Dissertations / Theses on the topic 'Spatial and temporal modelling'
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1
O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.
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The application of existing geostatistical theory to the context of stream networks provides a number of interesting and challenging problems. The most important of these is how to adapt existing theory to allow for stream, as opposed to Euclidean, distance to be used. Valid stream distance based models for the covariance structure have been denied in the literature, and this thesis explores the use of such models using data from the River Tweed. The data span a period of twenty-one years, beginning in 1986. During this time period, up to eighty-three stations are monitored for a variety of chemical and biological determinands. This thesis will focus on nitrogen, a key nutrient in determining water quality, especially given the Nitrates Directive (adopted in 1991) and the Water Framework Directive(adopted in 2002). These are European Union legislations that have set legally enforcable guidelines for controlling pollution which national bodies must comply with. The focus of analysis is on several choices that must be made in order to carry out spatial prediction on a river network. The role of spatial trend, whether it be based on stream or Euclidean distance, is discussed and the impact of the bandwidth of the estimate of nonparametric trend is explored. The stream distance based "tail-up" covariance model structure of Ver Hoef and Peterson (2010) is assessed and combined with a standard Euclidean distance based structure to form a mixture model. This is then evaluated using crossvalidation studies in order to determine the optimum mixture of the two covariance models for the data. Finally, the covariance models used for each of the elements of the mixture model are explored to determine the impact they have on the lowest root mean squared error, and the mixing proportion at which it is found. Using the predicted values at unobserved locations on the River Tweed, the distribution of yearly averaged nitrate levels around the river network is predicted and evaluated. Changes through the 21 years of data are noted and areas exceeding the limits set by the Nitrates Directive are highlighted. The differences in fitted values caused by using stream or Euclidean distance are evident in these predictions. The data is then modelled through space and time using additive models. A novel smoothing function for the spatial trend is defined. It is adapted from the tail-up model in order to retain its core features of flow connectivity and flow volume based weightings, in addition to being based on stream distance. This is then used to model all of the River Tweed data through space and time and identify temporal trends and seasonal patterns at different locations on the river.
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Kaimi, Irene. "Spatial and spatio-Temporal point processes, modelling and estimation." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525335.
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Xia, Jianhong, and not supplied. "Modelling the spatial-temporal movement of tourists." RMIT University. Mathematical and Geospatial Sciences, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080110.161021.
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Tourism is one of the most rapidly developing industries in the world. The study of spatio-temporal movement models of tourists are undertaken in variety of disciplines such as tourism, geography, mathematics, economics and artificial intelligence. Knowledge from these different fields has been difficult to integrate because tourist movement research has been conducted at different spatial and temporal scales. This thesis establishes a methodology for modelling the spatial-temporal movement of tourists and defines the spatial-temporal movement of tourists at both the macro and micro level. At the macro level, the sequence of tourist movements is modelled and the trend for tourist movements is predicted based on Markov Chain theory (MC). Log-linear models are then adopted to test the significance of the movement patterns of tourists. Tourism market segmentation based on the significant movement patterns of tourists is implemented using the EM (Expectation-Maximisation) algorithm. At the micro level, this thesis investigates the wayfinding decision-making processes of tourists. Four wayfinding models are developed and the relationships between the roles of landmarks and wayfinding decision-making are also discussed for each type of the wayfinding processes. The transition of a tourist movement between the macro and micro levels was examined based on the spatio-temporal zooming theory. A case study of Phillip Island, Victoria, Australia is undertaken to implement and evaluate the tourist movement models established in this thesis. Two surveys were conducted on Phillip Island to collect the macro and micro level movement data of tourists. As results show particular groups of tourists travelling with the same movement patterns have unique characteristics such as age and travel behaviours such as mode of transport. Effective tour packages can be designed based on significant movement patterns and the corresponding target markets. Tourists with various age groups, residency, gender and different levels of familiarity with physical environment have different wayfinding behaviours. The results of this study have been applied to tourism management on Phillip Island and the novel methods developed in this thesis have proved to be useful in improving park facilities and services provided to tourists, in designing tour packages for tourism market promotion and in understanding tourist wayfinding behaviours.
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Richardson, Jennifer. "Topics in statistics of spatial-temporal disease modelling." Thesis, Durham University, 2009. http://etheses.dur.ac.uk/2122/.
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This thesis is concerned with providing further statistical development in the area of space-time modelling with particular application to disease data. We briefly consider the non-Bayesian approaches of empirical mode decomposition and generalised linear modelling for analysing space-time data, but our main focus is on the increasingly popular Bayesian hierarchical approach and topics surrounding that. We begin by introducing the hierarchical Poisson regression model of Mugglin et al. [36] and a data set provided by NHS Direct which will be used to illustrate our results through-out the remainder of the thesis. We provide details of how a Bayesian analysis can be performed using Markov chain Monte Carlo (MCMC) via the software LinBUGS then go on to consider two particular issues associated with such analyses. Firstly, a problem with the efficiency of MCMC for the Poisson regression model is likely to be due to the presence of non-standard conditional distributions. We develop and test the 'improved auxiliary mixture sampling' method which introduces auxiliary variables to the conditional distribution in such a way that it becomes multivariate Normal and an efficient block Gibbs sampling scheme can be used to simulate from it. Secondly, since MCMC allows modelling of such complexity, inputs such as priors can only be elicited in a casual way thereby increasing the need to check how sensitive our output is to changes to the prior. We therefore develop and test the 'marginal sensitivity' method which, using only one MCMC output sample, quantifies how sensitive the marginal posterior distributions are to changes to prior parameters
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Navarrete, Miguel A. Ancona. "Dependence modelling and spatial prediction for extreme values." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369658.
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Zhao, Ruiqi. "Spatial and temporal modelling for automatic human behavioral analysis." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480683512602658.
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Jones-Todd, Charlotte M. "Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12009.
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Point processes are mechanisms that beget point patterns. Realisations of point processes are observed in many contexts, for example, locations of stars in the sky, or locations of trees in a forest. Inferring the mechanisms that drive point processes relies on the development of models that appropriately account for the dependencies inherent in the data. Fitting models that adequately capture the complex dependency structures in either space, time, or both is often problematic. This is commonly due to—but not restricted to—the intractability of the likelihood function, or computational burden of the required numerical operations. This thesis primarily focuses on developing point process models with some hierarchical structure, and specifically where this is a latent structure that may be considered as one of the following: (i) some unobserved construct assumed to be generating the observed structure, or (ii) some stochastic process describing the structure of the point pattern. Model fitting procedures utilised in this thesis include either (i) approximate-likelihood techniques to circumvent intractable likelihoods, (ii) stochastic partial differential equations to model continuous spatial latent structures, or (iii) improving computational speed in numerical approximations by exploiting automatic differentiation. Moreover, this thesis extends classic point process models by considering multivariate dependencies. This is achieved through considering a general class of joint point process model, which utilise shared stochastic structures. These structures account for the dependencies inherent in multivariate point process data. These models are applied to data originating from various scientific fields; in particular, applications are considered in ecology, medicine, and geology. In addition, point process models that account for the second order behaviour of these assumed stochastic structures are also considered.
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Miller, Gordon. "Spatial and temporal modelling of human campylobacteriosis in Scotland." Thesis, University of Aberdeen, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487338.
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Campylobacterjejuni infections are the largest recognisedcause ofbactenal gastroenteritis in the developed world. However, much of the epidemiology of these infections remains unclear. In this study, spatial and temporal modeling techniques are applied to various data sets ofreported human campylobacteriosis cases in Scotland in order to elucidate the aetiology of these infections. Time series analysis techniques are used to construct seasonal models of the national and regional incidence rates (by NHS Health Board). These are used to identify seasonal patterns, regional variation, and uncommon bursts ofinfection, as well as to predict future incidence trends. Significant regional differences are found in the annual incidence rates and seasonal patterns, particularly between Scotland's two largest population centres, Greater Glasgow and Lothian. Spatial mapping techniques ar?-,used to analyse incidence rates, stratified by age groups, at a subregional level for Grampian, Scotland. High incidence rates are identified in young children, particularly in rural areas. Regression analysis ofincidence rates against densities of farm animals, which excrete large numbers ofthe bacteria into the environment, demonstrate high correlations between young children and densities of cattle, sheep and pigs, indicating that environmental exposure may be a more significant risk than previously recognised. A large dataset ofserotyped human isolates in Grampian are studied to identify the variations in the types found between age groups in both urban and rural regions. The more common serotypes are found to be less evident in the older age groups, which could suggest a developed immunity to certain types with age. Further, variation is identified in the seasonality ofserotypes in young children between urban and rural regions, indicating . . further influence of environmental infection pathways. The Use of the spatial and temporal modeling techniques in this thesis develops the . epidemological understanding of C. jejuni infection, as well as indicating areas for future research.
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Northrop, Paul James. "Modelling and statistical analysis of spatial-temporal rainfall fields." Thesis, University College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340891.
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Russell, David Mark. "Spatial and temporal background modelling of non-stationary visual scenes." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/598.
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The prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally.
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Chen, C. C. "Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18517/.
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Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses.
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Clifford, Sam. "Spatio-temporal modelling of ultrafine particle number concentration." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63528/4/Samuel_Clifford_Thesis.pdf.
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This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates.
The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.
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Paulionienė, Laura. "Statistical modelling of spatio-temporal data based on spatial interpolation of time series parameters." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140117_113114-31261.
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Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu.
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Cramb, Susanna M. "Spatio-temporal modelling of cancer data in Queensland using Bayesian methods." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/89739/1/Susanna_Cramb_Thesis.pdf.
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Cancer is the leading contributor to the disease burden in Australia. This thesis develops and applies Bayesian hierarchical models to facilitate an investigation of the spatial and temporal associations for cancer diagnosis and survival among Queenslanders. The key objectives are to document and quantify the importance of spatial inequalities, explore factors influencing these inequalities, and investigate how spatial inequalities change over time. Existing Bayesian hierarchical models are refined, new models and methods developed, and tangible benefits obtained for cancer patients in Queensland. The versatility of using Bayesian models in cancer control are clearly demonstrated through these detailed and comprehensive analyses.
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Kerlin, Douglas H. "Modelling the spatial and temporal dynamics of upland birds in Scotland." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/879/.
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Population numbers change in space and time. The construction of models to investigate the spatial and temporal dynamics of populations may offer a means to identify the processes driving this change. In this thesis, we make use of models to examine the population ecology of three species of upland birds: red grouse, meadow pipit and capercaillie. Populations of red grouse in the British Isles exhibit cyclic fluctuations in abundance. Time series data from 287 grouse moors across the United Kingdom were analysed to investigate co-variation in these fluctuations. Results indicate high levels of synchrony between populations on neighbouring moors, with synchrony declining with increasing intermoor distance. At distances greater than 100km, populations exhibit only weak synchrony. Synchrony is shown to be a product of strong coupling events, which occur on average every one in six years. In the absence of such events, synchrony is shown to dissipate within three years. Further, we present evidence which suggests this coupling is driven (at least in part) by dispersal between populations. The density dependent structures are also found to be sufficiently homogeneous to allow correlations in climate to synchronise dynamics, but examination of three climate variables failed to detect a relationship. We also studied the population dynamics of meadow pipits in upland grassland ecosystems. Data, collected as part of an ongoing grazing field experiment, were analysed to construct a Bayesian model of population growth, and predict the effect of grazing intensity on meadow pipit populations. Results suggest grazing has a significant impact on population growth. Grazing may act to improve meadow pipit foraging efficiency and thus productivity. Finally, a spatially explicit population viability model was constructed to predict changes in the future abundance and distribution of capercaillie. Published estimates of key demographic variables were drawn from the literature to parameterise the model. The spatial structure of the population was inferred from spatial data, documenting the extent and configuration of remnant pine woodlands in Scotland. The model predicts a low probability of extinction for capercaillie in the future, and offers insights into key processes affecting the distribution and abundance of this species. The development of these models has advanced our understanding of the environmental processes driving changes in the spatial and temporal dynamics of these species. The results of these studies may be useful in anticipating the future consequences of various drivers of change on the ecology of upland species.
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Luo, Weiqi. "Spatial/temporal modelling of crop disease data using high-dimensional regression." Thesis, University of Leeds, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493292.
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Septoria tritici is one of the most serious foliar diseases of winter wheat across England and Wales, causing considerable reduction in yield quality and production. There are increasing pressures to control such a disease using disease forecasting systems sociated with various meteorological factors.
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Franco, Villoria Maria. "Temporal and spatial modelling of extreme river flow values in Scotland." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4017/.
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Extreme river flows can lead to inundation of floodplains, with consequent impacts for society, the environment and the economy. Flood risk estimates rely on river flow records, hence a good understanding of the patterns in river flow, and, in particular, in extreme river flow, is important to improve estimation of risk. In Scotland, a number of studies suggest a West to East rainfall gradient and increased variability in rainfall and river flow. This thesis presents and develops a number of statistical methods for analysis of different aspects of extreme river flows, namely the variability, temporal trend, seasonality and spatial dependence. The methods are applied to a large data set, provided by SEPA, of daily river flow records from 119 gauging stations across Scotland. The records range in length from 10 up to 80 years and are characterized by non-stationarity and long-range dependence. Examination of non-stationarity is done using wavelets. The results revealed significant changes in the variability of the seasonal pattern over the last 40 years, with periods of high and low variability associated with flood-rich and flood-poor periods respectively. Results from a wavelet coherency analysis suggest significant influence of large scale climatic indices (NAO, AMO) on river flow. A quantile regression model is then developed based on an additive regression framework using P-splines, where the parameters are fitted via weighted least squares. The proposed model includes a trend and seasonal component, estimated using the back-fitting algorithm. Incorporation of covariates and extension to higher dimension data sets is straightforward. The model is applied to a set of eight Scottish rivers to estimate the trend and seasonality in the 95th quantile of river flow. The results suggest differences in the long term trend between the East and the West and a more variable seasonal pattern in the East. Two different approaches are then considered for modelling spatial extremes. The first approach consists of a conditional probability model and concentrates on small subsets of rivers. Then a spatial quantile regression model is developed, extending the temporal quantile model above to estimate a spatial surface using the tensor product of the marginal B-spline bases. Residual spatial correlation using a Gaussian correlation function is incorporated into standard error estimation. Results from the 95th quantile fitted for individual months suggest changes in the spatial pattern of extreme river flow over time. The extension of the spatial quantile model to build a fully spatio-temporal model is briefly outlined and the main statistical issues identified.
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Duncan, Earl W. "Bayesian approaches to issues arising in spatial modelling." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.
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This thesis addressed several contemporary issues arising in the analysis of spatial data and the broader statistical methodology. Two state-of-the-art statistical models are developed for the purpose of identifying unusual trends, a new algorithm to deal with label switching is devised which outperforms existing solutions, and new approaches to spatial smoothing are explored. The outcomes from this thesis should be of interest to managers in the health sector, biostatisticians, and researchers who deal with spatial data.
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Nyström, Sandman Antonia. "Modelling spatial and temporal species distribution in the Baltic Sea phytobenthic zone." Doctoral thesis, Stockholms universitet, Systemekologiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-54269.
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Statistical modelling is often used to relate the presence or abundance of species to environmental predictors, thereby providing a basis for predictive mapping of species or biodiversity. The variables included must thus be relevant and reflect actual changes in the environment. Therefore, the quantification of species–environment relationships is an important aspect of predictive modelling. This thesis examines how phytobenthic species or communities in the Baltic Sea relate to environmental gradients, and if different aspects of phytobenthic species distribution in the Baltic Sea could be explained by spatial or temporal variation in environmental factors. Predictive distribution modelling usually focuses on how environmental variables control the distribution of species or communities. Thus the relative weight of the predictor variables on different scales is of importance. In this thesis, I show that the relative importance of environmental variables depends both on geographic scale and location, and that it also differs between species or species groups. There are no simple explanations to the temporal variability in species occurrence. I here show that the temporal changes in species distribution within the phytobentic zone varies in a spatial context. I also try to find temporal and spatio-temporal patterns in species distribution that could be related to changes in climate or anthropogenic disturbance. However, the findings in this thesis suggest that single factor explanations are insufficient for explaining large-scale changes in species distribution. A greater understanding of the relationship between species and their environment will lead to the development of more sensitive models of species distributions. The predictions can be used to visualise spatial changes in the distribution of plant and animal communities over time.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript.
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Christian, James Robert. "Modelling studies on a marine plankton community : biological, temporal and spatial structure." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/27859.
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The SELECT model (Frost, 1982) is analyzed, criticized, and extended to embrace new information about the feeding behaviour of copepods and the structure of the planktonic food web in a series of alternative models. Diel variations in photosynthesis, grazing, and predation on copepods (temporal structure) and patchiness of zooplankton and their predators (spatial structure) are modelled in other variants. It is observed that the vertical, temporal, and (horizontal) spatial structure of the planktonic ecosystem are important components of ecosystem models that can not safely be ignored. It is further observed that a convincing mechanism for the termination of diatom blooms is lacking and should be a subject of intensive research, and that the status of chlorophyll-containing microflagellates as phototrophs is questionable and should be reconsidered.Science, Faculty of
Zoology, Department of
Graduate
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Biberacher, Markus. "Modelling and optimisation of future energy systems : using spatial and temporal methods /." Saarbrücken : VDM Verlag Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2995456&prov=M&dok_var=1&dok_ext=htm.
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Biberacher, Markus. "Modelling and optimisation of future energy systems using spatial and temporal methods." Saarbrücken VDM Verlag Dr. Müller, 2004. http://d-nb.info/985380772/04.
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Comas, Rodriguez Carlos. "Modelling forest dynamics through the development of spatial and temporal marked point processes." Thesis, University of Strathclyde, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415363.
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Mao, Zhun. "Temporal and spatial modelling of root reinforcement in natural montane and subalpine forests." Thesis, Montpellier 2, 2011. http://www.theses.fr/2011MON20118.
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Il est généralement admis que la végétation peut stabiliser les pentes naturelles et artificielles contre les glissements de terrain superficiel. Par rapport au rôle mécanique, les racines renforcent le sol d'une pente en fournissant une cohésion additionnelle (cr). La quantification des cr est une étape essentielle pour évaluer la stabilité des pentes, quantifiée par le facteur de sécurité (FoS, défini par le ratio entre les forces de la résistance et les forces motrices sur une pente). La plupart des modèles prédictifs de cr existants ne prennent pas en compte la dynamique racinaire à l'échelle spatiale et temporelle qui peut conduire à une hétérogénéité de renforcement des racines. Ainsi, cette thèse vise à caractériser, quantifier et modéliser la répartition spatiale et temporelle de la dynamique racinaire ainsi que son impact sur la cr estimée. La distribution, croissance et mortalité racinaire ont été échantillonnées à l'aide de monolithes et de rhizotrons à deux altitudes dans des forêts mixtes et naturellement régénérées dans les Alpes françaises, composées d'îlots et de trouées. Avec les méthodes de modélisation statistique, une série de facteurs abiotiques et biotiques affectant la dynamique racinaire ont été étudiés. Pour quantifier les cr, une méta-analyse a été effectuée et les divers algorithmes de modélisation ont été employés et leurs résultats comparés. Cette étude a montré que: (i) dans un écosystème à espèces mixtes, la densité racinaire influence davantage les cr que les propriétés de la qualité racinaire; (ii) tous les facteurs abiotiques (altitude, paysage écologique, profondeur du sol et mois) peuvent faire varier la densité racinaire selon des conditions différentes du sol ; (iii) lors de l'observation de 1,5 ans à l'aide de rhizotrons, le cr augmente continuellement, rapidement dans la saison active et lentement pendant la saison dormante, mais cette augmentation est dépendante de la profondeur du sol, de l'altitude et du patch écologique ; (iv) malgré le fait que les racines les plus fines (]0, 1] mm en diamètre) soient les plus actives dans le cycle de nutritions et de carbone (selon des publications précédentes), elles contribuent peu au renforcement mécanique de terrain. Cette étude a permis d'élargir et d'approfondir nos connaissances sur le rôle des racines dans l'éco-ingénierieIt is largely recognized that vegetation can stabilize artificial and natural slopes against shallow landslides. Mechanically, plant roots reinforce soil on a slope by providing an additional cohesion (cr). Quantification of cr is a key step to estimate the stability of a given slope, usually quantified by the Factor of Safety (FoS, defined as the ratio between resisting forces and the driving forces on a slope). Most existing cr predictive models do not take into consideration spatial and temporal root dynamics which result in heterogeneous root reinforcement along a vegetated slope. Therefore, this thesis aims to characterize, quantify and model the spatial and temporal patterns in root dynamics and their impact on the estimation of cr. Root distribution, growth and mortality were measured using monoliths and rhizotrons installed at two altitudes in naturally regenerated mixed forests in the French Alps. These forests are composed of trees growing in groups (tree islands) with large gaps between the islands. Using statistical modeling approaches, abiotic and biotic factors affecting root dynamics were investigated. For quantifying cr, a meta-analysis was performed and different modeling algorithms were employed and results compared. Based on these studies, the following conclusions were made: (i) in a mixed, mature forest ecosystem root density influenced cr more than root mechanical properties; (ii) all abiotic factors (altitude, type of vegetation patch, soil depth and month) significantly affected root quantity to different degrees, depending on soil conditions; (iii) during the 1.5 years' observations in rhizotron, cr increased rapidly during the growing season and more slowly in the dormant season but the increment increase was largely dependent on soil depth, altitude and vegetation patch. (iv) The finest roots (]0, 1] mm in diameter), which are considered the most important for nutrient and carbon cycling, contributed little to mechanical reinforcement of the soil. Results are discussed with regard to ecological engineering strategies for unstable slopes
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Wilson, Christopher James. "Analysing recent spatial and temporal atmospheric methane variations using forward and inverse modelling." Thesis, University of Leeds, 2011. http://etheses.whiterose.ac.uk/3666/.
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The TOMCAT 3-D chemical transport model (CTM) has been used to investigate the cause of recent variations in global atmospheric methane (CH4), focusing on examining changes in the balance of sources and sinks of the species. The chemical loss, transport and emissions of methane have been studied and a new 4D-Var inverse version of TOMCAT has been created. The accuracy of the TOMCAT model transport was investigated by simulating the distribution of the long-lived species SF6. A range of model grid resolutions, boundary layer schemes and advection schemes were tested. New retrievals from the Atmospheric Chemistry Experiment (ACE) satellite instrument were used to test the model in the upper troposphere and lower stratosphere. The standard CTM simulated the observed distribution and growth of SF6 well. However, based on comparison with ground-based data, the interhemispheric transport in the TOMCAT model was found to be approximately 20% too slow, with too little temporal variation in southern hemisphere transport. On the whole, however, tracer transport in the CTM using its standard set-up was accurate. As a basis for the inverse model simpler advection and boundary layer (BL) schemes were tested. The advection scheme which conserved only up to first-order moments (rather than secondorder moments) did not significantly reduce the accuracy of the model transport. However, use of a local boundary layer mixing scheme rather than a non-local scheme did degrade the quality of the transport by reducing the speed of vertical mixing out of the BL. A number of currently used CH4 emission inventories were used with the forward TOMCAT model in order to examine the effect they have on the global CH4 budget, and two different estimates of the OH sink were also tested. A published OH field derived from global CH3CCl3 and a chemical box model was found to be more consistent with OH observations than the field from the full chemistry TOMCAT model. Although both OH fields produced global CH4 lifetimes consistent with published estimates, the TOMCAT OH field yielded model CH4 which was up to 100 ppb higher than observations at the surface. Data assimilation was used to improve the estimate of the stratospheric sink of CH4. Although this sink is small overall, it needs to be represented realistically in order to accurately reproduce global CH4 to within 10 ppbv. A new adjoint version of the TOMCAT model was produced by explicit coding, and was thoroughly tested. This was incorporated into a new 4D-Var inverse model which can be used to produce updated CH4 surface flux estimates which are constrained to agree with atmospheric observations. The inverse model was used to investigate emissions in the Arctic where the forward TOMCAT model and standard emissions revealed a seasonal cycle out of phase with surface CH4 observations. It was found that northern hemisphere summertime wetland emissions were overestimated in the GISS inventory by up to 100% for the period 2000-2006, and that this was likely due to the estimates of emission rates and thaw period used when producing that inventory. It was also found that increased Asian emissions suggested in the EDGAR V4.0 inventory are not consistent with observations unless mitigated by a corresponding drop in emissions elsewhere.
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Sumner, Christian John. "Spatio-temporal processing in auditory modelling." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300846.
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Said, Raed Ahmad Tawfeq. "Spatio-temporal modelling of seismic data." Thesis, University of Leeds, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441328.
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Jamtsho, Sonam. "Spatio-temporal modelling and analysis of spatial accessibility to primary health care: A case study of Bhutan." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/671.
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Both spatial and aspatial dimensions of healthcare system are important in strengthening the healthcare system of any country. Knowing the spatial aspects of healthcare accessibility can help develop proper health policies in planning equitable allocation of health resources across the country. This thesis deals with the modelling of population and spatial accessibility using GIS, and an analysis of spatial and temporal changes in accessibility to healthcare services in Bhutan.
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Aswi. "Bayesian spatio-temporal modelling of small areas: Dengue fever in Makassar Indonesia." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200547/1/_Aswi_Thesis.pdf.
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Dengue fever is still a serious problem in Indonesia, including Makassar. This thesis evaluates and applies different Bayesian spatial and spatio-temporal models when there are a small number of areas plus extends Bayesian spatial Weibull and Semiparametric Cox survival models to describe dengue hospital duration spatial patterns to help understand dengue fever in Makassar Indonesia. The key objectives are to investigate the impact of a small number of areas on the estimation of spatial and spatio-temporal models. The convenience of using Bayesian spatial and spatio-temporal models on dengue fever is clearly demonstrated through this detailed and comprehensive analysis.
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Nicholson, James P. G. "Modelling spatial and temporal patterns of ozone concentrations around a high-latitude urban area." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/15522.
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A Lagrangian column model, featuring a detailed description of vertical mixing, has been developed to simulate the three-dimensional structure in ozone and nitrogen oxides (NOx) concentrations in the boundary-layer, within and downwind of high-latitude urban areas. The short time-scale photochemical processes of ozone and NOx, as well as emissions and deposition to the ground, are simulated. This has been used to follow a range of one-dimensional trajectories, over a distance of 100km and a travel time in the order of 104s, through a simulated city under a variety of meteorological and pollutant emission regimes representing seasonal and diurnal extremes. An assessment of the extent of ozone destruction occurring, the rate of recovery of surface ozone concentrations downwind of the city and the influence of meteorological parameters on the ozone concentration, has been provided using the model. The model, with a spatial resolution of 1x1km, is also applied over a l00xl00km domain containing a simulation of the NOx emission field from the city of Edinburgh, UK (at latitude 560N) to simulate the two-dimensional city-scale processes of pollutants. Results are presented, using averaged wind-flow frequencies and appropriate stability conditions, to show the extent of the depletion of ozone by the city. The long-term average spatial patterns in the surface ozone and NOx concentrations over the model domain are reproduced quantitatively and are compared with observations from a series of monitoring sites on a north-south trajectory through the city and into rural areas. The model shows the average surface ozone concentrations in the urban area to be lower than the surrounding rural areas by typically 50% and that the areas experiencing a 20% ozone depletion over background values are generally restricted to within the boundaries of the urban area. From vertical profiles of ozone concentrations, the depletion of ozone to less than 50% of the rural surface values generally extends not more than 20m vertically above the urban area.
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Scerri, Kenneth. "A systems approach to spatio-temporal modelling." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522371.
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Mitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.
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Marinelli, Marco Antonio. "Modelling and communicating the effects of spatial data uncertainty on spatially based decision-making." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/1842.
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Important economic and environmental decisions are routinely based on spatial/ temporal models. This thesis studies the uncertainty in the predictions of three such models caused by uncertainty propagation. This is considered important as it quantifies the sensitivity of a model’s prediction to uncertainty in other components of the model, such as the model’s inputs. Furthermore, many software packages that implement these models do not permit users to easily visualize either the uncertainty in the data inputs, the effects of the model on the magnitude of that uncertainty, or the sensitivity of the uncertainty to individual data layers. In this thesis, emphasis has been placed on demonstrating the methods used to quantify and then, to a lesser extent, visualize the sensitivity of the models. Also, the key questions required to be resolved with regards to the source of the uncertainty and the structure of the model is investigated. For all models investigated, the propagation paths that most influence the uncertainty in the prediction were determined. How the influence of these paths can be minimised, or removed, is also discussed.Two different methods commonly used to analyse uncertainty propagation were investigated. The first is the analytical Taylor series method, which can be applied to models with continuous functions. The second is the Monte Carlo simulation method which can be used on most types of models. Also, the later can be used to investigate how the uncertainty propagation changes when the distribution of model uncertainty is non Gaussian. This is not possible with the Taylor method.The models tested were two continuous Precision Agriculture models and one ecological niche statistical model. The Precision Agriculture models studied were the nitrogen (N) availability component of the SPLAT model and the Mitscherlich precision agricultural model. The third, called BIOCLIM, is a probabilistic model that can be used to investigate and predict species distributions for both native and agricultural species.It was generally expected that, for a specific model, the results from the Taylor method and the Monte Carlo will agree. However, it was found that the structure of the model in fact influences this agreement, especially in the Mitscherlich Model which has more complex non linear functions. Several non-normal input uncertainty distributions were investigated to see if they could improve the agreement between these methods. The uncertainty and skew of the Monte Carlo results relative to the prediction of the model was also useful in highlighting how the distribution of model inputs and the models structure itself, may bias the results.The version of BIOCLIM used in this study uses three basic spatial climatic input layers (monthly maximum and minimum temperature and precipitation layers) and a dataset describing the current spatial distribution of the species of interest. The thesis investigated how uncertainty in the input data propagates through to the estimated spatial distribution for Field Peas (Pisum sativum) in the agriculturally significant region of south west Western Australia. The results clearly show the effect of uncertainty in the input layers on the predicted specie’s distribution map. In places the uncertainty significantly influences the final validity of the result and the spatial distribution of the validity also varies significantly.
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Briët, Olivier J. T. "Towards malaria prediction in Sri Lanka : modelling spatial and temporal variability of malaria case counts /." Basel : [s.n.], 2008. http://edoc.unibas.ch/diss/DissB_8750.
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Piyawong, Wirawan. "Spatio-temporal numerical modelling of whooping cough dynamics." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/6626.
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The SIR (Susceptible/Infectious/Recovered) whooping cough model involving nonlinear ordinary differential equations is studied and extended to incorporate (i) diffusion (ii) convection and (iii) diffusion-convection in one-space dimension. Firstand second-order finite-difference methods are developed to obtained the numerical solutions of the ordinary differential equations. Though implicit in nature, with the resulting improvements in stability, the methods are applied explicitly. The proposed methods are economical and reliable in comparison to classical numerical methods. When extended to the numerical solutions of the partial differential equations, the solutions are found by solving a system of linear algebraic equations at each time step, as opposed to solving a non-linear system, which often happens when solving non-linear partial differential equations.
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Goodfellow, Marc. "Spatio-temporal modelling and analysis of epileptiform EEG." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/spatiotemporal-modelling-and-analysis-of-epileptiform-eeg(0f76259a-1a58-44a9-b08b-1402c9b49896).html.
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In this thesis we investigate the mechanisms underlying the generation of abnormal EEG rhythms in epilepsy, which is a crucial step towards better treatment of this disorder in the future. To this end, macroscopic scale mathematical models of the interactions between neuronal populations are examined. In particular, the role of interactions between neural masses that are spatially distributed in cortical networks are explored. In addition, two other important aspects of the modelling process are addressed, namely the conversion of macroscopic model variables into EEG output and the comparison of multivariate, spatio-temporal data. For the latter, we adopt a vectorisation of the correlation matrix of windowed data and subsequent comparison of data by vector distance measures. Our modelling studies indicate that excitatory connectivity between neural masses facilitates self-organised dynamics. In particular, we report for the first time the production of complex rhythmic transients and the generation of intermittent periods of 'abnormal' rhythmic activity in two different models of epileptogenic tissue. These models therefore provide novel accounts of the spontaneous, intermittent transition between normal and pathological rhythms in primarily generalised epilepsies and the evocation of complex, self-terminating, spatio-temporal dynamics by brief stimulation in focal epilepsies. Two key properties of these models are excitability at the macroscopic level and the presence of spatial heterogeneities. The identification of neural mass excitability as an important processes in spatially extended brain networks is a step towards uncovering the multi-scale nature of the pathological mechanisms of epilepsy. A direct consequence of this work is therefore that novel experimental investigations are proposed, which in itself is a validation of our modelling approach. In addition, new considerations regarding the nature of dynamical systems as applied to problems of transitions between rhythmic states are proposed and will prompt future investigations of complex transients in spatio-temporal excitable systems.
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Naish, Suchithra. "Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/55047/1/Suchithra_Naish_Thesis.pdf.
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Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios.
Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads.
Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state.
A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland.
Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors.
Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland.
Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia.
Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia.
In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.
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Cabrera, Maritza. "Spatio-temporal modelling of dengue fever in Zulia state, Venezuela." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616878.
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Over half of the world's population are at risk of infection from dengue fever (Guha-Sapir 2005). This viral disease is transmitted by the female Aedes aegypti mosquito and is the major source of human death in the world when compared with any other vector borne disease (Gubler1998a). The first important epidemic of dengue haemorrhagic fever (DHF) in America was reported in Cuba in 1981 and subsequently in Venezuela during 1989 and 1990 (Oletta2006, Brightmer1998). There has been a trend of increased incidence in many Central and South American countries since 1990 - Brazil, Venezuela, Honduras and Mexico (SanMartin2010) with Venezuela having the highest number of cases of DHF. The urgent need for more effective public health measures to combat this disease in Venezuela drove the decision to undertake the work described in this dissertation. Spatio-Temporal modelling has been developed for the prediction of the occurrence of dengue fever in Zulia state, Venezuela. A systematic approach has been adopted to validate this tool. At the first stage of the analysis an exploratory study was performed to underline the most significant features of the dynamics of incidence rates of dengue fever from 2002 to 2008. In the second stage a Generalized Linear Model (GLM) approach was used in the form of Negative Binomial Generalized Linear Mixed model (GLMM) to compare Relative Risk (RR) across exposure groups by age and sex, using an epidemiological dataset covering the whole of Zulia State, Venezuela. This approach used both a frequentist and a Bayesian perspective for comparative purposes of both outcomes and methodologies. Finally a Spatio-Temporal model was constructed based on Generalized Additive Mixed model (GAMM) framework because the earlier analysis identified a complex association between covariates and response variables. This GAMM structure was further developed so that it could be used to help predict future outbreaks of the disease in Zulia state with a good degree of accuracy.
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Alowadi, Nahed. "Population based spatio-temporal probabilistic modelling of fMRI data." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8210/.
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High-dimensional functional magnetic resonance imaging (fMRI) data is characterized by complex spatial and temporal patterns related to neural activation. Mixture based Bayesian spatio-temporal modelling is able to extract spatiotemporal components representing distinct haemodyamic response and activation patterns. A recent development of such approach to fMRI data analysis is so-called spatially regularized mixture model of hidden process models (SMM-HPM). SMM-HPM can be used to reduce the four-dimensional fMRI data of a pre-determined region of interest (ROI) to a small number of spatio-temporal prototypes, sufficiently representing the spatio-temporal features of the underlying neural activation. Summary statistics derived from these features can be interpreted as quantification of (1) the spatial extent of sub-ROI activation patterns, (2) how fast the brain respond to external stimuli; and (3) the heterogeneity in single ROIs. This thesis aims to extend the single-subject SMM-HPM to a multi-subject SMM-HPM so that such features can be extracted at group-level, which would enable more robust conclusion to be drawn.
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Davies, T. P. "Spatio-temporal modelling for issues in crime and security." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1460300/.
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The distribution of incidents in time and space is a central issue in the study of crime, for both theoretical and practical reasons. It is also a context in which quantitative analysis and modelling has significant potential value: such research represents a means by which the implications of theory can be examined rigorously, and can also provide tools which support both policing and policy-making. The nature of the field, however, presents a number of challenges, particularly with regard to the incorporation of complex environmental factors and the modelling of individual-level behaviour. In this thesis, the techniques of complexity science are used to overcome these issues, and the approach is demonstrated using a number of examples from a range of crime types. The thesis begins by presenting a network-based framework for the analysis of spatio-temporal clustering. It is demonstrated that signature `motifs' can be identified in patterns of offending for burglary and maritime piracy, and that the technique provides a more nuanced characterisation of clustering than existing approaches. Analysis is then presented of the relationship between street network structure and the distribution of urban crime. It is shown that burglary risk is predicted by the graph-theoretic properties of street segments; in particular, those which correspond to levels of street usage. It is further demonstrated that the `near-repeat' phenomenon in burglary displays a form of directionality, which can be reconciled with a novel street network metric. These results are then used to inform a mathematical model of burglary, which is situated on a network and which may be used for prediction. This model is analysed and its behaviour characterised in terms of urban form. Finally, a model is presented for a contrasting crime problem, the London riots of 2011, and used to examine a number of policy questions.
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Ranciati, Saverio <1988>. "Statistical modelling of spatio-temporal dependencies in NGS data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7680/.
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Next-generation sequencing (NGS) has rapidly become the current standard in genetic related analysis. This switch from microarray to NGS required new statistical strategies to address the research questions inherent to the considered phenomena. First and foremost, NGS dataset usually consist of discrete observations characterized by overdispersion - that is, discrepancy between expected and observed variability - and an abundance of zeros, measured across a huge number of regions of the genome. With respect to chromatin immunoprecipitation sequencing (ChIP-Seq), a class of NGS data, it is of primary focus to discover the underlying (unobserved) pattern of `enrichment': more particularly, there is interest in the interactions between genes (or broader regions of the genome) and proteins, as they describe the mechanism of regulation under different conditions such as healthy or damaged tissue. Another interesting research question involves the clustering of these observations into groups that have practical relevance and interpretability, considering in particular that a single unit could potentially be allocated into more than one of these clusters, as it is reasonable to assume that its participation is not exclusive to one and only biological function and/or mechanism. Many of these complex processes, indeed, could also be described by sets of ordinary differential equations (ODE's), which are mathematical representations of the changes of a system through time, following a dynamic that is governed by some parameters we are interested in. In this thesis, we address the aforementioned tasks and research questions employing different statistical strategies, such as model-based clustering, graphical models, penalized smoothing and regression. We propose extensions of the existing approaches to better fit the problem at hand and we elaborate the methodology in a Bayesian environment, with the focus on incorporating the structural dependencies - both spatial and temporal - of the data at our disposal.
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Anandanadesan, Ananthi. "Modelling the spatio-temporal dynamics of aphid-parasitoid interactions." Thesis, University of Dundee, 2014. https://discovery.dundee.ac.uk/en/studentTheses/b6ace29c-4d4b-4e66-acd7-3f8a39c0656c.
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Hanson, A. R. "Modelling the spatial and temporal variation in density of breeding black ducks at landscape and regional levels." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58215.pdf.
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Li, Zheyuan. "Generalized additive models for large datasets : spatial-temporal modelling of the UK's Daily Black Smoke (1961-2005)." Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767604.
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The UK Black Smoke monitoring network has produced daily particulate air pollution data from a network of up to 1200 monitoring stations over several decades, resulting in 10 million measurements in total. Spatial-temporal modelling of the data is desirable for accurate trend/seasonality estimation and mapping and to provide daily exposure estimates for epidemiological cohort studies. Generalized additive models offer one way to do this if we can deal with the data volume and model size. This thesis will develop computation method for estimating generalized additive models having $O(10^4)$ coefficients and $O(10^8)$ observations. The strategy combines 3 elements: (i) fine scale discretization of covariates, (ii) an efficient approach to restricted likelihood optimization, that avoids computation of numerically awkward log determinant terms and (iii) restricted likelihood optimization algorithms that make good use of numerical linear algebra methods with high performance and good parallel scaling on mordern multi-core machines. The new method enables us to estimate spatial-temporal models for daily Black Smoke data over the last four decades at a daily resolution which had once been infeasible. A spatial-temporal dataset of daily Black Smoke is also produced on a grid of 5km x 5km resolution. Our prediction is shown to suffer from little extrapolation and bias.
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Pakdel, Sahar. "Spatial–temporal Modelling for Estimating Impacts of Storm Surge and Sea Level Rise on Coastal Communities: The Case of Isle Madame in Cape Breton, Nova Scotia, Canada." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20178.
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More frequent and harsh storms coupled with sea level rise are affecting Canada’s sensitive coastlines. This research studies Isle Madame in Cape Breton, Nova Scotia which has been designated by Natural Resource Canada as a sea level rise vulnerable coastal community in Canada. The research models the spatial and temporal impacts of sea level rise from storm surge by focusing on identifying vulnerable areas in the community via geographical information systems (GIS) using ArcGIS, as well as modeling dynamic coastal damage via system dynamics using STELLA. The research evaluates the impacts in terms of the environmental, social, cultural, economic pillars that profile the coastal community for a series of modelled Storm Scenarios. This research synthesizes information from a variety of sources including the coastal ecology and natural resources, as well as human society and socioeconomic indicators included in the four mentioned pillars. The objective of the research is to determine vulnerable areas on Isle Madame susceptible to storm damage, and consequently, to improve local community knowledge and preparedness to more frequent harsh storms. This research therefore presents a dynamic model for the evaluation of storm impacts in Isle Madame designed with the goal to help the community ultimately to plan and implement a strategy to adapt to pending environmental change.
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Catellazzi, Marie. "Spatio-temporal modelling of crop-existence in European argriculture landscapes." Thesis, Cranfield University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501005.
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Feng, Lingbing. "Essays on spatio-temporal data analysis : imputation, modelling and prediction." Phd thesis, Canberra, ACT : The Australian National University, 2015. http://hdl.handle.net/1885/149790.
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Ridefelt, Hanna. "Spatial and Temporal Variations of Solifluction and Related Environmental Parameters in the Abisko Mountains, Northern Sweden." Doctoral thesis, Uppsala universitet, Institutionen för geovetenskaper, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-101419.
Full textAbstract:
This thesis presents an assessment of the variation in solifluction occurrence, morphometry and movement rates in the Abisko region, northern Sweden. Variations in movement rates are analyzed both on a regional and local scale. The main methodological contributions of this thesis have been to provide new techniques of analyzing spatial and temporal variations of solifluction in order to detect long term temporal trends and to regionalize the variations in movement rates. The spatial analysis is achieved by using a combination of field measurements, GIS and remote sensing techniques and statistical analysis. The results are presented in six papers, focusing on the morphometry of solifluction landforms (paper I), the occurrence of permafrost (paper II), the spatial and temporal variations of lobe front movement rates using aerial photographs (paper III), the temporal, regional and local spatial variations in movement rates (paper IV – VI) and statistical modelling of the occurrence of solifluction landforms and calculation of geomorphic work (paper V and VI). The results show that, on a regional scale, vegetation patterns are a major control on the occurrence of turf-banked solifluction landforms, with high NDVI-values (vegetation) associated with the presence of forms. Elevation is also a major control on a regional scale with a decrease in lobe dimensions and movement rates with increased elevation. High soil moisture values are associated with larger landforms and increased movement rates. Movement rates are generally higher in the western part of the region and appear to increase with higher MAAT. Equally, geomorphic work is greatest in the western part of the region. The important controls on a local scale vary from site to site, but include vegetation, slope angle and soil moisture. The photo analysis indicates that annual movement rates of lobe fronts in Kärkevagge and Låktatjåkka valley over the period 1959-2000 ranges from not-detectable to 63mm/yr. The permafrost model shows probabilities >0.8 for permafrost at elevations above 1300 m a.s.l. in the western part of the region, decreasing to altitudes over 850 m a.s.l. in the eastern part of the region. Calculated geomorphic work suggests that solifluction is a significant denudational agent in the sub-Arctic mountains of northern Sweden, but less so than previously estimated.
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Wang, Yujiang. "Multi-scale modelling of epileptic seizure rhythms as spatio-temporal patterns." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/multiscale-modelling-of-epileptic-seizure-rhythms-as-spatiotemporal-patterns(baad4a1e-fa22-47c2-84af-1c26b9399148).html.
Full textAbstract:
Epileptic seizures are characterised by an onset of abnormal brain activity that evolves in space and time, which ultimately returns to normal background activity. For different types of seizures, the abnormal activity can be vastly different both in duration, electrographic morphology and spatial extent. Mechanistic understanding of the different seizure dynamics (spatially, as well as temporally) is crucial for the advancement and improvement of clinical treatment. To gain a deeper mechanistic insight into different seizure dynamics, mathematical models of brain processes were developed in this thesis. These models are used to explain electrographic seizure dynamics in their temporal, as well as their spatio-temporal evolution. Our studies show that the temporal evolution of seizure dynamics can be understood in terms of prototypic waveforms, which in turn can be represented in terms of three neural population processes. Such a minimal framework lends itself to a detailed phase space analysis, which elucidates seizure waveforms and seizure transitions as topological properties of the phase space. Based on the phase space considerations we show how during spike-wave seizures, single-pulse stimuli can have more complex effects than previously thought. In terms of the spatio-temporal dynamics of seizures, mechanisms for focal seizure onset and propagation are investigated in a model cortical sheet of coupled, discretised columns. The coupling followed nearest-neighbour, as well as realistic mesoscopic cortical connectivities. Different possible causes (e.g. spatial heterogeneities) of seizure generation, as well as different seizure spreading patterns (via different networks) have been investigated. We conclude that focal seizure onset can be due to global (e.g. whole-brain level) causes, global conditions & local triggers, and local (e.g. cortical column level) causes. Clinically relevant predictions from this work include the suggestion of a specific stimulation protocol in spike-wave seizures that incorporates phase space information; and the suggestion of using microscopic cortical incisions to disrupt the integrity of abnormal cortical tissue in order to prevent focal seizure onset. In conclusion, multi-scale computational modelling of seizure dynamics is proposed as an important tool to link theoretical understanding, experimental results, and patient-specific clinical data.
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Castellazzi, M. S. "Spatio-temporal modelling of crop co-existence in European agricultural landscapes." Thesis, Cranfield University, 2007. http://dspace.lib.cranfield.ac.uk/handle/1826/3747.
Full textAbstract:
The environmental risk of growing genetically modified (GM) crops and particularly the spreading of GM genes to related non-GM crops is currently a concern in European agriculture. Because the risks of contamination are linked to the spatial and temporal arrangements of crops within the landscape, scenarios of crop arrangement are required to investigate the risks and potential coexistence measures. However, until recently, only manual methods were available to create scenarios. This thesis aims to provide a flexible referenced tool to create such scenarios. The model, called LandSFACTS, is a scientific research tool which allocates crops into fields, to meet user-defined crop spatio-temporal arrangements, using an empirical and statistical approach. The control of the crop arrangements is divided into two main sections: (i) the temporal arrangement of crops: encompassing crop rotations as transition matrices (specifically-developed methodology), temporal constraints (return period of crops, forbidden crop sequences), initial crops in fields regulated by temporal patterns (specifically-developed statistical analyses) and yearly crop proportions; and (ii) the spatial arrangements of crops: encompassing possible crops in fields, crop rotation in fields regulated by spatial patterns (specifically-developed statistical analyses), and spatial constraints (separation distances between crops). The limitations imposed by the model include the size of the smallest spatial and temporal unit: only one crop is allocated per field and per year. The model has been designed to be used by researchers with agronomic knowledge of the landscape. An assessment of the model did not lead to the detection of any significant flaws and therefore the model is considered valid for the stated specifications. Following this evaluation, the model is being used to fill incomplete datasets, build up and compare scenarios of crop allocations. Within the GM coexistence context, the model could provide useful support to investigate the impact of crop arrangement and potential coexistence measures on the risk of GM contamination of crops. More informed advice could therefore be provided to decision makers on the feasibility and efficiency of coexistence measures for GM cultivation.