M. [Computational Mathematics Group, Pacific Northwest National Laboratory, Richland WA USA; Panzeri, M. [Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milano Italy; Tartakovsky, G. D. [Hydrology Group, Pacific Northwest National Laboratory, Richland WA USA; Guadagnini, A. [Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milano Italy
2017-11-01
Equations governing flow and transport in heterogeneous porous media are scale-dependent. We demonstrate that it is possible to identify a support scale $\\eta^*$, such that the typically employed approximate formulations of Moment Equations (ME) yield accurate (statistical) moments of a target environmental state variable. Under these circumstances, the ME approach can be used as an alternative to the Monte Carlo (MC) method for Uncertainty Quantification in diverse fields of Earth and environmental sciences. MEs are directly satisfied by the leading moments of the quantities of interest and are defined on the same support scale as the governing stochastic partial differential equations (PDEs). Computable approximations of the otherwise exact MEs can be obtained through perturbation expansion of moments of the state variables in orders of the standard deviation of the random model parameters. As such, their convergence is guaranteed only for the standard deviation smaller than one. We demonstrate our approach in the context of steady-state groundwater flow in a porous medium with a spatially random hydraulic conductivity.
Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.
2017-10-01
Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS
NOTE: Spatial dependence of the phase in localized bioelectrical impedance analysis
Shiffman, C. A.; Aaron, R.; Altman, A.
2001-04-01
The variety of phase functions, θ(z) = arctan X(z)/R(z), observed earlier on the thighs of healthy and seriously ill subjects via localized bioelectrical impedance analysis, can be represented by a model which combines realistic thigh shapes with homogeneous, axially symmetric conductivity tensors. While quantitative results depend sensitively on the way current is injected, it appears to be generally true that dθ/dz φz (and vice versa), where φr and φz are the phases of the radial and longitudinal conductivity components.
Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China
Directory of Open Access Journals (Sweden)
Jianguo Chen
2017-05-01
Full Text Available The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB, Bayesian hierarchical model (BHM and the geographically weighted Poisson regression model (GWPR were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE of BHM and GWPR was decreased by 97.88% and 77.15%, and the R d 2 of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R d 2 . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.
A Spatial Model of the Biomass to Energy Cycle
DEFF Research Database (Denmark)
Möller, Bernd
2003-01-01
by location. This paper aims to contribute to the development of a biomass to energy evaluation and mapping system, using geographical information systems (GIS). A GIS-based in-forest residue model considers forest growth and choice of harvest method. Data from a sawmill survey is used to assess sawmill resi......-dues. For both sources the costs of road transportation have been modelled using spatial cost allocation. As emphasis has been on using public data, the model is still a rough es-timate, which could be improved using forest industry data and refined algorithms. As a first result, the cost distribution...... and the costs of accumulated amounts of wood residues can now be calculated almost instantly for each location in the country. It is assumed that this approach will facilitate the assessment of future biomass markets....
Modelling spatial-temporal and coordinative parameters in swimming.
Seifert, L; Chollet, D
2009-07-01
This study modelled the changes in spatial-temporal and coordinative parameters through race paces in the four swimming strokes. The arm and leg phases in simultaneous strokes (butterfly and breaststroke) and the inter-arm phases in alternating strokes (crawl and backstroke) were identified by video analysis to calculate the time gaps between propulsive phases. The relationships among velocity, stroke rate, stroke length and coordination were modelled by polynomial regression. Twelve elite male swimmers swam at four race paces. Quadratic regression modelled the changes in spatial-temporal and coordinative parameters with velocity increases for all four strokes. First, the quadratic regression between coordination and velocity showed changes common to all four strokes. Notably, the time gaps between the key points defining the beginning and end of the stroke phases decreased with increases in velocity, which led to decreases in glide times and increases in the continuity between propulsive phases. Conjointly, the quadratic regression among stroke rate, stroke length and velocity was similar to the changes in coordination, suggesting that these parameters may influence coordination. The main practical application for coaches and scientists is that ineffective time gaps can be distinguished from those that simply reflect an individual swimmer's profile by monitoring the glide times within a stroke cycle. In the case of ineffective time gaps, targeted training could improve the swimmer's management of glide time.
Experimental Measurement, Analysis and Modelling of Dependency ...
African Journals Online (AJOL)
This leads us to apply the method of optimal linearization associated the finite element method with the nonlinear problem of transfer of heat if thermal conductivity, the specific heat and the emissivity of studied material depend on the temperature. We obtain a good agreement between the resolution of the nonlinear ...
Objective Tuning of Model Parameters in CAM5 Across Different Spatial Resolutions
Bulaevskaya, V.; Lucas, D. D.
2014-12-01
Parameterizations of physical processes in climate models are highly dependent on the spatial and temporal resolution and must be tuned for each resolution under consideration. At high spatial resolutions, objective methods for parameter tuning are computationally prohibitive. Our work has focused on calibrating parameters in the Community Atmosphere Model 5 (CAM5) for three spatial resolutions: 1, 2, and 4 degrees. Using perturbed-parameter ensembles and uncertainty quantification methodology, we have identified input parameters that minimize discrepancies of energy fluxes simulated by CAM5 across the three resolutions and with respect to satellite observations. We are also beginning to exploit the parameter-resolution relationships to objectively tune parameters in a high-resolution version of CAM5 by leveraging cheaper, low-resolution simulations and statistical models. We will present our approach to multi-resolution climate model parameter tuning, as well as the key findings. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and was supported from the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC) project on Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System.
Incorporating Pass-Phrase Dependent Background Models for Text-Dependent Speaker Verification
Sarkar, A. K.; Tan, Zheng-Hua
2016-01-01
In this paper, we propose pass-phrase dependent background models (PBMs) for text-dependent (TD) speaker verification (SV) to integrate the pass-phrase identification process into the conventional TD-SV system, where a PBM is derived from a text-independent background model through adaptation using the utterances of a particular pass-phrase. During training, pass-phrase specific target speaker models are derived from the particular PBM using the training data for the respective target model. ...
The effect of spatially heterogeneous damage in simple models of earthquake fault networks
Tiampo, K. F.; Dominguez, R.; Klein, W.; Serino, C.; Kazemian, J.
2011-12-01
Natural earthquake fault systems are highly heterogeneous in space; inhomogeneities occur because the earth is made of a variety of materials of different strengths and dissipate stress differently. Because the spatial arrangement of these materials is dependent on the geologic history, the spatial distribution of these various materials can be quite complex and occur over a variety of length scales. One way that the inhomogeneous nature of fault systems manifests itself is in the spatial patterns which emerge in seismicity (Tiampo et al., 2002, 2007). Despite their inhomogeneous nature, real faults are often modeled as spatially homogeneous systems. One argument for this approach is that earthquake faults experience long range stress transfer, and if this range is longer than the length scales associated with the inhomogeneities of the system, the dynamics of the system may be unaffected by the inhomogeneities. However, it is not clear that this is always the case. In this work we study the scaling of earthquake models that are variations of Olami-Feder-Christensen (OFC) and Burridge-Knopoff (BK) models, in order to explore the effect of spatial inhomogeneities on earthquake-like systems when interaction ranges are long, but not necessarily longer than the distances associated with the inhomogeneities of the system (Burridge and L. Knopoff, 1967; Rundle and Jackson, 1977; Olami et al., 1988). For long ranges and without inhomogeneities, such models have been found to produce scaling similar to GR scaling found in real earthquake systems (Rundle and Klein, 1993). In the earthquake models discussed here, damage is distributed inhomogeneously throughout and the interaction ranges, while long, are not longer than all of the damage length scales. In addition, we attempt to model the effect of a fixed distribution of asperities, and find that this has an effect on the magnitude-frequency relation, producing larger events at regular intervals, We find that the scaling
The role of basolateral amygdala adrenergic receptors in hippocampus dependent spatial memory in rat
Directory of Open Access Journals (Sweden)
Vafaei A.L.
2008-03-01
Full Text Available Background and the purpose of the study: There are extensive evidences indicating that the noradrenergic system of the basolateral nucleus of the amygdala (BLA is involved in memory processes. The present study investigated the role of the BLA adrenergic receptors (ARs in hippocampus dependent spatial memory in place avoidance task in male rat. Material and Methods: Long Evans rats (n=150 were trained to avoid footshock in a 60° segment while foraging for scattered food on a circular (80-cm diameter arena. The rats were injected bilaterally in the BLA specific ARS (Adrenergic receptors agonist norepinephrine (NE, 0.5 and 1 µg/µl and specific β-ARs antagonist propranolol (PRO, 0.5 and 1 µg/µl before acquisition, after training or before retrieval of the place avoidance task. Control rats received vehicle at the same volume. The learning in a single 30-min session was assessed 24h later by a 30-min extinction trial in which the time to first entrance and the number of entrances to the shocked area measured the avoidance memory. Results: Acquisition and consolidation were enhanced and impaired significantly by NE and PRO when the drugs were injected 10 min before or immediately after training, respectively. In contrast, neither NE nor PRO influenced animal performances when injected before retention testing. Conclusion: Findings of this study indicates that adrenergic system of the BLA plays an important role in regulation of memory storage and show further evidences for the opinion that the BLA plays an important role in integrating hormonal and neurotransmitter influences on memory storage.
Simulation of time-dependent Heisenberg models in one dimension
DEFF Research Database (Denmark)
Volosniev, A. G.; Hammer, H. -W.; Zinner, N. T.
2016-01-01
constants can be manipulated by time-dependent driving of the shape of the external confinement. As illustrative examples, we consider a harmonic trapping potential with a variable frequency and an infinite square well potential with a time-dependent barrier in the middle.......In this Letter, we provide a theoretical analysis of strongly interacting quantum systems confined by a time-dependent external potential in one spatial dimension. We show that such systems can be used to simulate spin chains described by Heisenberg Hamiltonians in which the exchange coupling...
Spatial air pollution modelling for a West-African town
Directory of Open Access Journals (Sweden)
Sirak Zenebe Gebreab
2015-11-01
Full Text Available Land use regression (LUR modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2 concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS. Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.
An alternative to the standard spatial econometric approaches in hedonic house price models
DEFF Research Database (Denmark)
von Graevenitz, Kathrine; Panduro, Toke Emil
2015-01-01
Omitted, misspecified, or mismeasured spatially varying characteristics are a cause for concern in hedonic house price models. Spatial econometrics or spatial fixed effects have become popular ways of addressing these concerns. We discuss the limitations of standard spatial approaches to hedonic...
Does a hospital's quality depend on the quality of other hospitals? A spatial econometrics approach.
Gravelle, Hugh; Santos, Rita; Siciliani, Luigi
2014-11-01
We examine whether a hospital's quality is affected by the quality provided by other hospitals in the same market. We first sketch a theoretical model with regulated prices and derive conditions on demand and cost functions which determine whether a hospital will increase its quality if its rivals increase their quality. We then apply spatial econometric methods to a sample of English hospitals in 2009-10 and a set of 16 quality measures including mortality rates, readmission, revision and redo rates, and three patient reported indicators, to examine the relationship between the quality of hospitals. We find that a hospital's quality is positively associated with the quality of its rivals for seven out of the sixteen quality measures. There are no statistically significant negative associations. In those cases where there is a significant positive association, an increase in rivals' quality by 10% increases a hospital's quality by 1.7% to 2.9%. The finding suggests that for some quality measures a policy which improves the quality in one hospital will have positive spillover effects on the quality in other hospitals.
Royle, J. Andrew; Converse, Sarah J.
2014-01-01
Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.
A Biophysical Neural Model To Describe Spatial Visual Attention
International Nuclear Information System (INIS)
Hugues, Etienne; Jose, Jorge V.
2008-01-01
Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations
Spatially-explicit models of global tree density
Glick, Henry B.; Bettigole, Charlie; Maynard, Daniel S.; Covey, Kristofer R.; Smith, Jeffrey R.; Crowther, Thomas W.
2016-01-01
Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services. PMID:27529613
Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia
Redzuan Tahar, Mohammad; Kamarudin, Farahana; Umar, Roslan; Khairul Amri Kamarudin, Mohd; Sabri, Nor Hazmin; Ahmad, Karzaman; Rahim, Sobri Abdul; Sharul Aikal Baharim, Mohd
2017-03-01
Sky brightness is an essential topic in the field of astronomy, especially for optical astronomical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter (SQM) manufactured by Unihedron are used to measure the sky brightness on a moonless night (or when the Moon is below the horizon), when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System (GIS), thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec{}-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × {10}-4{cd} {{{m}}}-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.
A Temperature-Dependent Hysteresis Model for Relaxor Ferroelectric Compounds
National Research Council Canada - National Science Library
Raye, Julie K; Smith, Ralph C
2004-01-01
This paper summarizes the development of a homogenized free energy model which characterizes the temperature-dependent hysteresis and constitutive nonlinearities inherent to relaxor ferroelectric materials...
Juchem Neto, J. P.; Claeyssen, J. C. R.; Pôrto Júnior, S. S.
2018-03-01
In this paper we introduce capital transport cost in a unidimensional spatial Solow-Swan model of economic growth with capital-induced labor migration, considered in an unbounded domain. Proceeding with a stability analysis, we show that there is a critical value for the capital transport cost where the dynamic behavior of the economy changes, provided that the intensity of capital-induced labor migration is strong enough. On the one hand, if the capital transport cost is higher than this critical value, the spatially homogeneous equilibrium of coexistence of the model is stable, and the economy converges to this spatially homogeneous state in the long run; on the other hand, if transport cost is lower than this critical value, the equilibrium is unstable, and the economy may develop different spatio-temporal dynamics, including the formation of stable economic agglomerations and spatio-temporal economic cycles, depending on the other parameters in the model. Finally, numerical simulations support the results of the stability analysis, and illustrate the spatio-temporal dynamics generated by the model, suggesting that the economy as a whole benefits from the formation of economic agglomerations and cycles, with a higher capital transport cost reducing this gain.
Time dependent viscous string cloud cosmological models
Tripathy, S. K.; Nayak, S. K.; Sahu, S. K.; Routray, T. R.
2009-09-01
Bianchi type-I string cosmological models are studied in Saez-Ballester theory of gravitation when the source for the energy momentum tensor is a viscous string cloud coupled to gravitational field. The bulk viscosity is assumed to vary with time and is related to the scalar expansion. The relationship between the proper energy density ρ and string tension density λ are investigated from two different cosmological models.
Input-dependent wave attenuation in a critically-balanced model of cortex.
Directory of Open Access Journals (Sweden)
Xiao-Hu Yan
Full Text Available A number of studies have suggested that many properties of brain activity can be understood in terms of critical systems. However it is still not known how the long-range susceptibilities characteristic of criticality arise in the living brain from its local connectivity structures. Here we prove that a dynamically critically-poised model of cortex acquires an infinitely-long ranged susceptibility in the absence of input. When an input is presented, the susceptibility attenuates exponentially as a function of distance, with an increasing spatial attenuation constant (i.e., decreasing range the larger the input. This is in direct agreement with recent results that show that waves of local field potential activity evoked by single spikes in primary visual cortex of cat and macaque attenuate with a characteristic length that also increases with decreasing contrast of the visual stimulus. A susceptibility that changes spatial range with input strength can be thought to implement an input-dependent spatial integration: when the input is large, no additional evidence is needed in addition to the local input; when the input is weak, evidence needs to be integrated over a larger spatial domain to achieve a decision. Such input-strength-dependent strategies have been demonstrated in visual processing. Our results suggest that input-strength dependent spatial integration may be a natural feature of a critically-balanced cortical network.
Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone
Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo
2017-12-01
The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.
A multi-scale spatial model of hepatitis-B viral dynamics.
Directory of Open Access Journals (Sweden)
Quentin Cangelosi
Full Text Available Chronic hepatitis B viral infection (HBV afflicts around 250 million individuals globally and few options for treatment exist. Once infected, the virus entrenches itself in the liver with a notoriously resilient colonisation of viral DNA (covalently-closed circular DNA, cccDNA. The majority of infections are cleared, yet we do not understand why 5% of adult immune responses fail leading to the chronic state with its collateral morbid effects such as cirrhosis and eventual hepatic carcinoma. The liver environment exhibits particularly complex spatial structures for metabolic processing and corresponding distributions of nutrients and transporters that may influence successful HBV entrenchment. We assembled a multi-scaled mathematical model of the fundamental hepatic processing unit, the sinusoid, into a whole-liver representation to investigate the impact of this intrinsic spatial heterogeneity on the HBV dynamic. Our results suggest HBV may be exploiting spatial aspects of the liver environment. We distributed increased HBV replication rates coincident with elevated levels of nutrients in the sinusoid entry point (the periportal region in tandem with similar distributions of hepatocyte transporters key to HBV invasion (e.g., the sodium-taurocholate cotransporting polypeptide or NTCP, or immune system activity. According to our results, such co-alignment of spatial distributions may contribute to persistence of HBV infections, depending on spatial distributions and intensity of immune response as well. Moreover, inspired by previous HBV models and experimentalist suggestions of extra-hepatic HBV replication, we tested in our model influence of HBV blood replication and observe an overall nominal effect on persistent liver infection. Regardless, we confirm prior results showing a solo cccDNA is sufficient to re-infect an entire liver, with corresponding concerns for transplantation and treatment.
Mutsvari, Timothy; Bandyopadhyay, Dipankar; Declerck, Dominique; Lesaffre, Emmanuel
2013-12-30
Dental caries is a highly prevalent disease affecting the tooth's hard tissues by acid-forming bacteria. The past and present caries status of a tooth is characterized by a response called caries experience (CE). Several epidemiological studies have explored risk factors for CE. However, the detection of CE is prone to misclassification because some cases are neither clearly carious nor noncarious, and this needs to be incorporated into the epidemiological models for CE data. From a dentist's point of view, it is most appealing to analyze CE on the tooth's surface, implying that the multilevel structure of the data (surface-tooth-mouth) needs to be taken into account. In addition, CE data are spatially referenced, that is, an active lesion on one surface may impact the decay process of the neighboring surfaces, and that might also influence the process of scoring CE. In this paper, we investigate two hypotheses: that is, (i) CE outcomes recorded at surface level are spatially associated; and (ii) the dental examiners exhibit some spatial behavior while scoring CE at surface level, by using a spatially referenced multilevel autologistic model, corrected for misclassification. These hypotheses were tested on the well-known Signal Tandmobiel® study on dental caries, and simulation studies were conducted to assess the effect of misclassification and strength of spatial dependence on the autologistic model parameters. Our results indicate a substantial spatial dependency in the examiners' scoring behavior and also in the prevalence of CE at surface level. Copyright © 2013 John Wiley & Sons, Ltd.
Is a matrix exponential specification suitable for the modeling of spatial correlation structures?
Strauß, Magdalena E; Mezzetti, Maura; Leorato, Samantha
2017-05-01
This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms.
The spin dependent odderon in the diquark model
Energy Technology Data Exchange (ETDEWEB)
Szymanowski, Lech [National Centre for Nuclear Research (NCBJ), Warsaw (Poland); Zhou, Jian, E-mail: jzhou@sdu.edu.cn [School of Physics, & Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Jinan, Shandong 250100 (China); Nikhef and Department of Physics and Astronomy, VU University Amsterdam, De Boelelaan 1081, NL-1081 HV Amsterdam (Netherlands)
2016-09-10
In this short note, we report a di-quark model calculation for the spin dependent odderon and demonstrate that the asymmetrical color source distribution in the transverse plane of a transversely polarized hadron plays an essential role in yielding the spin dependent odderon. This calculation confirms the earlier finding that the spin dependent odderon is closely related to the parton orbital angular momentum.
Stochastic population growth in spatially heterogeneous environments: the density-dependent case.
Hening, Alexandru; Nguyen, Dang H; Yin, George
2018-02-01
This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for resources, spatio-temporal heterogeneity and dispersal. The population is spread throughout n patches whose population abundances are modeled as the solutions of a system of nonlinear stochastic differential equations living on [Formula: see text]. We prove that r, the stochastic growth rate of the total population in the absence of competition, determines the long-term behaviour of the population. The parameter r can be expressed as the Lyapunov exponent of an associated linearized system of stochastic differential equations. Detailed analysis shows that if [Formula: see text], the population abundances converge polynomially fast to a unique invariant probability measure on [Formula: see text], while when [Formula: see text], the population abundances of the patches converge almost surely to 0 exponentially fast. This generalizes and extends the results of Evans et al. (J Math Biol 66(3):423-476, 2013) and proves one of their conjectures. Compared to recent developments, our model incorporates very general density-dependent growth rates and competition terms. Furthermore, we prove that persistence is robust to small, possibly density dependent, perturbations of the growth rates, dispersal matrix and covariance matrix of the environmental noise. We also show that the stochastic growth rate depends continuously on the coefficients. Our work allows the environmental noise driving our system to be degenerate. This is relevant from a biological point of view since, for example, the environments of the different patches can be perfectly correlated. We show how one can adapt the nondegenerate results to the degenerate setting. As an example we fully analyze the two-patch case, [Formula: see text], and show that the stochastic growth rate is a decreasing function of the dispersion rate. In particular, coupling two
Aloisio, Jason M; Palmer, Matthew I; Giampieri, Mario A; Tuininga, Amy R; Lewis, James D
2017-01-01
Plant survivorship depends on biotic and abiotic factors that vary at local and regional scales. This survivorship, in turn, has cascading effects on community composition and the physical structure of vegetation. Survivorship of native plant species is variable among populations planted in environmentally stressful habitats like urban roofs, but the degree to which factors at different spatial scales affect survivorship in urban systems is not well understood. We evaluated the effects of biotic and abiotic factors on survivorship, composition, and physical structure of two native perennial species assemblages, one characterized by a mixture of C 4 grasses and forbs (Hempstead Plains, HP) and one characterized by a mixture of C 3 grasses and forbs (Rocky Summit, RS), that were initially sown at equal ratios of growth forms (5:1:4; grass, N-fixing forb and non-N-fixing forb) in replicate 2-m 2 plots planted on 10 roofs in New York City (New York, USA). Of 24 000 installed plants, 40% survived 23 months after planting. Within-roof factors explained 71% of variation in survivorship, with biotic (species identity and assemblage) factors accounting for 54% of the overall variation, and abiotic (growing medium depth and plot location) factors explaining 17% of the variation. Among-roof factors explained 29% of variation in survivorship and increased solar radiation correlated with decreased survivorship. While growing medium properties (pH, nutrients, metals) differed among roofs there was no correlation with survivorship. Percent cover and sward height increased with increasing survivorship. At low survivorship, cover of the HP assemblage was greater compared to the RS assemblage. Sward height of the HP assemblage was about two times greater compared to the RS assemblage. These results highlight the effects of local biotic and regional abiotic drivers on community composition and physical structure of green roof vegetation. As a result, initial green roof plant
Spatial dependence of 2MASS luminosity and mass functions in the old open cluster NGC 188
Bonatto, C.; Bica, E.; Santos, J. F. C., Jr.
2005-04-01
Luminosity and mass functions in the old open cluster NGC 188 are analysed by means of J and H 2MASS photometry, which provides uniformity and spatial coverage for a proper background subtraction. With an age of about 6-8 Gyr, NGC 188 is expected to be suffering the effects of advanced dynamical evolution. Indeed, previous works in optical bands have suggested the presence of mass segregation. Within the uncertainties, the observed projected radial density profile of NGC 188 departs from the two-parameter King model in two inner regions, which reflects the non-virialized dynamical state and possibly, some degree of non-sphericity in the spatial shape of this old open cluster. Fits with two and three-parameter King models to the radial distribution of stars resulted in a core radius Rcore=1.3±0.1 pc and a tidal radius Rtidal=21±4 pc, about twice as large as the visual limiting radius. The concentration parameter c=1.2±0.1 of NGC 188 makes this open cluster structurally comparable to the loose globular clusters. The present 2MASS analysis resulted in significant slope variations with distance in the mass function φ(m)∝ m-(1+χ), being flat in the central parts (χ=0.6±0.7) and steep in the cluster outskirts (χ=7.2±0.6). The overall mass function has a slope χ=1.9±0.7, slightly steeper than a standard Salpeter mass function. In this context, NGC 188 is similar to the 3.2 Gyr, dynamically evolved open cluster M 67. Solar metallicity Padova isochrone fits to the near-infrared colour-magnitude diagram of NGC 188 resulted in an age of 7.0±1.0 Gyr. The best fit, obtained with the 7.1 Gyr isochrone, produced a distance modulus (m-M)0=11.1±0.1, E(B-V)=0.0, and a distance to the Sun d⊙=1.66±0.08 kpc. The observed stellar mass (in the range 0.98 M⊙- 1.08 M⊙) in NGC 188 is mobs=380±12 M⊙. A simple extrapolation of the observed overall mass function to stars with 0.08 M⊙ resulted in a total present mass of mtot˜(1.8±0.7)×104 M⊙. On the other hand
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
Experimental Measurement, Analysis and Modelling of Dependency ...
African Journals Online (AJOL)
We propose a direct method of measurement of the total emissivity of opaque samples on a range of temperature around the ambient one. The method rests on the modulation of the temperature of the sample and the infra-red signal processing resulting from the surface of the sample we model the total emissivity obtained ...
Modelling Spatial and Temporal Fault Zone Evolution in Basement Rocks
Lunn, R. J.; Willson, J. P.; Shipton, Z. K.
2006-12-01
There is considerable industrial interest in assessing the permeability of faults for the purpose of oil and gas production, deep well injection of waste liquids, underground storage of natural gas and disposal of radioactive waste. Prior estimation of fault hydraulic properties is highly error prone. Faults zones are formed through a complex interaction of mechanical, hydraulic and chemical processes and their permeability varies considerably over both space and time. Algorithms for predicting fault seal potential using throw and host rock property data exist for clay-rich fault seals but are contentious. In the case of crystalline rocks and sand-sand contacts, no such algorithms exist. In any case, the study of fault growth processes does not suggest that there is a clear or simple relationship between fault throw and the fault zone permeability. To improve estimates of fault zone permeability, it is important to understand the underlying hydro-mechanical processes of fault zone formation. In this research, we explore the spatial and temporal evolution of fault zones through development and application of a 2D hydro-mechanical finite element model. The development of fault zone damage is simulated perpendicular to the main slip surface using a fully coupled solution of Navier's equation for mechanical deformation and Darcy's Law/conservation of fluid mass for subsurface fluid flow. The model is applied to study development of fault zones in basement rocks, based on the conceptual model of S. J. Martell, J. Struct. Geol. 12(7):869-882, 1990. We simulate the evolution of fault zones from pre-existing joints and explore controls on the growth rate and locations of multiple splay fractures which link-up to form complex damage zones. We are the first researchers to successfully simulate the temporal and spatial evolution of multiple wing cracks, tertiary fracturing, antithetic fractures propagating into the compressive region, infill fracturing between faults and
Time-dependent intranuclear cascade model
International Nuclear Information System (INIS)
Barashenkov, V.S.; Kostenko, B.F.; Zadorogny, A.M.
1980-01-01
An intranuclear cascade model with explicit consideration of the time coordinate in the Monte Carlo simulation of the development of a cascade particle shower has been considered. Calculations have been performed using a diffuse nuclear boundary without any step approximation of the density distribution. Changes in the properties of the target nucleus during the cascade development have been taken into account. The results of these calculations have been compared with experiment and with the data which had been obtained by means of a time-independent cascade model. The consideration of time improved agreement between experiment and theory particularly for high-energy shower particles; however, for low-energy cascade particles (with grey and black tracks in photoemulsion) a discrepancy remains at T >= 10 GeV. (orig.)
Spatial Modeling of Risk in Natural Resource Management
Directory of Open Access Journals (Sweden)
Peter Jones
2002-01-01
Full Text Available Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods
The lack of spatial soil erosion data has been a major constraint on the refinement and application of physically based erosion models. Spatially distributed models can only be thoroughly validated with distributed erosion data. The fallout cesium-137 has been widely used to generate spatial soil re...
Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
Schroter, M.; Remme, R.P.; Sumarga, E.; Barton, D.N.; Hein, L.G.
2015-01-01
Assessment of ecosystem services through spatial modelling plays a key role in ecosystem accounting. Spatial models for ecosystem services try to capture spatial heterogeneity with high accuracy. This endeavour, however, faces several practical constraints. In this article we analyse the trade-offs
Mapping snow depth return levels: smooth spatial modeling versus station interpolation
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J. Blanchet
2010-12-01
Full Text Available For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965–1966 to 2007–2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.
Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew
2017-12-01
In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.
A random energy model for size dependence : recurrence vs. transience
Külske, Christof
1998-01-01
We investigate the size dependence of disordered spin models having an infinite number of Gibbs measures in the framework of a simplified 'random energy model for size dependence'. We introduce two versions (involving either independent random walks or branching processes), that can be seen as
Stability analysis for a general age-dependent vaccination model
International Nuclear Information System (INIS)
El Doma, M.
1995-05-01
An SIR epidemic model of a general age-dependent vaccination model is investigated when the fertility, mortality and removal rates depends on age. We give threshold criteria of the existence of equilibriums and perform stability analysis. Furthermore a critical vaccination coverage that is sufficient to eradicate the disease is determined. (author). 12 refs
Spatial Extent Models for Natural Language Phrases Involving Directional Containment
Singh, G.; de By, R.A.
2015-01-01
We study the problem of assigning a spatial extent to a text phrase such as central northern California', with the objective of allowing spatial interpretations of natural language, and consistency testing of complex utterances that involve multiple phrases from which spatial extent can be derived.
Spatial Segregation, Redistribution and Welfare: A Theoretical Model
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Tommaso Gabrieli
2016-03-01
Full Text Available This paper develops a theoretical model focusing on the effect that different neighborhood compositions can have on the formation of individual beliefs about economic opportunities. Specifically we highlight two effects that spatial segregation may have: (1 it can efficiently separate the individual effort choices of highly and low productive individuals, (2 it may imply that the median voter imposes a level of redistribution that is inefficient from the aggregate point of view. The trade-off implies that segregated and non-segregated cities may present very similar levels of aggregate welfare. We employ this framework to discuss how the structure of cities can play a role in the determination of US-type and Europe-type politico-economic equilibria and the implications for planning policies.
Unemployment estimation: Spatial point referenced methods and models
Pereira, Soraia
2017-06-26
Portuguese Labor force survey, from 4th quarter of 2014 onwards, started geo-referencing the sampling units, namely the dwellings in which the surveys are carried. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region. The labor force survey choose, according to an preestablished sampling criteria, a certain number of dwellings across the nation and survey the number of unemployed in these dwellings. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently, there has been increased interest in estimating these figures in smaller areas. Direct estimation methods, due to reduced sampling sizes in small areas, tend to produce fairly large sampling variations therefore model based methods, which tend to
The backbone of a City Information Model (CIM) : Implementing a spatial data model for urban design
Gil, J.A.; Almeida, J.; Duarte, J.P.
2011-01-01
We have been witnessing an increased interest in a more holistic approach to urban design practice and education. In this paper we present a spatial data model for urban design that proposes the combination of urban environment feature classes with design process feature classes. This data model is
The formulation and estimation of a spatial skew-normal generalized ordered-response model.
2016-06-01
This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...
Grazi, F.; van den Bergh, J.C.J.M.; Rietveld, P.
2007-01-01
A welfare framework for the analysis of the spatial dimensions of sustainability is developed. It covers agglomeration effects, interregional trade, negative environmental externalities, and various land use categories. The model is used to compare rankings of spatial configurations according to
SPATIAL MOTION OF THE MAGELLANIC CLOUDS: TIDAL MODELS RULED OUT?
International Nuclear Information System (INIS)
Ruzicka, Adam; Palous, Jan; Theis, Christian
2009-01-01
Recently, Kallivayalil et al. derived new values of the proper motion for the Large and Small Magellanic Clouds (LMC and SMC, respectively). The spatial velocities of both Clouds are unexpectedly higher than their previous values resulting from agreement between the available theoretical models of the Magellanic System and the observations of neutral hydrogen (H I) associated with the LMC and the SMC. Such proper motion estimates are likely to be at odds with the scenarios for creation of the large-scale structures in the Magellanic System suggested so far. We investigated this hypothesis for the pure tidal models, as they were the first ones devised to explain the evolution of the Magellanic System, and the tidal stripping is intrinsically involved in every model assuming the gravitational interaction. The parameter space for the Milky Way (MW)-LMC-SMC interaction was analyzed by a robust search algorithm (genetic algorithm) combined with a fast, restricted N-body model of the interaction. Our method extended the known variety of evolutionary scenarios satisfying the observed kinematics and morphology of the Magellanic large-scale structures. Nevertheless, assuming the tidal interaction, no satisfactory reproduction of the H I data available for the Magellanic Clouds was achieved with the new proper motions. We conclude that for the proper motion data by Kallivayalil et al., within their 1σ errors, the dynamical evolution of the Magellanic System with the currently accepted total mass of the MW cannot be explained in the framework of pure tidal models. The optimal value for the western component of the LMC proper motion was found to be μ W lmc ∼> -1.3 mas yr -1 in case of tidal models. It corresponds to the reduction of the Kallivayalil et al. value for μ W lmc by ∼ 40% in its magnitude.
The importance of distance to resources in the spatial modelling of bat foraging habitat.
Directory of Open Access Journals (Sweden)
Ana Rainho
Full Text Available Many bats are threatened by habitat loss, but opportunities to manage their habitats are now increasing. Success of management depends greatly on the capacity to determine where and how interventions should take place, so models predicting how animals use landscapes are important to plan them. Bats are quite distinctive in the way they use space for foraging because (i most are colonial central-place foragers and (ii exploit scattered and distant resources, although this increases flying costs. To evaluate how important distances to resources are in modelling foraging bat habitat suitability, we radio-tracked two cave-dwelling species of conservation concern (Rhinolophus mehelyi and Miniopterus schreibersii in a Mediterranean landscape. Habitat and distance variables were evaluated using logistic regression modelling. Distance variables greatly increased the performance of models, and distance to roost and to drinking water could alone explain 86 and 73% of the use of space by M. schreibersii and R. mehelyi, respectively. Land-cover and soil productivity also provided a significant contribution to the final models. Habitat suitability maps generated by models with and without distance variables differed substantially, confirming the shortcomings of maps generated without distance variables. Indeed, areas shown as highly suitable in maps generated without distance variables proved poorly suitable when distance variables were also considered. We concluded that distances to resources are determinant in the way bats forage across the landscape, and that using distance variables substantially improves the accuracy of suitability maps generated with spatially explicit models. Consequently, modelling with these variables is important to guide habitat management in bats and similarly mobile animals, particularly if they are central-place foragers or depend on spatially scarce resources.
The Importance of Distance to Resources in the Spatial Modelling of Bat Foraging Habitat
Rainho, Ana; Palmeirim, Jorge M.
2011-01-01
Many bats are threatened by habitat loss, but opportunities to manage their habitats are now increasing. Success of management depends greatly on the capacity to determine where and how interventions should take place, so models predicting how animals use landscapes are important to plan them. Bats are quite distinctive in the way they use space for foraging because (i) most are colonial central-place foragers and (ii) exploit scattered and distant resources, although this increases flying costs. To evaluate how important distances to resources are in modelling foraging bat habitat suitability, we radio-tracked two cave-dwelling species of conservation concern (Rhinolophus mehelyi and Miniopterus schreibersii) in a Mediterranean landscape. Habitat and distance variables were evaluated using logistic regression modelling. Distance variables greatly increased the performance of models, and distance to roost and to drinking water could alone explain 86 and 73% of the use of space by M. schreibersii and R. mehelyi, respectively. Land-cover and soil productivity also provided a significant contribution to the final models. Habitat suitability maps generated by models with and without distance variables differed substantially, confirming the shortcomings of maps generated without distance variables. Indeed, areas shown as highly suitable in maps generated without distance variables proved poorly suitable when distance variables were also considered. We concluded that distances to resources are determinant in the way bats forage across the landscape, and that using distance variables substantially improves the accuracy of suitability maps generated with spatially explicit models. Consequently, modelling with these variables is important to guide habitat management in bats and similarly mobile animals, particularly if they are central-place foragers or depend on spatially scarce resources. PMID:21547076
International Nuclear Information System (INIS)
Schuetter, K.
1985-01-01
An instrument for measuring a time-differential disturbed angular correlation was developed. Using this instrument the disturbance of the spatial correlation of the γ-quanta of the 171-245 keV γ-γ-cascade in 111 Cd was examined in dependence of the density of the gaseous 111 InI-systems and the time difference between the emission of the both γ-quanta. (BBOE)
Spatially Resolved Spectral Powder Analysis: Experiments and Modeling.
Scheibelhofer, Otto; Wahl, Patrick R; Larchevêque, Boris; Chauchard, Fabien; Khinast, Johannes G
2018-01-01
Understanding the behavior of light in granular media is necessary for determining the sample size, shape, and weight when probing using fiber optic setups. This is required for a correct estimate of the active pharmaceutical ingredient content in a pharmaceutical blend via near-infrared spectroscopy. Several strategies to describe the behavior of light in granular and turbid media exist. A common approach is the Monte-Carlo simulation of individual photons and their description using mean free path lengths for scattering and absorption. In this work, we chose a complementary method by approximating these parameters via real physical counterparts, i.e., the particle size, shape, and density and the resulting chord lengths. Additionally, the wavelength dependence of refractive indices is incorporated. The obtained results were compared with those obtained in an experimental setup that included the SAM-Spec Felin probe head by Indatech for detecting spatially resolved spectra of samples. Our method facilitates the interpretation of the acquired experimental results by contrasting the optical response, the physical particle attributes, and the simulation results.
A dynamic phase transition model for spatial agglomeration processes.
Weidlich, W; Haag, G
1987-11-01
A nonlinear model of population migration is presented in order to provide a dynamic explanation for the formation of metropolitan areas. "In Section 2 the model is introduced in terms of the rate equations for the mean values of the regional population numbers with specifically chosen individual transition rates. Section 3 gives a survey of concepts and results for the convenience of the reader not interested in the details of the mathematical derivations. Section 4 derives the stationary solutions of the rate equations, that is, the equilibria of the system. Section 5 treats the time dependent solutions of the model equations focussing on the exact analytic solutions along so-called symmetry paths. Section 6 analyzes the dynamic stability of the symmetry path solutions and decides which stationary states are unstable and which are stable equilibrium states." excerpt
Spatial and Temporal Self-Calibration of a Hydroeconomic Model
Howitt, R. E.; Hansen, K. M.
2008-12-01
across key nodes on the network and to annual carryover storage at ground and surface water storage facilities. To our knowledge, this is the first hydroeconomic model to perform spatial and temporal calibration simultaneously. The base for the LFN model is CALVIN, a hydroeconomic optimization model of the California water system developed at the University of California, Davis (Draper, et al. 2003). The LFN model, programmed in GAMS, is nonlinear, which permits incorporation of dynamic groundwater pumping costs that reflect head elevation. Hydropower production, also nonlinear in storage levels, could be added in the future. In this paper, we describe model implementation and performance over a sequence of water years drawn from the historical hydrologic record in California. Preliminary findings indicate that calibration occurs within acceptable limits and simulations replicate base case results well. Cai, X., and Wang, D. 2006. "Calibrating Holistic Water Resources-Economic Models." Journal of Water Resources Planning and Management November-December. Draper, A.J., M.W. Jenkins, K.W. Kirby, J.R. Lund, and R.E. Howitt. 2003. "Economic-Engineering Optimization for California Water Management." Journal of Water Resources Planning and Management 129(3):155-164. Howitt, R.E. 1995. "Positive Mathematical Programming." American Journal of Agricultural Economics 77:329-342. Howitt, R.E. 1998. "Self-Calibrating Network Flow Models." Working Paper, Department of Agricultural and Resource Economics, University of California, Davis. October 1998. class="ab'>
de Vaan, M.
2012-01-01
The high-tech industry in Silicon Valley, automobile production in Detroit, and financial services in New York and London are just a few examples of industries that are spatially concentrated. This phenomenon has attracted interest from a wide range of social scientists and regional and national
Directory of Open Access Journals (Sweden)
Thomas J Rodhouse
Full Text Available Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas] population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones" with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity--a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.
Accounting for spatial effects in land use regression for urban air pollution modeling.
Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G
2015-01-01
In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Assessments of habitat preferences and quality depend on spatial scale and metrics of fitness
Chalfoun, A.D.; Martin, T.E.
2007-01-01
1. Identifying the habitat features that influence habitat selection and enhance fitness is critical for effective management. Ecological theory predicts that habitat choices should be adaptive, such that fitness is enhanced in preferred habitats. However, studies often report mismatches between habitat preferences and fitness consequences across a wide variety of taxa based on a single spatial scale and/or a single fitness component. 2. We examined whether habitat preferences of a declining shrub steppe songbird, the Brewer's sparrow Spizella breweri, were adaptive when multiple reproductive fitness components and spatial scales (landscape, territory and nest patch) were considered. 3. We found that birds settled earlier and in higher densities, together suggesting preference, in landscapes with greater shrub cover and height. Yet nest success was not higher in these landscapes; nest success was primarily determined by nest predation rates. Thus landscape preferences did not match nest predation risk. Instead, nestling mass and the number of nesting attempts per pair increased in preferred landscapes, raising the possibility that landscapes were chosen on the basis of food availability rather than safe nest sites. 4. At smaller spatial scales (territory and nest patch), birds preferred different habitat features (i.e. density of potential nest shrubs) that reduced nest predation risk and allowed greater season-long reproductive success. 5. Synthesis and applications. Habitat preferences reflect the integration of multiple environmental factors across multiple spatial scales, and individuals may have more than one option for optimizing fitness via habitat selection strategies. Assessments of habitat quality for management prescriptions should ideally include analysis of diverse fitness consequences across multiple ecologically relevant spatial scales. ?? 2007 The Authors.
A spatial stochastic programming model for timber and core area management under risk of fires
Yu Wei; Michael Bevers; Dung Nguyen; Erin Belval
2014-01-01
Previous stochastic models in harvest scheduling seldom address explicit spatial management concerns under the influence of natural disturbances. We employ multistage stochastic programming models to explore the challenges and advantages of building spatial optimization models that account for the influences of random stand-replacing fires. Our exploratory test models...
Can plasticity make spatial structure irrelevant in individual-tree models?
Directory of Open Access Journals (Sweden)
Oscar García
2014-08-01
Full Text Available Background Distance-dependent individual-tree models have commonly been found to add little predictive power to that of distance-independent ones. One possible reason is plasticity, the ability of trees to lean and to alter crown and root development to better occupy available growing space. Being able to redeploy foliage (and roots into canopy gaps and less contested areas can diminish the importance of stem ground locations. Methods Plasticity was simulated for 3 intensively measured forest stands, to see to what extent and under what conditions the allocation of resources (e.g., light to the individual trees depended on their ground coordinates. The data came from 50 × 60 m stem-mapped plots in natural monospecific stands of jack pine, trembling aspen and black spruce from central Canada. Explicit perfect-plasticity equations were derived for tessellation-type models. Results Qualitatively similar simulation results were obtained under a variety of modelling assumptions. The effects of plasticity varied somewhat with stand uniformity and with assumed plasticity limits and other factors. Stand-level implications for canopy depth, distribution modelling and total productivity were examined. Conclusions Generally, under what seem like conservative maximum plasticity constraints, spatial structure accounted for less than 10% of the variance in resource allocation. The perfect-plasticity equations approximated well the simulation results from tessellation models, but not those from models with less extreme competition asymmetry. Whole-stand perfect plasticity approximations seem an attractive alternative to individual-tree models.
Using multilevel spatial models to understand salamander site occupancy patterns after wildfire
Chelgren, Nathan; Adams, Michael J.; Bailey, Larissa L.; Bury, R. Bruce
2011-01-01
Studies of the distribution of elusive forest wildlife have suffered from the confounding of true presence with the uncertainty of detection. Occupancy modeling, which incorporates probabilities of species detection conditional on presence, is an emerging approach for reducing observation bias. However, the current likelihood modeling framework is restrictive for handling unexplained sources of variation in the response that may occur when there are dependence structures such as smaller sampling units that are nested within larger sampling units. We used multilevel Bayesian occupancy modeling to handle dependence structures and to partition sources of variation in occupancy of sites by terrestrial salamanders (family Plethodontidae) within and surrounding an earlier wildfire in western Oregon, USA. Comparison of model fit favored a spatial N-mixture model that accounted for variation in salamander abundance over models that were based on binary detection/non-detection data. Though catch per unit effort was higher in burned areas than unburned, there was strong support that this pattern was due to a higher probability of capture for individuals in burned plots. Within the burn, the odds of capturing an individual given it was present were 2.06 times the odds outside the burn, reflecting reduced complexity of ground cover in the burn. There was weak support that true occupancy was lower within the burned area. While the odds of occupancy in the burn were 0.49 times the odds outside the burn among the five species, the magnitude of variation attributed to the burn was small in comparison to variation attributed to other landscape variables and to unexplained, spatially autocorrelated random variation. While ordinary occupancy models may separate the biological pattern of interest from variation in detection probability when all sources of variation are known, the addition of random effects structures for unexplained sources of variation in occupancy and detection
Directory of Open Access Journals (Sweden)
David Rehkopf
2015-01-01
Full Text Available Transmission of the agent of tuberculosis, Mycobacterium tuberculosis, is dependent on social context. A discrete spatial model representing neighborhoods segregated by levels of crowding and immunocompetence is constructed and used to evaluate prevention strategies, based on a number of assumptions about the spatial dynamics of tuberculosis. A cellular automata model is used to (a construct neighborhoods of different densities, (b model stochastically local interactions among individuals, and (c model the spread of tuberculosis within and across neighborhoods over time. Since infected people may become progressively sick but also heal through treatment, the transition among stages was modeled with transition probabilities. A moderate level of successful treatment (40% dramatically reduced the number of infections across all neighborhoods. Increasing the treatment in neighborhoods of a lower socioeconomic level from 40% to 90% results in an additional decrease of approximately 25% in the number of infected individuals overall. In conclusion, we find that a combination of a moderate level of successful treatment across all areas with more focused treatment efforts in lower socioeconomic areas resulted in the least number of infections over time.
Model dependence of isospin sensitive observables at high densities
International Nuclear Information System (INIS)
Guo, Wen-Mei; Yong, Gao-Chan; Wang, Yongjia; Li, Qingfeng; Zhang, Hongfei; Zuo, Wei
2013-01-01
Within two different frameworks of isospin-dependent transport model, i.e., Boltzmann–Uehling–Uhlenbeck (IBUU04) and Ultrarelativistic Quantum Molecular Dynamics (UrQMD) transport models, sensitive probes of nuclear symmetry energy are simulated and compared. It is shown that neutron to proton ratio of free nucleons, π − /π + ratio as well as isospin-sensitive transverse and elliptic flows given by the two transport models with their “best settings”, all have obvious differences. Discrepancy of numerical value of isospin-sensitive n/p ratio of free nucleon from the two models mainly originates from different symmetry potentials used and discrepancies of numerical value of charged π − /π + ratio and isospin-sensitive flows mainly originate from different isospin-dependent nucleon–nucleon cross sections. These demonstrations call for more detailed studies on the model inputs (i.e., the density- and momentum-dependent symmetry potential and the isospin-dependent nucleon–nucleon cross section in medium) of isospin-dependent transport model used. The studies of model dependence of isospin sensitive observables can help nuclear physicists to pin down the density dependence of nuclear symmetry energy through comparison between experiments and theoretical simulations scientifically
Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot
Directory of Open Access Journals (Sweden)
Peng Ge
2013-01-01
Full Text Available Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and the ω path of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.
Working models for spatial distribution and level of Mars' seismicity
Knapmeyer, M.; Oberst, J.; Hauber, E.; Wählisch, M.; Deuchler, C.; Wagner, R.
2006-11-01
We present synthetic catalogs of Mars quakes, intended to be used for performance assessments of future seismic networks on the planet. We have compiled a new inventory of compressional and extensional tectonic faults for the planet Mars, comprising 8500 faults with a total length of 680,000 km. The faults were mapped on the basis of Mars Orbiting Laser Altimeter (MOLA) shaded relief. Hence we expect to have assembled a homogeneous data set, not biased by illumination and viewing conditions of image data. Updated models of Martian crater statistics and geological maps were used to assign new maximum ages to all faults. On the basis of the fault catalog, spatial distributions of seismicity were simulated, using assumptions on the available annual seismic moment budget, the moment-frequency relationship, and a relation between rupture length and released moment. We have constructed five different models of Martian seismicity, predicting an annual moment release between 3.42 × 1016 Nm and 4.78 × 1018 Nm and up to 572 events with magnitudes greater than 4 per year as upper limit end-member case. Most events are expected on the Tharsis shield, but minor seismic centers are expected south of Hellas and north of Utopia Planitia.
Arima, E Y
2016-01-01
Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.
Directory of Open Access Journals (Sweden)
E Y Arima
Full Text Available Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200-300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads.
Arima, E. Y.
2016-01-01
Tropical forests are now at the center stage of climate mitigation policies worldwide given their roles as sources of carbon emissions resulting from deforestation and forest degradation. Although the international community has created mechanisms such as REDD+ to reduce those emissions, developing tropical countries continue to invest in infrastructure development in an effort to spur economic growth. Construction of roads in particular is known to be an important driver of deforestation. This article simulates the impact of road construction on deforestation in Western Amazonia, Peru, and quantifies the amount of carbon emissions associated with projected deforestation. To accomplish this objective, the article adopts a Bayesian probit land change model in which spatial dependencies are defined between regions or groups of pixels instead of between individual pixels, thereby reducing computational requirements. It also compares and contrasts the patterns of deforestation predicted by both spatial and non-spatial probit models. The spatial model replicates complex patterns of deforestation whereas the non-spatial model fails to do so. In terms of policy, both models suggest that road construction will increase deforestation by a modest amount, between 200–300 km2. This translates into aboveground carbon emissions of 1.36 and 1.85 x 106 tons. However, recent introduction of palm oil in the region serves as a cautionary example that the models may be underestimating the impact of roads. PMID:27010739
A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model
Directory of Open Access Journals (Sweden)
S. Kolberg
2006-01-01
Full Text Available A method for assimilating remotely sensed snow covered area (SCA into the snow subroutine of a grid distributed precipitation-runoff model (PRM is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC, which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E, based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started
Incorporating Pass-Phrase Dependent Background Models for Text-Dependent Speaker verification
DEFF Research Database (Denmark)
Sarkar, Achintya Kumar; Tan, Zheng-Hua
2018-01-01
is compared to conventional text-independent background model based TD-SV systems using either Gaussian mixture model (GMM)-universal background model (UBM) or Hidden Markov model (HMM)-UBM or i-vector paradigms. In addition, we consider two approaches to build PBMs: speaker-independent and speaker......In this paper, we propose pass-phrase dependent background models (PBMs) for text-dependent (TD) speaker verification (SV) to integrate the pass-phrase identification process into the conventional TD-SV system, where a PBM is derived from a text-independent background model through adaptation using...... and the selected PBM is then used for the log likelihood ratio (LLR) calculation with respect to the claimant model. The proposed method incorporates the pass-phrase identification step in the LLR calculation, which is not considered in conventional standalone TD-SV systems. The performance of the proposed method...
Continuum modeling of rate-dependent granular flows in SPH
Hurley, Ryan C.; Andrade, José E.
2017-01-01
We discuss a constitutive law for modeling rate-dependent granular flows that has been implemented in smoothed particle hydrodynamics (SPH). We model granular materials using a viscoplastic constitutive law that produces a Drucker-Prager-like yield condition in the limit of vanishing flow. A friction law for non-steady flows, incorporating rate-dependence and dilation, is derived and implemented within the constitutive law. We compare our SPH simulations with experimental data, demonstrating that they can capture both steady and non-steady dynamic flow behavior, notably including transient column collapse profiles. This technique may therefore be attractive for modeling the time-dependent evolution of natural and industrial flows.
Kondo, Hiroko; Kurahashi, Minori; Mori, Daisuke; Iinuma, Mitsuo; Tamura, Yasuo; Mizutani, Kenmei; Shimpo, Kan; Sonoda, Shigeru; Azuma, Kagaku; Kubo, Kin-ya
2016-01-01
Teeth are crucial, not only for mastication, but for overall nutrition and general health, including cognitive function. Aged mice with chronic stress due to tooth loss exhibit impaired hippocampus-dependent learning and memory. Exposure to an enriched environment restores the reduced hippocampal function. Here, we explored the effects of an enriched environment on learning deficits and hippocampal morphologic changes in aged senescence-accelerated mouse strain P8 (SAMP8) mice with tooth loss. Eight-month-old male aged SAMP8 mice with molar intact or with molars removed were housed in either a standard environment or enriched environment for 3 weeks. The Morris water maze was performed for spatial memory test. The newborn cell proliferation, survival, and differentiation in the hippocampus were analyzed using 5-Bromodeoxyuridine (BrdU) immunohistochemical method. The hippocampal brain-derived neurotrophic factor (BDNF) levels were also measured. Mice with upper molars removed (molarless) exhibited a significant decline in the proliferation and survival of newborn cells in the dentate gyrus (DG) as well as in hippocampal BDNF levels. In addition, neuronal differentiation of newly generated cells was suppressed and hippocampus-dependent spatial memory was impaired. Exposure of molarless mice to an enriched environment attenuated the reductions in the hippocampal BDNF levels and neuronal differentiation, and partially improved the proliferation and survival of newborn cells, as well as the spatial memory ability. These findings indicated that an enriched environment could ameliorate the hippocampus-dependent spatial memory impairment induced by molar tooth loss. Copyright © 2015 Elsevier Ltd. All rights reserved.
Maerker, Michael; Bolus, Michael
2014-05-01
We present a unique spatial dataset of Neanderthal sites in Europe that was used to train a set of stochastic models to reveal the correlations between the site locations and environmental indices. In order to assess the relations between the Neanderthal sites and environmental variables as described above we applied a boosted regression tree approach (TREENET) a statistical mechanics approach (MAXENT) and support vector machines. The stochastic models employ a learning algorithm to identify a model that best fits the relationship between the attribute set (predictor variables (environmental variables) and the classified response variable which is in this case the types of Neanderthal sites. A quantitative evaluation of model performance was done by determining the suitability of the model for the geo-archaeological applications and by helping to identify those aspects of the methodology that need improvements. The models' predictive performances were assessed by constructing the Receiver Operating Characteristics (ROC) curves for each Neanderthal class, both for training and test data. In a ROC curve the Sensitivity is plotted over the False Positive Rate (1-Specificity) for all possible cut-off points. The quality of a ROC curve is quantified by the measure of the parameter area under the ROC curve. The dependent variable or target variable in this study are the locations of Neanderthal sites described by latitude and longitude. The information on the site location was collected from literature and own research. All sites were checked for site accuracy using high resolution maps and google earth. The study illustrates that the models show a distinct ranking in model performance with TREENET outperforming the other approaches. Moreover Pre-Neanderthals, Early Neanderthals and Classic Neanderthals show a specific spatial distribution. However, all models show a wide correspondence in the selection of the most important predictor variables generally showing less
Roy, Christian
2015-01-01
The wetlands in the Prairie Pothole Region and in the Great Plains are notorious for their sensitivity to weather variability. These wetlands have been the focus of considerable attention because of their ecological importance and because of the expected impact of climate change. Few models in the literature, however, take into account spatial variation in the importance of wetland drivers. This is surprising given the importance spatial heterogeneity in geomorphology and climatic conditions have in the region. In this paper, I use spatially-varying coefficients to assess the variation in ecological drivers in a number of ponds observed over a 50-year period (1961-2012). I included the number of ponds observed the year before on a log scale, the log of total precipitation, and mean maximum temperature during the four previous seasons as explanatory variables. I also included a temporal component to capture change in the number of ponds due to anthropogenic disturbance. Overall, fall and spring precipitation were most important in pond abundance in the west, whereas winter and summer precipitation were the most important drivers in the east. The ponds in the east of the survey area were also more dependent on pond abundance during the previous year than those in the west. Spring temperature during the previous season influenced pond abundance; while the temperature during the other seasons had a limited effect. The ponds in the southwestern part of the survey area have been increasing independently of climatic conditions, whereas the ponds in the northeast have been steadily declining. My results underline the importance of accounting the spatial heterogeneity in environmental drivers, when working at large spatial scales. In light of my results, I also argue that assessing the impacts of climate change on wetland abundance in the spring, without more accurate climatic forecasting, will be difficult.
Directory of Open Access Journals (Sweden)
Christian Roy
Full Text Available The wetlands in the Prairie Pothole Region and in the Great Plains are notorious for their sensitivity to weather variability. These wetlands have been the focus of considerable attention because of their ecological importance and because of the expected impact of climate change. Few models in the literature, however, take into account spatial variation in the importance of wetland drivers. This is surprising given the importance spatial heterogeneity in geomorphology and climatic conditions have in the region. In this paper, I use spatially-varying coefficients to assess the variation in ecological drivers in a number of ponds observed over a 50-year period (1961-2012. I included the number of ponds observed the year before on a log scale, the log of total precipitation, and mean maximum temperature during the four previous seasons as explanatory variables. I also included a temporal component to capture change in the number of ponds due to anthropogenic disturbance. Overall, fall and spring precipitation were most important in pond abundance in the west, whereas winter and summer precipitation were the most important drivers in the east. The ponds in the east of the survey area were also more dependent on pond abundance during the previous year than those in the west. Spring temperature during the previous season influenced pond abundance; while the temperature during the other seasons had a limited effect. The ponds in the southwestern part of the survey area have been increasing independently of climatic conditions, whereas the ponds in the northeast have been steadily declining. My results underline the importance of accounting the spatial heterogeneity in environmental drivers, when working at large spatial scales. In light of my results, I also argue that assessing the impacts of climate change on wetland abundance in the spring, without more accurate climatic forecasting, will be difficult.
Spatial dependence of the super-exchange interactions for transition-metal trimers in graphene
Crook, Charles B.; Houchins, Gregory; Zhu, Jian-Xin; Balatsky, Alexander V.; Constantin, Costel; Haraldsen, Jason T.
2018-01-01
This study examines the magnetic interactions between spatially variable manganese and chromium trimers substituted into a graphene superlattice. Using density functional theory, we calculate the electronic band structure and magnetic populations for the determination of the electronic and magnetic properties of the system. To explore the super-exchange coupling between the transition-metal atoms, we establish the magnetic ground states through a comparison of multiple magnetic and spatial configurations. Through an analysis of the electronic and magnetic properties, we conclude that the presence of transition-metal atoms can induce a distinct magnetic moment in the surrounding carbon atoms as well as produce a Ruderman-Kittel-Kasuya-Yosida-like super-exchange coupling. It is hoped that these simulations can lead to the realization of spintronic applications in graphene through electronic control of the magnetic clusters.
Llorens, Francesc; Sanabria, Daniel; Huertas, Florentino
2015-01-01
We investigated the effect of a previous bout of intense exercise on exogenous spatial attention. In Experiment 1, a group of participants performed an exogenous spatial task at rest (without prior effort), immediately after intense exercise, and after recovering from an intense exercise. The analyses revealed that the typical "facilitation effect" (i.e., faster reaction times on cued than on uncued trials) immediately after exercise was positively correlated with participants' fitness level. In Experiment 2, a high-fit and a low-fit group performed the same task at rest (without prior effort) and immediately after an intense exercise. Results revealed that, after the bout of exercise, only low-fit participants showed reduced attentional effects compared to the rest condition. We argue that the normal functioning of exogenous attention was influenced by intense effort, affecting low-fit participants to a larger extent than to high-fit participants. As a consequence, target processing was prioritized over irrelevant stimuli.
Parameter dependence and outcome dependence in dynamical models for state vector reduction
International Nuclear Information System (INIS)
Ghirardi, G.C.; Grassi, R.; Butterfield, J.; Fleming, G.N.
1993-01-01
The authors apply the distinction between parameter independence and outcome independence to the linear and nonlinear models of a recent nonrelativistic theory of continuous state vector reduction. It is shown that in the nonlinear model there is a set of realizations of the stochastic process that drives the state vector reduction for which parameter independence is violated for parallel spin components in the EPR-Bohm setup. Such a set has an appreciable probability of occurrence (∼ 1/2). On the other hand, the linear model exhibits only extremely small parameter dependence effects. Some specific features of the models are investigated and it is recalled that, as has been pointed out recently, to be able to speak of definite outcomes (or equivalently of possessed objective elements of reality) at finite times, the criteria for their attribution to physical systems must be slightly changed. The concluding section is devoted to a detailed discussion of the difficulties met when attempting to take, as a starting point for the formulation of a relativistic theory, a nonrelativistic scheme which exhibits parameter dependence. Here the authors derive a theorem which identifies the precise sense in which the occurrence of parameter dependence forbids a genuinely relativistic generalization. Finally, the authors show how the appreciable parameter dependence of the nonlinear model gives rise to problems with relativity, while the extremely weak parameter dependence of the linear model does not give rise to any difficulty, provided the appropriate criteria for the attribution of definite outcomes are taken into account. 19 refs
Wismadi, Arif; Zuidgeest, Mark; Brussel, Mark; van Maarseveen, Martin
2014-01-01
To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen's Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran's I interpretation for spatial equity, we evaluate the distribution output regarding, first, `the spatial distribution patterns of priority targeting for allocation' (SPT) and, second, `the effect of new distribution patterns after location-allocation' (ELA). The Moran's I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.
Takizawa, Kuniharu; Kikuchi, Hiroshi; Fujikake, Hideo; Kodama, Kenichi; Kishi, Kiyoshi
1994-03-01
A spatial light modulator consisting of a polymer-dispersed liquid crystal (PDLC) film, a dielectric mirror, and a Bi12SiO20 photoconductor is useful for projection-type displays, optical image processing, and optical computing. However, a portion of the reading light scattered by the PDLC film passes through the dielectric mirror and illuminates the photoconductor, thus causing deterioration of display-image quality. This article reports on the results of a detailed study on the influence of reading light on the resolution and amplification factor, which is the ratio of reading light intensity to the maximum intensity of writing light. Angular distributions of light scattered by a PDLC cell were measured and the results were used to calculate the intensity of scattered light absorbed by the photoconductor. We then analyzed the optical input/output characteristics of the spatial light modulator with regard to the optical feedback effect caused by the reading light in order to discover the parameter for evaluating image quality. The relation between amplification and resolution is derived from this parameter. We have also considered a light absorption layer for preventing the deterioration of image quality and obtained the relation between the amplification factor and the transmittance of the light absorption layer for high definition images of high brightness. Finally, these theoretical results were confirmed by an experiment using a spatial light modulator with no dielectric mirror.
Scaling-up spatially-explicit ecological models using graphics processors
Koppel, Johan van de; Gupta, Rohit; Vuik, Cornelis
2011-01-01
How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to
Spatial object modeling in fuzzy topological spaces: with applications to land cover change
Tang, Xinming; Tang, Xinming
2004-01-01
The central topic of this thesis focuses on the accommodation of fuzzy spatial objects in a GIS. Several issues are discussed theoretically and practically, including the definition of fuzzy spatial objects, the topological relations between them, the modeling of fuzzy spatial objects, the
Fitzgibbon, W E; Morgan, J J; Webb, G F
2017-03-27
A deterministic model is developed for the spatial spread of an epidemic disease in a geographical setting. The disease is borne by vectors to susceptible hosts through criss-cross dynamics. The model is focused on an outbreak that arises from a small number of infected hosts imported into a subregion of the geographical setting. The goal is to understand how spatial heterogeneity of the vector and host populations influences the dynamics of the outbreak, in both the geographical spread and the final size of the epidemic. Partial differential equations are formulated to describe the spatial interaction of the hosts and vectors. The partial differential equations have reaction-diffusion terms to describe the criss-cross interactions of hosts and vectors. The partial differential equations of the model are analyzed and proven to be well-posed. A local basic reproduction number for the epidemic is analyzed. The epidemic outcomes of the model are correlated to the spatially dependent parameters and initial conditions of the model. The partial differential equations of the model are adapted to seasonality of the vector population, and applied to the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality in Brazil. The results for the model simulations of the 2015-2016 Zika seasonal outbreak in Rio de Janeiro Municipality indicate that the spatial distribution and final size of the epidemic at the end of the season are strongly dependent on the location and magnitude of local outbreaks at the beginning of the season. The application of the model to the Rio de Janeiro Municipality Zika 2015-2016 outbreak is limited by incompleteness of the epidemic data and by uncertainties in the parametric assumptions of the model.
Knodel, Markus
2017-10-02
Mathematical models of virus dynamics have not previously acknowledged spatial resolution at the intracellular level despite substantial arguments that favor the consideration of intracellular spatial dependence. The replication of the hepatitis C virus (HCV) viral RNA (vRNA) occurs within special replication complexes formed from membranes derived from endoplasmatic reticulum (ER). These regions, termed membranous webs, are generated primarily through specific interactions between nonstructural virus-encoded proteins (NSPs) and host cellular factors. The NSPs are responsible for the replication of the vRNA and their movement is restricted to the ER surface. Therefore, in this study we developed fully spatio-temporal resolved models of the vRNA replication cycle of HCV. Our simulations are performed upon realistic reconstructed cell structures-namely the ER surface and the membranous webs-based on data derived from immunostained cells replicating HCV vRNA. We visualized 3D simulations that reproduced dynamics resulting from interplay of the different components of our models (vRNA, NSPs, and a host factor), and we present an evaluation of the concentrations for the components within different regions of the cell. Thus far, our model is restricted to an internal portion of a hepatocyte and is qualitative more than quantitative. For a quantitative adaption to complete cells, various additional parameters will have to be determined through further in vitro cell biology experiments, which can be stimulated by the results deccribed in the present study.
Including spatial data in nutrient balance modelling on dairy farms
van Leeuwen, Maricke; van Middelaar, Corina; Stoof, Cathelijne; Oenema, Jouke; Stoorvogel, Jetse; de Boer, Imke
2017-04-01
The Annual Nutrient Cycle Assessment (ANCA) calculates the nitrogen (N) and phosphorus (P) balance at a dairy farm, while taking into account the subsequent nutrient cycles of the herd, manure, soil and crop components. Since January 2016, Dutch dairy farmers are required to use ANCA in order to increase understanding of nutrient flows and to minimize nutrient losses to the environment. A nutrient balance calculates the difference between nutrient inputs and outputs. Nutrients enter the farm via purchased feed, fertilizers, deposition and fixation by legumes (nitrogen), and leave the farm via milk, livestock, manure, and roughages. A positive balance indicates to which extent N and/or P are lost to the environment via gaseous emissions (N), leaching, run-off and accumulation in soil. A negative balance indicates that N and/or P are depleted from soil. ANCA was designed to calculate average nutrient flows on farm level (for the herd, manure, soil and crop components). ANCA was not designed to perform calculations of nutrient flows at the field level, as it uses averaged nutrient inputs and outputs across all fields, and it does not include field specific soil characteristics. Land management decisions, however, such as the level of N and P application, are typically taken at the field level given the specific crop and soil characteristics. Therefore the information that ANCA provides is likely not sufficient to support farmers' decisions on land management to minimize nutrient losses to the environment. This is particularly a problem when land management and soils vary between fields. For an accurate estimate of nutrient flows in a given farming system that can be used to optimize land management, the spatial scale of nutrient inputs and outputs (and thus the effect of land management and soil variation) could be essential. Our aim was to determine the effect of the spatial scale of nutrient inputs and outputs on modelled nutrient flows and nutrient use efficiencies
Characterizing QALYs under a General Rank Dependent Utility Model
H. Bleichrodt (Han); J. Quiggin (John)
1997-01-01
textabstractThis paper provides a characterization of QALYs, the most important outcome measure in medical decision making, in the context of a general rank dependent utility model. We show that both for chronic and for nonchronic health states the characterization of QALYs depends on intuitive
Jin, Jun-Jang; Ko, Il-Gyu; Kim, Sung-Eun; Hwang, Lakkyong; Lee, Man-Gyoon; Kim, Dae-Young; Jung, Sun-Young
2017-08-01
The effect of exercise, which increases hippocampal neurogenesis and improves memory function, is well documented, however, differences in the effect of exercise on young children and adults are not yet known. In the present study, age-dependent differences of treadmill exercise on spatial learning ability between young- and adult-age rats were investigated. The rats in the exercise groups were forced to run on a motorized treadmill for 30 min once a day for 6 weeks. Radial 8-arm maze test was conducted for the determination of spatial learning ability. Cell proliferation in the hippocampal dentate gyrus was determined by 5-bromo-2'-deoxyuridine immunohistochemistry. Western blot for brain-derived neurotrophic factor (BDNF) and tyrosine kinase B (TrkB) was performed. In the present study, the number of errors in the young-age rats was effectively decreased by treadmill exercise. Hippocampal neurogenesis was more active in the young-age rats than in the adult-age rats. BDNF and TrkB expression in the hippocampus was greater in the adult-age rats than in the young-age rats. The results of this study showed that adults have excellent spatial learning abilities than children, but the improvement of exercise-induced spatial learning ability through neurogenesis is better in children.
Gastambide, Francois; Taylor, Amy M; Palmer, Clare; Svard, Heta; Karjalainen, Maija; Janhunen, Sanna K; Tricklebank, Mark; Bannerman, David M
2015-11-01
Adult rats exposed to methylazoxymethanol acetate (MAM) at embryonic day 17 (E17) display robust pathological alterations in the hippocampus. However, discrepancies exist in the literature regarding the behavioural effects of this pre-natal manipulation. Therefore, a systematic assessment of MAM E17-induced behavioural alterations was conducted using a battery of dorsal and ventral hippocampus-dependent tests. Compared to saline controls, MAM E17-treated rats displayed deficits in spatial reference memory in both the aversive hidden platform watermaze task and an appetitive Y-maze task. Deficits in the spatial reference memory watermaze task were replicated across three different cohorts and two laboratories. In contrast, there was little, or no, effect on the non-spatial, visible platform watermaze task or an appetitive, non-spatial, visual discrimination task, respectively. MAM rats were also impaired in the spatial novelty preference task which assesses short-term memory, and displayed reduced anxiety levels in the elevated plus maze task. Thus, MAM E17 administration resulted in abnormal spatial information processing and reduced anxiety in a number of hippocampus-dependent behavioural tests, paralleling the effects of dorsal and ventral hippocampal lesions, respectively. These findings corroborate recent pathological and physiological studies, further highlighting the usefulness of MAM E17 as a model of hippocampal dysfunction in at least some aspects of schizophrenia.
MODELING SPATIAL TREE PATTERNS IN THE TAPAJÓS FOREST USING INTERFEROMETRIC HEIGHT
Directory of Open Access Journals (Sweden)
João R. dos Santos
2005-04-01
Full Text Available The spatial distribution of very large trees in primary Amazon forest is extracted from a digital model of interferometric forest height by an approach of local maximum filtering. The spatial point patterns of very large trees are modeled by a series of Markov point process models. Spatial distribution is regular, and interaction decreases with distance; very large trees are shown to exert repulsive interaction with their neighboring very large trees.
Bollmann, Steffen; Ghisleni, Carmen; Poil, Simon-Shlomo; Martin, Ernst; Ball, Juliane; Eich-Höchli, Dominique; Klaver, Peter; O'Gorman, Ruth L; Michels, Lars; Brandeis, Daniel
2017-06-01
Attention-deficit/hyperactivity disorder (ADHD) has been associated with spatial working memory as well as frontostriatal core deficits. However, it is still unclear how the link between these frontostriatal deficits and working memory function in ADHD differs in children and adults. This study examined spatial working memory in adults and children with ADHD, focussing on identifying regions demonstrating age-invariant or age-dependent abnormalities. We used functional magnetic resonance imaging to examine a group of 26 children and 35 adults to study load manipulated spatial working memory in patients and controls. In comparison to healthy controls, patients demonstrated reduced positive parietal and frontostriatal load effects, i.e., less increase in brain activity from low to high load, despite similar task performance. In addition, younger patients showed negative load effects, i.e., a decrease in brain activity from low to high load, in medial prefrontal regions. Load effect differences between ADHD and controls that differed between age groups were found predominantly in prefrontal regions. Age-invariant load effect differences occurred predominantly in frontostriatal regions. The age-dependent deviations support the role of prefrontal maturation and compensation in ADHD, while the age-invariant alterations observed in frontostriatal regions provide further evidence that these regions reflect a core pathophysiology in ADHD.
Stochastic hyperelastic modeling considering dependency of material parameters
Caylak, Ismail; Penner, Eduard; Dridger, Alex; Mahnken, Rolf
2018-03-01
This paper investigates the uncertainty of a hyperelastic model by treating random material parameters as stochastic variables. For its stochastic discretization a polynomial chaos expansion (PCE) is used. An important aspect in our work is the consideration of stochastic dependencies in the stochastic modeling of Ogden's material model. To this end, artificial experiments are generated using the auto-regressive moving average process based on real experiments. The parameter identification for all data provides statistics of Ogden's material parameters, which are subsequently used for stochastic modeling. Stochastic dependencies are incorporated into the PCE using a Nataf transformation from dependent distributed random variables to independent standard normal distributed ones. The representative numerical example shows that our proposed method adequately takes into account the stochastic dependencies of Ogden's material parameters.
Spatial Variation of Soil Type and Soil Moisture in the Regional Atmospheric Modeling System
Energy Technology Data Exchange (ETDEWEB)
Buckley, R.
2001-06-27
Soil characteristics (texture and moisture) are typically assumed to be initially constant when performing simulations with the Regional Atmospheric Modeling System (RAMS). Soil texture is spatially homogeneous and time-independent, while soil moisture is often spatially homogeneous initially, but time-dependent. This report discusses the conversion of a global data set of Food and Agriculture Organization (FAO) soil types to RAMS soil texture and the subsequent modifications required in RAMS to ingest this information. Spatial variations in initial soil moisture obtained from the National Center for Environmental Predictions (NCEP) large-scale models are also introduced. Comparisons involving simulations over the southeastern United States for two different time periods, one during warmer, more humid summer conditions, and one during cooler, dryer winter conditions, reveals differences in surface conditions related to increases or decreases in near-surface atmospheric moisture con tent as a result of different soil properties. Three separate simulation types were considered. The base case assumed spatially homogeneous soil texture and initial soil moisture. The second case assumed variable soil texture and constant initial soil moisture, while the third case allowed for both variable soil texture and initial soil moisture. The simulation domain was further divided into four geographically distinct regions. It is concluded there is a more dramatic impact on thermodynamic variables (surface temperature and dewpoint) than on surface winds, and a more pronounced variability in results during the summer period. While no obvious trends in surface winds or dewpoint temperature were found relative to observations covering all regions and times, improvement in surface temperatures in most regions and time periods was generally seen with the incorporation of variable soil texture and initial soil moisture.
Regional temperature models are needed for characterizing and mapping stream thermal regimes, establishing reference conditions, predicting future impacts and identifying critical thermal refugia. Spatial statistical models have been developed to improve regression modeling techn...
Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente
2013-01-01
In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…
Stisen, S.; Demirel, C.; Koch, J.
2017-12-01
Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing
Modeling Spatial and Temporal Fault Zone Evolution in Basement Rocks
Lunn, R. J.; Moir, H.; Shipton, Z. K.; Willson, J. P.
2007-05-01
There is considerable industrial interest in assessing the permeability of faults for the purpose of oil and gas production, deep well injection of waste liquids, underground storage of natural gas and disposal of radioactive waste. Deterministic prior estimation of fault hydraulic properties is highly error prone. Faults zones are formed through a complex interaction of mechanical, hydraulic and chemical processes and their permeability varies considerably over both space and time. Algorithms for predicting fault seal potential using throw and host rock property data exist for clay-rich fault seals but are contentious. In the case of crystalline rocks and sand-sand contacts, no such algorithms exist. In any case, the study of fault growth processes does not suggest that there is a clear or simple relationship between fault throw and the fault zone permeability. To improve estimates of fault zone permeability, it is important to understand the underlying hydro-mechanical processes of fault zone formation. In this research, we explore the spatial and temporal evolution of fault zones through development and application of a 2D hydro-mechanical finite element model. The temporal development of fault zone damage is simulated perpendicular to the main slip surface using Navier's equation for mechanical deformation. The model is applied to study development of fault zones in basement rocks. We simulate the evolution of fault zones from pre-existing joints and explore controls on the growth rate and locations of multiple splay fractures which link-up to form complex damage zones. We explore the temporal evolution of the stress field surrounding the fault tip for both propagation of isolated small faults and for fault linkage Results from these simulations have been validated using outcrop data.
Duncan, Earl W; White, Nicole M; Mengersen, Kerrie
2017-12-16
When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing. The purpose of this study is to review the main specifications of the spatial weights matrix found in the literature, and together with some new and less common specifications, compare the effect that they have on smoothing and model performance. The popular BYM model is described, and a simple solution for addressing the identifiability issue among the spatial random effects is provided. Seventeen different definitions of the spatial weights matrix are defined, which are classified into four classes: adjacency-based weights, and weights based on geographic distance, distance between covariate values, and a hybrid of geographic and covariate distances. These last two definitions embody the main novelty of this research. Three synthetic data sets are generated, each representing a different underlying spatial structure. These data sets together with a real spatial data set from the literature are analysed using the models. The models are evaluated using the deviance information criterion and Moran's I statistic. The deviance information criterion indicated that the model which uses binary, first-order adjacency weights to perform spatial smoothing is generally an optimal choice for achieving a good model fit. Distance-based weights also generally perform quite well and offer similar parameter interpretations. The less commonly explored options for performing spatial smoothing generally provided a worse model fit than models with more traditional approaches to smoothing, but usually outperformed the benchmark model which did not conduct spatial smoothing. The specification of the spatial weights matrix can have a colossal impact on model
Parameter Scaling for Epidemic Size in a Spatial Epidemic Model with Mobile Individuals.
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Chiyori T Urabe
Full Text Available In recent years, serious infectious diseases tend to transcend national borders and widely spread in a global scale. The incidence and prevalence of epidemics are highly influenced not only by pathogen-dependent disease characteristics such as the force of infection, the latent period, and the infectious period, but also by human mobility and contact patterns. However, the effect of heterogeneous mobility of individuals on epidemic outcomes is not fully understood. Here, we aim to elucidate how spatial mobility of individuals contributes to the final epidemic size in a spatial susceptible-exposed-infectious-recovered (SEIR model with mobile individuals in a square lattice. After illustrating the interplay between the mobility parameters and the other parameters on the spatial epidemic spreading, we propose an index as a function of system parameters, which largely governs the final epidemic size. The main contribution of this study is to show that the proposed index is useful for estimating how parameter scaling affects the final epidemic size. To demonstrate the effectiveness of the proposed index, we show that there is a positive correlation between the proposed index computed with the real data of human airline travels and the actual number of positive incident cases of influenza B in the entire world, implying that the growing incidence of influenza B is attributed to increased human mobility.
Pressel, Kyle G.; Collins, William D.; Desai, Ankur R.
2014-08-01
Recent studies have established that atmospheric water vapor fields exhibit spatial spectra that take the form of power laws and hence can be compactly characterized by scaling exponents. The power law scaling exponents have been shown to exhibit substantial vertical variability. In this work, Taylor's frozen turbulence hypothesis is used to infer the first-order spatial structure function and generalized detrended fluctuation function scaling exponents for scales between 1 km and 100 km. Both methods are used to estimate the Hurst exponent (H) using 10 Hz time series of water vapor measured at 396 m altitude from an Ameriflux tower in Wisconsin. Due to the diurnal cycle in the boundary layer height at the 396 m observational level, H may be estimated for both the daytime convective mixed layer and the nocturnal residual layer. Values of H≈13 are obtained for the convective mixed layer, while values of H>12 apply in the nocturnal residual layer. The results are shown to be remarkably consistent with a similar analysis from satellite-based observations as reported in Pressel and Collins (2012).
Modeling Spatial Data within Object Relational-Databases
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Iuliana BOTHA
2011-03-01
Full Text Available Spatial data can refer to elements that help place a certain object in a certain area. These elements are latitude, longitude, points, geometric figures represented by points, etc. However, when translating these elements into data that can be stored in a computer, it all comes down to numbers. The interesting part that requires attention is how to memorize them in order to obtain fast and various spatial queries. This part is where the DBMS (Data Base Management System that contains the database acts in. In this paper, we analyzed and compared two object-relational DBMS that work with spatial data: Oracle and PostgreSQL.
Multiphase modelling of vascular tumour growth in two spatial dimensions
Hubbard, M.E.
2013-01-01
In this paper we present a continuum mathematical model of vascular tumour growth which is based on a multiphase framework in which the tissue is decomposed into four distinct phases and the principles of conservation of mass and momentum are applied to the normal/healthy cells, tumour cells, blood vessels and extracellular material. The inclusion of a diffusible nutrient, supplied by the blood vessels, allows the vasculature to have a nonlocal influence on the other phases. Two-dimensional computational simulations are carried out on unstructured, triangular meshes to allow a natural treatment of irregular geometries, and the tumour boundary is captured as a diffuse interface on this mesh, thereby obviating the need to explicitly track the (potentially highly irregular and ill-defined) tumour boundary. A hybrid finite volume/finite element algorithm is used to discretise the continuum model: the application of a conservative, upwind, finite volume scheme to the hyperbolic mass balance equations and a finite element scheme with a stable element pair to the generalised Stokes equations derived from momentum balance, leads to a robust algorithm which does not use any form of artificial stabilisation. The use of a matrix-free Newton iteration with a finite element scheme for the nutrient reaction-diffusion equations allows full nonlinearity in the source terms of the mathematical model.Numerical simulations reveal that this four-phase model reproduces the characteristic pattern of tumour growth in which a necrotic core forms behind an expanding rim of well-vascularised proliferating tumour cells. The simulations consistently predict linear tumour growth rates. The dependence of both the speed with which the tumour grows and the irregularity of the invading tumour front on the model parameters is investigated. © 2012 Elsevier Ltd.
Energy Technology Data Exchange (ETDEWEB)
Franzkowiak, V.; Petry, H.; Ebel, A. [Cologne Univ. (Germany). Inst. for Geophysics and Meteorology
1997-12-31
The sensitivity of a mesoscale chemistry transport model to the temporal and spatial resolution of aircraft emission inventories is evaluated. A statistical analysis of air traffic in the North-Atlantic flight corridor is carried out showing a highly variable, fine structured spatial distribution and a pronounced daily variation. Sensitivity studies comparing different emission scenarios reveal a strong dependency to the emission time and location of both transport and response in chemical formation of subsequent products. The introduction of a pronounced daily variation leads to a 30% higher ozone production in comparison to uniformly distributed emissions. (author) 9 refs.
DEFF Research Database (Denmark)
Hinrichsen, H.H.; Schmidt, J.O.; Petereit, C.
2005-01-01
Temporal mismatch between the occurrence of larvae and their prey potentially affects the spatial overlap and thus the contact rates between predator and prey. This might have important consequences for growth and survival. We performed a case study investigating the influence of circulation......-prey overlap, dependent on the hatching time of cod larvae. By performing model runs for the years 1979-1998 investigated the intra- and interannual variability of potential spatial overlap between predator and prey. Assuming uniform prey distributions, we generally found the overlap to have decreased since...
Spatial aggregation for crop modelling at regional scales: the effects of soil variability
Coucheney, Elsa; Villa, Ana; Eckersten, Henrik; Hoffmann, Holger; Jansson, Per-Erik; Gaiser, Thomas; Ewert, Franck; Lewan, Elisabet
2017-04-01
Modelling agriculture production and adaptation to the environment at regional or global scale receives much interest in the context of climate change. Process-based soil-crop models describe the flows of mass (i.e. water, carbon and nitrogen) and energy in the soil-plant-atmosphere system. As such, they represent valuable tools for predicting agricultural production in diverse agro-environmental contexts as well as for assessing impacts on the environment; e.g. leaching of nitrates, changes in soil carbon content and GHGs emissions. However, their application at regional and global scales for climate change impact studies raises new challenges related to model input data, calibration and evaluation. One major concern is to take into account the spatial variability of the environmental conditions (e.g. climate, soils, management practices) used as model input and because the impacts of climate change on cropping systems depend strongly on the site conditions and properties (1). For example climate change effects on yield can be either negative or positive depending on the soil type (2). Additionally, the use of different methods of upscaling and downscaling adds new sources of modelling uncertainties (3). In the present study, the effect of aggregating soil input data by area majority of soil mapping units was explored for spatially gridded simulations with the soil-vegetation model CoupModel for a region in Germany (North Rhine-Westphalia, NRW). The data aggregation effect (DAE) was analysed for wheat yield, water drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE was higher for soil C and N variables than for yield and drainage and were strongly related to the spatial coverage of specific soils within the study region. These 'key soils' were identified by a model sensitivity analysis to soils present in the NRW region. The spatial aggregation of the key soils additionally influenced the DAE. Our results suggest that a spatial
Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci
2017-05-01
Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.
Tang, Shuaiqi; Zhang, Minghua; Xie, Shaocheng
2017-08-01
Large-scale forcing data, such as vertical velocity and advective tendencies, are required to drive single-column models (SCMs), cloud-resolving models, and large-eddy simulations. Previous studies suggest that some errors of these model simulations could be attributed to the lack of spatial variability in the specified domain-mean large-scale forcing. This study investigates the spatial variability of the forcing and explores its impact on SCM simulated precipitation and clouds. A gridded large-scale forcing data during the March 2000 Cloud Intensive Operational Period at the Atmospheric Radiation Measurement program's Southern Great Plains site is used for analysis and to drive the single-column version of the Community Atmospheric Model Version 5 (SCAM5). When the gridded forcing data show large spatial variability, such as during a frontal passage, SCAM5 with the domain-mean forcing is not able to capture the convective systems that are partly located in the domain or that only occupy part of the domain. This problem has been largely reduced by using the gridded forcing data, which allows running SCAM5 in each subcolumn and then averaging the results within the domain. This is because the subcolumns have a better chance to capture the timing of the frontal propagation and the small-scale systems. Other potential uses of the gridded forcing data, such as understanding and testing scale-aware parameterizations, are also discussed.
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models.
Finley, Andrew O; Banerjee, Sudipto; Carlin, Bradley P
2007-04-01
Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude-longitude, Easting-Northing etc.) in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models
Directory of Open Access Journals (Sweden)
Andrew O. Finley
2007-04-01
Full Text Available Scientists and investigators in such diverse fields as geological and environmental sciences, ecology, forestry, disease mapping, and economics often encounter spatially referenced data collected over a fixed set of locations with coordinates (latitude–longitude, Easting–Northing etc. in a region of study. Such point-referenced or geostatistical data are often best analyzed with Bayesian hierarchical models. Unfortunately, fitting such models involves computationally intensive Markov chain Monte Carlo (MCMC methods whose efficiency depends upon the specific problem at hand. This requires extensive coding on the part of the user and the situation is not helped by the lack of available software for such algorithms. Here, we introduce a statistical software package, spBayes, built upon the R statistical computing platform that implements a generalized template encompassing a wide variety of Gaussian spatial process models for univariate as well as multivariate point-referenced data. We discuss the algorithms behind our package and illustrate its use with a synthetic and real data example.
Anekawati, Anik; Widjanarko Otok, Bambang; Purhadi; Sutikno
2017-09-01
In some cases, education research often involves the latent variables that have a causal relationship as well as a spatial effect. Therefore, it requires a statistical analysis technique called spatial structural equation modelling (spatial SEM). In this research, a spatial SEM was developed to model the quality of education in high schools in Sumenep Regency. This model was improved after the evaluation of an outer and inner model of the model scheme centroid, factor and path since some indicators were not valid. The path scheme model showed better results compared to the other schemes since all of its indicators were valid and its value of R-square increased. Furthermore, only the model of path scheme was tested for spatial effects. The result of the identification test of spatial effects on the inner model using a robust Lagrange multiplier test (using queen contiguity) showed that the education quality model leads to a spatial autoregressive model (SAR in SEM) with a significance level α of 5%, while the model of school infrastructure has no significant spatial effects. The improved model of SAR in SEM, the R2 value obtained was 47.33%, so that it is clear that data variation can be explained by the model of SAR in SEM for the quality of education in high schools.
Semantic concept-enriched dependence model for medical information retrieval.
Choi, Sungbin; Choi, Jinwook; Yoo, Sooyoung; Kim, Heechun; Lee, Youngho
2014-02-01
In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. Standardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. We performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. Our semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept's explicit importance in the query. By capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance. Copyright © 2013 Elsevier Inc. All rights reserved.
MODEL OF SPATIAL EVALUATION FOR TOURISM ECO-RENT
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Maja Fredotović
2011-02-01
Full Text Available Tourism is extremely interacted with the environment. Taking into account that tourism uses the space and related resources, it seems right to pay for the damages caused to the environment. This is the basis of the tourist spatial eco rent. The paper evaluates the space and resources used by tourism as the basis for the introduction of the tourism eco-rent in the area of Makarska Riviera, a traditional tourism destination. It is divided into three main spatial units: urban areas, bathing zone (beaches, Biokovo Park of Nature. According to natural and geographical reasoning, a number of zones with different spatial values within each spatial unit has been identified. Each unit, i.e. zone was evaluated according to various criteria relevant to the evaluation of space for tourism and tourism development purposes. Having ranked zones within each unit, using the multiriteria ranking method PROMETHEE II, comparative analysis of the obtained results was carried out as well.
Progressive Spatial Processing Deficits in a Mouse Model of the Fragile X Premutation
Hunsaker, Michael R.; Wenzel, H. Jürgen; Willemsen, Rob; Berman, Robert F.
2012-01-01
Fragile X associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder that is the result of a CGG trinucleotide repeat expansion in the range of 55-200 in the 5’ UTR of the FMR1 gene. To better understand the progression of this disorder, a knock-in (CGG KI) mouse was developed by substituting the mouse CGG8 trinucleotide repeat with an expanded CGG98 repeat from human origin. It has been shown that this mouse shows deficits on the water maze at 52 weeks of age. In the present study, this CGG KI mouse model of FXTAS was tested on behavioral tasks that emphasize spatial information processing. The results demonstrate that at 12 and 24 weeks of age, CGG KI mice were unable to detect a change in the distance between two objects (metric task), but showed intact detection of a transposition of the objects (topological task). At 48 weeks of age, CGG KI mice were unable to detect either change in object location. These data indicate that hippocampal-dependent impairments in spatial processing may occur prior to parietal cortex-dependent impairments in FXTAS. PMID:20001115
Disaggregation, aggregation and spatial scaling in hydrological modelling
Becker, Alfred; Braun, Peter
1999-04-01
A typical feature of the land surface is its heterogeneity in terms of the spatial variability of land surface characteristics and parameters controlling physical/hydrological, biological, and other related processes. Different forms and degrees of heterogeneity need to be taken into account in hydrological modelling. The first part of the article concerns the conditions under which a disaggregation of the land surface into subareas of uniform or "quasihomogeneous" behaviour (hydrotopes or hydrological response units - HRUs) is indispensable. In a case study in northern Germany, it is shown that forests in contrast to arable land, areas with shallow groundwater in contrast to those with deep, water surfaces and sealed areas should generally be distinguished (disaggregated) in modelling, whereas internal heterogeneities within these hydrotopes can be assessed statistically, e.g., by areal distribution functions (soil water holding capacity, hydraulic conductivity, etc.). Models with hydrotope-specific parameters can be applied to calculate the "vertical" processes (fluxes, storages, etc.), and this, moreover, for hydrotopes of different area, and even for groups of distributed hydrotopes in a reference area (hydrotope classes), provided that the meteorological conditions are similar. Thus, a scaling problem does not really exist in this process domain. The primary domain for the application of scaling laws is that of lateral flows in landscapes and river basins. This is illustrated in the second part of the article, where results of a case study in Bavaria/Germany are presented and discussed. It is shown that scaling laws can be applied efficiently for the determination of the Instantaneous Unit Hydrograph (IUH) of the surface runoff system in river basins: simple scaling for basins larger than 43 km 2, and multiple scaling for smaller basins. Surprisingly, only two parameters were identified as important in the derived relations: the drainage area and, in some
A multistate dynamic site occupancy model for spatially aggregated sessile communities
Fukaya, Keiichi; Royle, J. Andrew; Okuda, Takehiro; Nakaoka, Masahiro; Noda, Takashi
2017-01-01
Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations.We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error.By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account.Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.
Collaborative spatial analysis and modelling in a research environment
CSIR Research Space (South Africa)
Naudé, A
2006-02-01
Full Text Available of an open-source geoportal and geospatial content management framework (adapted for low-bandwidth environments), customisable spatial analysis workbenches (providing guidance and tools for geoprocesses such as spatial disaggregation) and the formulation... resources and processes. In these two sections, the concept of a knowledge geoportal is introduced. A knowledge geoportal includes the notion of customisable workbenches, aimed at addressing the other key problems seen in Figure 1. Further...
Directory of Open Access Journals (Sweden)
Erfan Ayubi
2017-05-01
Full Text Available OBJECTIVES The aim of this study was to explore the spatial pattern of female breast cancer (BC incidence at the neighborhood level in Tehran, Iran. METHODS The present study included all registered incident cases of female BC from March 2008 to March 2011. The raw standardized incidence ratio (SIR of BC for each neighborhood was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by a Besag, York, and Mollie spatial model and the spatial empirical Bayesian method. The purely spatial scan statistic was used to identify spatial clusters. RESULTS There were 4,175 incident BC cases in the study area from 2008 to 2011, of which 3,080 were successfully geocoded to the neighborhood level. Higher than expected rates of BC were found in neighborhoods located in northern and central Tehran, whereas lower rates appeared in southern areas. The most likely cluster of higher than expected BC incidence involved neighborhoods in districts 3 and 6, with an observed-to-expected ratio of 3.92 (p<0.001, whereas the most likely cluster of lower than expected rates involved neighborhoods in districts 17, 18, and 19, with an observed-to-expected ratio of 0.05 (p<0.001. CONCLUSIONS Neighborhood-level inequality in the incidence of BC exists in Tehran. These findings can serve as a basis for resource allocation and preventive strategies in at-risk areas.
Advanced spatial metrics analysis in cellular automata land use and cover change modeling
International Nuclear Information System (INIS)
Zamyatin, Alexander; Cabral, Pedro
2011-01-01
This paper proposes an approach for a more effective definition of cellular automata transition rules for landscape change modeling using an advanced spatial metrics analysis. This approach considers a four-stage methodology based on: (i) the search for the appropriate spatial metrics with minimal correlations; (ii) the selection of the appropriate neighborhood size; (iii) the selection of the appropriate technique for spatial metrics application; and (iv) the analysis of the contribution level of each spatial metric for joint use. The case study uses an initial set of 7 spatial metrics of which 4 are selected for modeling. Results show a better model performance when compared to modeling without any spatial metrics or with the initial set of 7 metrics.
Directory of Open Access Journals (Sweden)
Emilia Modranka
2015-01-01
Full Text Available Financial support is the main instrument of regional development policy in the European Union. Concentration, programming and partnership, are presented as "core principles" for improving the effectiveness of structural expenditure based on compensating the structural disadvantage of the assisted regions. The purpose of this paper is to analyze the spatial dependencies in level of absorption of funds in comparison to allocation criteria of intervention funded from Regional Operational Programmes. The research was based on data about the state of implementation of European funds in the subregions (NUTS 3 in 2007-2012., generated from the National Information System SIMIK 07-13.
Spatial distribution of mineral dust single scattering albedo based on DREAM model
Kuzmanoski, Maja; Ničković, Slobodan; Ilić, Luka
2016-04-01
Mineral dust comprises a significant part of global aerosol burden. There is a large uncertainty in estimating role of dust in Earth's climate system, partly due to poor characterization of its optical properties. Single scattering albedo is one of key optical properties determining radiative effects of dust particles. While it depends on dust particle sizes, it is also strongly influenced by dust mineral composition, particularly the content of light-absorbing iron oxides and the mixing state (external or internal). However, an assumption of uniform dust composition is typically used in models. To better represent single scattering albedo in dust atmospheric models, required to increase accuracy of dust radiative effect estimates, it is necessary to include information on particle mineral content. In this study, we present the spatial distribution of dust single scattering albedo based on the Dust Regional Atmospheric Model (DREAM) with incorporated particle mineral composition. The domain of the model covers Northern Africa, Middle East and the European continent, with horizontal resolution set to 1/5°. It uses eight particle size bins within the 0.1-10 μm radius range. Focusing on dust episode of June 2010, we analyze dust single scattering albedo spatial distribution over the model domain, based on particle sizes and mineral composition from model output; we discuss changes in this optical property after long-range transport. Furthermore, we examine how the AERONET-derived aerosol properties respond to dust mineralogy. Finally we use AERONET data to evaluate model-based single scattering albedo. Acknowledgement We would like to thank the AERONET network and the principal investigators, as well as their staff, for establishing and maintaining the AERONET sites used in this work.
Sex-biased dispersal patterns depend on the spatial scale in a social rodent
Gauffre, B.; Petit, E.; Brodier, S.; Bretagnolle, V.; Cosson, J. F.
2009-01-01
Dispersal is a fundamental process in ecology because it influences the dynamics, genetic structure and persistence of populations. Furthermore, understanding the evolutionary causes of dispersal pattern, particularly when they differ between genders, is still a major question in evolutionary ecology. Using a panel of 10 microsatellite loci, we investigated at different spatial scales the genetic structure and the sex-specific dispersal patterns in the common vole Microtus arvalis, a small colonial mammal. This study was conducted in an intensive agricultural area of western France. Hierarchical FST analyses, relatedness and assignment tests suggested (i) that females are strongly kin-clustered within colonies; (ii) that dispersal is strongly male-biased at a local scale; and (iii) long-distance dispersal is not rare and more balanced between genders. We conclude that males migrate continuously from colony to colony to reproduce, whereas females may disperse just once and would be mainly involved in new colony foundation. PMID:19586945
Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.
Groth, Detlef
2017-04-01
Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later
A temperature dependent slip factor based thermal model for friction ...
Indian Academy of Sciences (India)
This paper proposes a new slip factor based three-dimensional thermal model to predict the temperature distribution during friction stir welding of 304L stainless steel plates. The proposed model employs temperature and radius dependent heat source to study the thermal cycle, temperature distribution, power required, the ...
A temperature dependent slip factor based thermal model for friction
Indian Academy of Sciences (India)
This paper proposes a new slip factor based three-dimensional thermal model to predict the temperature distribution during friction stir welding of 304L stainless steel plates. The proposed model employs temperature and radius dependent heat source to study the thermal cycle, temperature distribution, power required, the ...
Variance-based sensitivity indices for models with dependent inputs
International Nuclear Information System (INIS)
Mara, Thierry A.; Tarantola, Stefano
2012-01-01
Computational models are intensively used in engineering for risk analysis or prediction of future outcomes. Uncertainty and sensitivity analyses are of great help in these purposes. Although several methods exist to perform variance-based sensitivity analysis of model output with independent inputs only a few are proposed in the literature in the case of dependent inputs. This is explained by the fact that the theoretical framework for the independent case is set and a univocal set of variance-based sensitivity indices is defined. In the present work, we propose a set of variance-based sensitivity indices to perform sensitivity analysis of models with dependent inputs. These measures allow us to distinguish between the mutual dependent contribution and the independent contribution of an input to the model response variance. Their definition relies on a specific orthogonalisation of the inputs and ANOVA-representations of the model output. In the applications, we show the interest of the new sensitivity indices for model simplification setting. - Highlights: ► Uncertainty and sensitivity analyses are of great help in engineering. ► Several methods exist to perform variance-based sensitivity analysis of model output with independent inputs. ► We define a set of variance-based sensitivity indices for models with dependent inputs. ► Inputs mutual contributions are distinguished from their independent contributions. ► Analytical and computational tests are performed and discussed.
Modelling malaria treatment practices in Bangladesh using spatial statistics
Directory of Open Access Journals (Sweden)
Haque Ubydul
2012-03-01
Full Text Available Abstract Background Malaria treatment-seeking practices vary worldwide and Bangladesh is no exception. Individuals from 88 villages in Rajasthali were asked about their treatment-seeking practices. A portion of these households preferred malaria treatment from the National Control Programme, but still a large number of households continued to use drug vendors and approximately one fourth of the individuals surveyed relied exclusively on non-control programme treatments. The risks of low-control programme usage include incomplete malaria treatment, possible misuse of anti-malarial drugs, and an increased potential for drug resistance. Methods The spatial patterns of treatment-seeking practices were first examined using hot-spot analysis (Local Getis-Ord Gi statistic and then modelled using regression. Ordinary least squares (OLS regression identified key factors explaining more than 80% of the variation in control programme and vendor treatment preferences. Geographically weighted regression (GWR was then used to assess where each factor was a strong predictor of treatment-seeking preferences. Results Several factors including tribal affiliation, housing materials, household densities, education levels, and proximity to the regional urban centre, were found to be effective predictors of malaria treatment-seeking preferences. The predictive strength of each of these factors, however, varied across the study area. While education, for example, was a strong predictor in some villages, it was less important for predicting treatment-seeking outcomes in other villages. Conclusion Understanding where each factor is a strong predictor of treatment-seeking outcomes may help in planning targeted interventions aimed at increasing control programme usage. Suggested strategies include providing additional training for the Building Resources across Communities (BRAC health workers, implementing educational programmes, and addressing economic factors.
Model-driven dependability assessment of software systems
Bernardi, Simona; Petriu, Dorina C
2013-01-01
In this book, the authors present cutting-edge model-driven techniques for modeling and analysis of software dependability. Most of them are based on the use of UML as software specification language. From the software system specification point of view, such techniques exploit the standard extension mechanisms of UML (i.e., UML profiling). UML profiles enable software engineers to add non-functional properties to the software model, in addition to the functional ones. The authors detail the state of the art on UML profile proposals for dependability specification and rigorously describe the t
Energy based model for temperature dependent behavior of ferromagnetic materials
International Nuclear Information System (INIS)
Sah, Sanjay; Atulasimha, Jayasimha
2017-01-01
An energy based model for temperature dependent anhysteretic magnetization curves of ferromagnetic materials is proposed and benchmarked against experimental data. This is based on the calculation of macroscopic magnetic properties by performing an energy weighted average over all possible orientations of the magnetization vector. Most prior approaches that employ this method are unable to independently account for the effect of both inhomogeneity and temperature in performing the averaging necessary to model experimental data. Here we propose a way to account for both effects simultaneously and benchmark the model against experimental data from ~5 K to ~300 K for two different materials in both annealed (fewer inhomogeneities) and deformed (more inhomogeneities) samples. This demonstrates that this framework is well suited to simulate temperature dependent experimental magnetic behavior. - Highlights: • Energy based model for temperature dependent ferromagnetic behavior. • Simultaneously accounts for effect of temperature and inhomogeneities. • Benchmarked against experimental data from 5 K to 300 K.
A Dependent Hidden Markov Model of Credit Quality
Directory of Open Access Journals (Sweden)
Małgorzata Wiktoria Korolkiewicz
2012-01-01
Full Text Available We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.
White, Katherine J Curtis
2008-05-01
I investigate the relationship between county population change and farm dependence in the Great Plains region during the twentieth century, using spatial data analysis techniques. This research is rooted in a long-standing sociological and demographic interest in population responses to economic transitions and informs the theoretical understanding of urbanization processes. Using census and environmental data, the analysis challenges earlier assertions of a simple transition in the relationship between farm dependence and population change that accompanied modern technological advancements, namely tractors (the mechanization thesis). Rather than observing the proposed positive-to-negative shift, study results show a negative association throughout the pre- and post-mechanization periods. Partial support is found if the thesis is revised to consider the relationship between population change and the change in farm dependence rather than the level of farm dependence. Findings show mixed support for an alternative argument that nonfarm industries moderate the influence of farm dependence (the industry complex thesis). In contrast to earlier applications of the thesis, industrial relations in the Great Plains context are characterized by specialization rather than cooperation.
CURTIS WHITE, KATHERINE J.
2008-01-01
I investigate the relationship between county population change and farm dependence in the Great Plains region during the twentieth century, using spatial data analysis techniques. This research is rooted in a long-standing sociological and demographic interest in population responses to economic transitions and informs the theoretical understanding of urbanization processes. Using census and environmental data, the analysis challenges earlier assertions of a simple transition in the relationship between farm dependence and population change that accompanied modern technological advancements, namely tractors (the mechanization thesis). Rather than observing the proposed positive-to-negative shift, study results show a negative association throughout the pre- and post-mechanization periods. Partial support is found if the thesis is revised to consider the relationship between population change and the change in farm dependence rather than the level of farm dependence. Findings show mixed support for an alternative argument that nonfarm industries moderate the influence of farm dependence (the industry complex thesis). In contrast to earlier applications of the thesis, industrial relations in the Great Plains context are characterized by specialization rather than cooperation. PMID:18613486
Mendiguren, Gorka; Koch, Julian; Stisen, Simon
2017-11-01
Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds
Atlantic Bluefin Tuna: A Novel Multistock Spatial Model for Assessing Population Biomass
Taylor, Nathan G.; McAllister, Murdoch K.; Lawson, Gareth L.; Carruthers, Tom; Block, Barbara A.
2011-01-01
Atlantic bluefin tuna (Thunnus thynnus) is considered to be overfished, but the status of its populations has been debated, partly because of uncertainties regarding the effects of mixing on fishing grounds. A better understanding of spatial structure and mixing may help fisheries managers to successfully rebuild populations to sustainable levels while maximizing catches. We formulate a new seasonally and spatially explicit fisheries model that is fitted to conventional and electronic tag data, historic catch-at-age reconstructions, and otolith microchemistry stock-composition data to improve the capacity to assess past, current, and future population sizes of Atlantic bluefin tuna. We apply the model to estimate spatial and temporal mixing of the eastern (Mediterranean) and western (Gulf of Mexico) populations, and to reconstruct abundances from 1950 to 2008. We show that western and eastern populations have been reduced to 17% and 33%, respectively, of 1950 spawning stock biomass levels. Overfishing to below the biomass that produces maximum sustainable yield occurred in the 1960s and the late 1990s for western and eastern populations, respectively. The model predicts that mixing depends on season, ontogeny, and location, and is highest in the western Atlantic. Assuming that future catches are zero, western and eastern populations are predicted to recover to levels at maximum sustainable yield by 2025 and 2015, respectively. However, the western population will not recover with catches of 1750 and 12,900 tonnes (the “rebuilding quotas”) in the western and eastern Atlantic, respectively, with or without closures in the Gulf of Mexico. If future catches are double the rebuilding quotas, then rebuilding of both populations will be compromised. If fishing were to continue in the eastern Atlantic at the unregulated levels of 2007, both stocks would continue to decline. Since populations mix on North Atlantic foraging grounds, successful rebuilding policies will
Atlantic bluefin tuna: a novel multistock spatial model for assessing population biomass.
Taylor, Nathan G; McAllister, Murdoch K; Lawson, Gareth L; Carruthers, Tom; Block, Barbara A
2011-01-01
Atlantic bluefin tuna (Thunnus thynnus) is considered to be overfished, but the status of its populations has been debated, partly because of uncertainties regarding the effects of mixing on fishing grounds. A better understanding of spatial structure and mixing may help fisheries managers to successfully rebuild populations to sustainable levels while maximizing catches. We formulate a new seasonally and spatially explicit fisheries model that is fitted to conventional and electronic tag data, historic catch-at-age reconstructions, and otolith microchemistry stock-composition data to improve the capacity to assess past, current, and future population sizes of Atlantic bluefin tuna. We apply the model to estimate spatial and temporal mixing of the eastern (Mediterranean) and western (Gulf of Mexico) populations, and to reconstruct abundances from 1950 to 2008. We show that western and eastern populations have been reduced to 17% and 33%, respectively, of 1950 spawning stock biomass levels. Overfishing to below the biomass that produces maximum sustainable yield occurred in the 1960s and the late 1990s for western and eastern populations, respectively. The model predicts that mixing depends on season, ontogeny, and location, and is highest in the western Atlantic. Assuming that future catches are zero, western and eastern populations are predicted to recover to levels at maximum sustainable yield by 2025 and 2015, respectively. However, the western population will not recover with catches of 1750 and 12,900 tonnes (the "rebuilding quotas") in the western and eastern Atlantic, respectively, with or without closures in the Gulf of Mexico. If future catches are double the rebuilding quotas, then rebuilding of both populations will be compromised. If fishing were to continue in the eastern Atlantic at the unregulated levels of 2007, both stocks would continue to decline. Since populations mix on North Atlantic foraging grounds, successful rebuilding policies will
Atlantic bluefin tuna: a novel multistock spatial model for assessing population biomass.
Directory of Open Access Journals (Sweden)
Nathan G Taylor
Full Text Available Atlantic bluefin tuna (Thunnus thynnus is considered to be overfished, but the status of its populations has been debated, partly because of uncertainties regarding the effects of mixing on fishing grounds. A better understanding of spatial structure and mixing may help fisheries managers to successfully rebuild populations to sustainable levels while maximizing catches. We formulate a new seasonally and spatially explicit fisheries model that is fitted to conventional and electronic tag data, historic catch-at-age reconstructions, and otolith microchemistry stock-composition data to improve the capacity to assess past, current, and future population sizes of Atlantic bluefin tuna. We apply the model to estimate spatial and temporal mixing of the eastern (Mediterranean and western (Gulf of Mexico populations, and to reconstruct abundances from 1950 to 2008. We show that western and eastern populations have been reduced to 17% and 33%, respectively, of 1950 spawning stock biomass levels. Overfishing to below the biomass that produces maximum sustainable yield occurred in the 1960s and the late 1990s for western and eastern populations, respectively. The model predicts that mixing depends on season, ontogeny, and location, and is highest in the western Atlantic. Assuming that future catches are zero, western and eastern populations are predicted to recover to levels at maximum sustainable yield by 2025 and 2015, respectively. However, the western population will not recover with catches of 1750 and 12,900 tonnes (the "rebuilding quotas" in the western and eastern Atlantic, respectively, with or without closures in the Gulf of Mexico. If future catches are double the rebuilding quotas, then rebuilding of both populations will be compromised. If fishing were to continue in the eastern Atlantic at the unregulated levels of 2007, both stocks would continue to decline. Since populations mix on North Atlantic foraging grounds, successful rebuilding
Data Model for Multi Hazard Risk Assessment Spatial Support Decision System
Andrejchenko, Vera; Bakker, Wim; van Westen, Cees
2014-05-01
The goal of the CHANGES Spatial Decision Support System is to support end-users in making decisions related to risk reduction measures for areas at risk from multiple hydro-meteorological hazards. The crucial parts in the design of the system are the user requirements, the data model, the data storage and management, and the relationships between the objects in the system. The implementation of the data model is carried out entirely with an open source database management system with a spatial extension. The web application is implemented using open source geospatial technologies with PostGIS as the database, Python for scripting, and Geoserver and javascript libraries for visualization and the client-side user-interface. The model can handle information from different study areas (currently, study areas from France, Romania, Italia and Poland are considered). Furthermore, the data model handles information about administrative units, projects accessible by different types of users, user-defined hazard types (floods, snow avalanches, debris flows, etc.), hazard intensity maps of different return periods, spatial probability maps, elements at risk maps (buildings, land parcels, linear features etc.), economic and population vulnerability information dependent on the hazard type and the type of the element at risk, in the form of vulnerability curves. The system has an inbuilt database of vulnerability curves, but users can also add their own ones. Included in the model is the management of a combination of different scenarios (e.g. related to climate change, land use change or population change) and alternatives (possible risk-reduction measures), as well as data-structures for saving the calculated economic or population loss or exposure per element at risk, aggregation of the loss and exposure using the administrative unit maps, and finally, producing the risk maps. The risk data can be used for cost-benefit analysis (CBA) and multi-criteria evaluation (SMCE). The
Characterization of Models for Time-Dependent Behavior of Soils
DEFF Research Database (Denmark)
Liingaard, Morten; Augustesen, Anders; Lade, Poul V.
2004-01-01
developed for metals and steel but are, to some extent, used to characterize time effects in geomaterials. The third part is a review of constitutive laws that describe not only viscous effects but also the inviscid ( rate-independent) behavior of soils, in principle, under any possible loading condition...... Different classes of constitutive models have been developed to capture the time-dependent viscous phenomena ~ creep, stress relaxation, and rate effects ! observed in soils. Models based on empirical, rheological, and general stress-strain-time concepts have been studied. The first part....... Special attention is paid to elastoviscoplastic models that combine inviscid elastic and time-dependent plastic behavior. Various general elastoviscoplastic models can roughly be divided into two categories: Models based on the concept of overstress and models based on nonstationary flow surface theory...
Which measurement strategies to improve spatial erosion and deposition patterns modelling?
Pineux, Nathalie; Maugnard, Alexandre; Swerts, Gilles; Bielders, Charles; Degré, Aurore
2014-05-01
Validation of the erosion models requires field data. To date, many authors continue to highlight the paucity of accurate field observations and long-term enough studies. The fields observations are often put aside because these measures are difficult to obtain: weighty experimental devices, climatic dependence, … Hence the models are evolving and propose refined calculation procedures including for instance the calculation of landscape evolution. The need of field data therefore increases and new measuring strategies should arise. In the centre of Belgium we choose an agricultural watershed quite representative of the local context. It covers 124 ha of loamy soil with more than 90% of arable land and a weak proportion of forest and artificial lands. The slope ranges between 0 and 9%. Instrumentation on the watershed includes meteorological observations and discharge measurement coupled with water sampling at different outlets. The weather data (radiation, temperature, wind velocity, relative humidity and rainfall) and discharge measurement (comparison between Doppler and pressure sensors) will allow us to model the hydrological behaviour of the catchment. Rainfall readings (tipping buckets) are completed with erosivity readings (disdrometer). Erosivity, together with soil data, land use and agricultural practices observations on field, will be used as entry in the Landsoil model. The sediment samplings at 3 points in the catchment will give an insight of the sediment delivery of 3 subcatchments. The Landsoil model calculates the evolution of the DTM through time. This cannot be compared to measurements at the outlet and requires further data collection. Older elevation data and/or archaeological data are a possible source of information even if their precision remains scarce in our context. 1950's soil surveys are on the contrary really informative since they detail the horizons depth in a spatial way and can be compared to new observation across the watershed
Impact of climate change on river flooding assessed with different spatial model resolutions
Booij, Martijn J.
2005-01-01
The impact of climate change on flooding in the river Meuse is assessed on a daily basis using spatially and temporally changed climate patterns and a hydrological model with three different spatial resolutions. This is achieved by selecting a hydrological modelling framework and implementing
Computer Games versus Maps before Reading Stories: Priming Readers' Spatial Situation Models
Smith, Glenn Gordon; Majchrzak, Dan; Hayes, Shelley; Drobisz, Jack
2011-01-01
The current study investigated how computer games and maps compare as preparation for readers to comprehend and retain spatial relations in text narratives. Readers create situation models of five dimensions: spatial, temporal, causal, goal, and protagonist (Zwaan, Langston, & Graesser 1995). Of these five, readers mentally model the spatial…
Model predictive control for optimal treatment in a spatial cancer game
Javier Muros, Francisco; M. Maestre, Jose; You, Li; Stankova, Katerina
2018-01-01
This work focuses on modeling tumorigenesis as a spatial evolutionary game and on ﬁnding optimal cancer treatment using a model predictive control approach. Extending a nonspatial cancer game from the literature into a spatial setting, we consider a solid tumor composed of cells of two different
A spatial-dynamic value transfer model of economic losses from a biological invasion
Thomas P. Holmes; Andrew M. Liebhold; Kent F. Kovacs; Betsy. Von Holle
2010-01-01
Rigorous assessments of the economic impacts of introduced species at broad spatial scales are required to provide credible information to policy makers. We propose that economic models of aggregate damages induced by biological invasions need to link microeconomic analyses of site-specific economic damages with spatial-dynamic models of value change associated with...
Directory of Open Access Journals (Sweden)
Dachs Gabi U
2004-11-01
Full Text Available Abstract Background Electroporation is currently receiving much attention as a way to increase drug and DNA delivery. Recent studies demonstrated the feasibility of electrogene therapy using a range of therapeutic genes for the treatment of experimental tumors. However, the transfection efficiency of electroporation-assisted DNA delivery is still low compared to viral methods and there is a clear need to optimize this approach. In order to optimize treatment, knowledge about spatial and time dependency of gene expression following delivery is of utmost importance in order to improve gene delivery. Intravital microscopy of tumors growing in dorsal skin fold window chambers is a useful method for monitoring gene transfection, since it allows non-invasive dynamic monitoring of gene expression in tumors in a live animal. Methods Intravital microscopy was used to monitor real time spatial distribution of the green fluorescent protein (GFP and time dependence of transfection efficiency in syngeneic P22 rat tumor model. DNA alone, liposome-DNA complexes and