Relevant bibliographies by topics / Spatial and temporal modelling

Academic literature on the topic 'Spatial and temporal modelling'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Spatial and temporal modelling"

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Myers, Donald E. "Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach." Technometrics 62, no. 4 (October 1, 2020): 561. http://dx.doi.org/10.1080/00401706.2020.1783953.

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Pavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.

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Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.
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Arah, J. R. M. "Modelling spatial and temporal variability of denitrification." Biology and Fertility of Soils 9, no. 1 (February 1990): 71–77. http://dx.doi.org/10.1007/bf00335865.

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Jørgensen, Sven Erik. "Quantitative ecology. Spatial and temporal scaling." Ecological Modelling 79, no. 1-3 (May 1995): 288. http://dx.doi.org/10.1016/0304-3800(95)90066-7.

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Wheater, H. S., V. S. Isham, D. R. Cox, R. E. Chandler, A. Kakou, P. J. Northrop, L. Oh, C. Onof, and I. Rodriguez-Iturbe. "Spatial-temporal rainfall fields: modelling and statistical aspects." Hydrology and Earth System Sciences 4, no. 4 (December 31, 2000): 581–601. http://dx.doi.org/10.5194/hess-4-581-2000.

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Abstract. The HYREX experiment has provided a data set unique in the UK, with a dense network of raingauges available for studying the rainfall at a fine local scale and a network of radar stations allowing detailed examination of the spatial and temporal structure of rainfall at larger scales. In this paper, the properties and characteristics of the rainfall process, as measured by the HYREX recording network of rainguages and radars, are studied from a statistical perspective. The results of these analyses are used to develop various models of the rainfall process, for use in hydrological applications. Some typical results of these various modelling exercises are presented. Keywords: Rainfall statistics, rainfall models, hydrological design
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Seifert, L., and D. Chollet. "Modelling spatial–temporal and coordinative parameters in swimming." Journal of Science and Medicine in Sport 12, no. 4 (July 2009): 495–99. http://dx.doi.org/10.1016/j.jsams.2008.03.002.

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Wheater, H. S., R. E. Chandler, C. J. Onof, V. S. Isham, E. Bellone, C. Yang, D. Lekkas, G. Lourmas, and M. L. Segond. "Spatial-temporal rainfall modelling for flood risk estimation." Stochastic Environmental Research and Risk Assessment 19, no. 6 (October 14, 2005): 403–16. http://dx.doi.org/10.1007/s00477-005-0011-8.

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Burton, A., C. G. Kilsby, H. J. Fowler, P. S. P. Cowpertwait, and P. E. O'Connell. "RainSim: A spatial–temporal stochastic rainfall modelling system." Environmental Modelling & Software 23, no. 12 (December 2008): 1356–69. http://dx.doi.org/10.1016/j.envsoft.2008.04.003.

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Mohammadian mosammam, Ali, Elnaz Abbasi, and Jorge Mateu. "Bayesian Approach for Modelling Spatial–Temporal Crime Data." Journal of Statistical Sciences 16, no. 2 (March 1, 2023): 435–48. http://dx.doi.org/10.52547/jss.16.2.435.

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BAKAR, K. SHUVO, and PHILIP KOKIC. "Bayesian Gaussian models for point referenced spatial and spatio-temporal data." Journal of Statistical Research 51, no. 1 (August 24, 2017): 17–40. http://dx.doi.org/10.47302/jsr.2017510102.

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When data is correlated both spatially and temporally, spatial and spatio-temporal modelling is useful for meaningful interpretation of the parameters of the covariates and for reliable predictions. In this paper we discuss some modelling strategies for point referenced spatial and spatio-temporal data. We describe Gaussian models in this context and use Bayesian hierarchical approaches for model based inference and predictions through the Markov chain Monte Carlo (MCMC) algorithm. Yearly average precipitation data from Western Australia is used to illustrate the models.
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Dissertations / Theses on the topic "Spatial and temporal modelling"

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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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Books on the topic "Spatial and temporal modelling"

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Location, transport and land-use: Modelling spatial-temporal information. Berlin: Springer, 2005.

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Guignard, Fabian. On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95231-0.

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Watts, S. Contributions to the modelling of the temporal and spatial statistics of radar sea clutter and the establishment of new detection performance limits. Birmingham: University of Birmingham, 1987.

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Toxopeus, A. G. ISM: An interactive spatial and temporal modelling system as a tool in ecosystem management : with two case studies : Cibodas Biosphere Reserve, West Java, Indonesia, Amboseli Biosphere Reserve, Kajiado District, Central-Southern Kenya. Enschede, the Netherlands: ITC, 1996.

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Zoidis, Georgios. Temporal data modelling. Manchester: UMIST, 1997.

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Oliviero, Stock, ed. Spatial and temporal reasoning. Dordrecht: Kluwer Academic Publishers, 1997.

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Stock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.

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Stock, Oliviero, ed. Spatial and Temporal Reasoning. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-0-585-28322-7.

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Roddick, John F., and Kathleen Hornsby, eds. Temporal, Spatial, and Spatio-Temporal Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3.

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Andrew, Lawson, and Denison David G. T, eds. Spatial cluster modelling. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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Book chapters on the topic "Spatial and temporal modelling"

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Thériault, Marius, Christophe Claramunt, Anne-Marie Séguin, and Paul Villeneuve. "Temporal GIS and Statistical Modelling of Personal Lifelines." In Advances in Spatial Data Handling, 433–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56094-1_32.

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Hashimoto, Hideo. "Re-exports in Spatial and Temporal Price and Allocation Models." In Recent Advances in Spatial Equilibrium Modelling, 79–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80080-1_3.

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Burt, T. P. "Infiltration for Soil Erosion Models: Some Temporal and Spatial Complications." In Modelling Soil Erosion by Water, 213–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58913-3_16.

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de Tré, Guy, Rita de Caluwe, Jörg Verstraete, and Axel Hallez. "The Applicability of Generalized Constraints in Spatio-Temporal Database Modelling and Querying." In Spatio-Temporal Databases, 127–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09968-1_7.

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Díaz Mercado, Vitali. "Spatial drought tracking development." In Spatio-Temporal Characterisation of Drought: Data Analytics, Modelling, Tracking, Impact and Prediction, 79–104. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003279655-9.

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Matthias, Volker, Jan Arndt, Armin Aulinger, Johannes Bieser, and Markus Quante. "Modelling the Temporal and Spatial Allocation of Emission Data." In Springer Proceedings in Complexity, 193–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22055-6_30.

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Saha, Suman, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, and Fabio Cuzzolin. "Spatio-Temporal Action Instance Segmentation and Localisation." In Modelling Human Motion, 141–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46732-6_8.

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Ebel, Adolf, Rainer Friedrich, and Henning Rodhe. "GENEMIS: Assessment, Improvement, and Temporal and Spatial Disaggregation of European Emission Data." In Tropospheric Modelling and Emission Estimation, 181–214. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03470-5_6.

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Keskin, Sinan, and Adnan Yazıcı. "Modelling and Designing Spatial and Temporal Big Data for Analytics." In Communications in Computer and Information Science, 104–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00840-6_12.

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van Oijen, Marcel. "Spatio-Temporal Modelling and Adaptive Sampling." In Bayesian Compendium, 169–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0_23.

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Conference papers on the topic "Spatial and temporal modelling"

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"Maximal autocorrelation factors for function-valued spatial/temporal data." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.a3.hooker.

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Pokrajac, D., and Z. Obradovic. "Improved Spatial-Temporal Forecasting through Modelling of Spatial Residuals in Recent History." In Proceedings of the 2001 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2001. http://dx.doi.org/10.1137/1.9781611972719.9.

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Li, Jiahao, Bin Li, and Yan Lu. "Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3547845.

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Touzani, Mounir, and Christophe Ponsard. "Towards Modelling and Analysis of Spatial and Temporal Requirements." In 2016 IEEE 24th International Requirements Engineering Conference (RE). IEEE, 2016. http://dx.doi.org/10.1109/re.2016.60.

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Matzarakis, Andreas, Marcel Gangwisch, and Tim Herbert. "Modelling in Human Biometeorology: Spatial-Temporal Analysis of Thermal Indices." In The 4th International Electronic Conference on Atmospheric Sciences. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/ecas2021-10297.

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"Modelling the Spatial and Temporal Resolution of a Sensor Observation." In GI_Forum 2013 - Creating the GISociety. Vienna: Austrian Academy of Sciences Press, 2013. http://dx.doi.org/10.1553/giscience2013s71.

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François Marquant, Julien, Akomeno Orowo Omu, Kristina Orehounig, Ralph Evins, and Jan Carmeliet. "Application of Spatial-Temporal Clustering to Facilitate Energy System Modelling." In 2015 Building Simulation Conference. IBPSA, 2015. http://dx.doi.org/10.26868/25222708.2015.2862.

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Bai, Mengjiong. "Detection of Micro-expression Recognition Based on Spatio-Temporal Modelling and Spatial Attention." In ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3382507.3421160.

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"Temporal and spatial variation of precipitation in Nansha Islands, South China Sea." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.a4.yao.

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"Aspect-controlled spatial and temporal soil moisture patterns across three different latitudes." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.k6.srivastava.

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Reports on the topic "Spatial and temporal modelling"

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ALMODARESI, S. A., and Ali BOLOOR. A mathematical modelling for spatio temporal substitution base on Ergodic theorem. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0026.

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Santos, Jr, and Eugene. Modelling Temporal Abductive Explanation. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada263096.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Dasberg, Shmuel, Jan W. Hopmans, Larry J. Schwankl, and Dani Or. Drip Irrigation Management by TDR Monitoring of Soil Water and Solute Distribution. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568095.bard.

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Drip irrigation has the potential of high water use efficiency, but actual water measurement is difficult because of the limited wetted volume. Two long-term experiments in orchards in Israel and in California and several field crop studies supported by this project have demonstrated the feasibility of precise monitoring of soil water distribution for drip irrigation in spite of the limited soil wetting. Time Domain Reflectometry (TDR) enables in situ measurement of soil water content of well defined small volumes. Several approaches were tried in monitoring the soil water balance in the field during drip irrigation. These also facilitated the estimation of water uptake: 1. The use of multilevel moisture probe TDR system. This approach proved to be of limited value because of the extremely small diameter of measurement. 2. The placement of 20 cm long TDR probes at predetermined distances from the drippers in citrus orchards. 3. Heavy instrumentation with neutron scattering access tubes and tensiometers of a single drip irrigated almond tree. 4. High resolution spatial and temporal measurements (0.1m x 0.1m grid) of water content by TDR in corn irrigated by surface and subsurface drip. The latter approach was accompanied by parametric modelling of water uptake intensity patterns by corn roots and superimposed with analytical solutions for water flow from point and line sources. All this lead to general and physically based suggestions for the placement of soil water sensors for scheduling drip irrigation.
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Williams, H. Chapter 2: Temporal and spatial divisions. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/205246.

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Griffiths, Hugh. Bistatic Denial Using Spatial-Temporal Coding. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada387730.

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Mainali, Janardan. Spatial Statistical Approaches to Water Quality Modelling. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7383.

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Swinney, H. L. Complex temporal and spatial patterns in nonequilibrium systems. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/5053202.

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Mobley, Curtis D., and Robert A. Maffione. Spatial and Temporal Measurements of Benthic Optical Properties. Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada362432.

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Maffione, Robert A. Spatial and Temporal Measurements of Benthic Optical Properties. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada627748.

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