Journal articles: '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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Baker, Leanne F., Kyle J. Artym, and Heidi K. Swanson. "Optimal sampling methods for modelling the occupancy of Arctic grayling (Thymallus arcticus) in the Canadian Barrenlands." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 10 (October 2017): 1564–74. http://dx.doi.org/10.1139/cjfas-2016-0429.

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In occupancy models, imperfect detectability of animals is usually corrected for by using temporally repeated surveys to estimate probability of detection. Substituting spatial replicates for temporal replicates could be an advantageous sampling strategy in remote Arctic regions, but may lead to serious violations of model assumptions. Using a case study of site occupancy of adfluvial young-of-year Arctic grayling (Thymallus arcticus) in Barrenland tundra streams, we assessed the reliability and efficiency of alternative sampling strategies: (i) randomly distributed versus sequential adjacent spatial replicates; (ii) visual versus electrofishing surveys; and (iii) spatial versus temporal replicates. Sequential, adjacent spatial replicates produced spatially autocorrelated data. Autocorrelation was relieved using randomly distributed spatial replicates, but using these randomly distributed spatial replicates introduced significant error into estimates of the probability of occupancy in streams. Models designed for spatially autocorrelated data could minimize this bias. Visual and electrofishing surveys produced comparable probabilities of detection. Spatially replicated surveys performed better than temporal replicates. The easiest and relatively most cost-effective sampling methods performed as well as, or better than, the more established, expensive, and logistically difficult alternatives for occupancy estimation.

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Schmitz, Oliver, Elga Salvadore, Lien Poelmans, Johannes van der Kwast, and Derek Karssenberg. "A framework to resolve spatio-temporal misalignment in component-based modelling." Journal of Hydroinformatics 16, no. 4 (December 6, 2013): 850–71. http://dx.doi.org/10.2166/hydro.2013.180.

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Process-based spatio-temporal component models simulate real world processes, using encapsulated process representations that operate at individual spatial and temporal discretisations. These component models act as building blocks in the construction of multi-disciplinary, multi-scale integrated models. Coupling these independent component models, however, involves aggregation or disaggregation of the exchanged variables at model runtime, since each of the component models exposes potentially different spatial and temporal discretisations. Although conceptual methodologies for spatial and temporal scaling are available, dedicated tools that assist modellers to implement dynamic spatial and temporal scaling operations are rare. We present the accumulator, a programmable general-purpose model building block executing custom scaling operations at model runtime. We therefore characterise runtime information of input and output variables required for the implementation of scaling operations between component models with different discretisations. The accumulator is a component of an integrated modelling framework and can be completed by the modeller with custom operations for spatial and temporal scaling. To illustrate the applicability of the accumulators an integrated model is developed that couples an existing land use change model and hydrological component models at different spatial and temporal scales. The accumulators as building blocks allow modellers to construct multi-scale integrated models in a flexible manner.

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Steenbeek, Jeroen, Joe Buszowski, David Chagaris, Villy Christensen, Marta Coll, Elizabeth A. Fulton, Stelios Katsanevakis, et al. "Making spatial-temporal marine ecosystem modelling better – A perspective." Environmental Modelling & Software 145 (November 2021): 105209. http://dx.doi.org/10.1016/j.envsoft.2021.105209.

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Khachatryan, Kh A., A. Zh Narimanyan, and A. Kh Khachatryan. "On mathematical modelling of temporal spatial spread of epidemics." Mathematical Modelling of Natural Phenomena 15 (2020): 6. http://dx.doi.org/10.1051/mmnp/2019056.

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In the present work a generalized epidemic model containing a system of integral-differential equations is described. Using different transformations the system is reduced to a single nonlinear multidimensional integral equation. For the obtained equation the existence and uniqueness results are proved. Based on theoretical convergence results several application examples are presented with corresponding numerical results.

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Barndorff-Nielsen, O. E., and J. Schmiegel. "Lévy-based spatial-temporal modelling, with applications to turbulence." Russian Mathematical Surveys 59, no. 1 (February 28, 2004): 65–90. http://dx.doi.org/10.1070/rm2004v059n01abeh000701.

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Singh, Murari, and Michael J. Jones. "Modelling spatial–temporal covariance structures in monocropping barley trials." Journal of Applied Statistics 35, no. 3 (March 2008): 321–33. http://dx.doi.org/10.1080/02664760701832992.

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Lam, A., D. Karssenberg, B. J. J. M. van den Hurk, and M. F. P. Bierkens. "Spatial and temporal connections in groundwater contribution to evaporation." Hydrology and Earth System Sciences Discussions 8, no. 1 (February 1, 2011): 1541–68. http://dx.doi.org/10.5194/hessd-8-1541-2011.

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Abstract. In climate models, lateral terrestrial water fluxes are usually neglected. We estimated the contribution of vertical and lateral groundwater fluxes to the land surface water budget at a subcontinental scale, by modelling convergence of groundwater and surfacewater fluxes. We present a hydrological model of the entire Danube Basin at 5 km resolution, and use it to show the importance of groundwater for the surface climate. The contribution of groundwater to evaporation is significant, and can be upwards of 30% in summer. We show that this contribution is local by presenting the groundwater travel times and the magnitude of groundwater convergence. Throughout the Danube Basin the lateral fluxes of groundwater are negligible when modelling at this scale and resolution. Also, it is shown that the contribution of groundwater to evaporation has important temporal characteristics. An experiment with the same model shows that a wet episode influences groundwaters contribution to summer evaporation for several years afterwards. This indicates that modelling groundwater flow has the potential to augment the multi-year memory of climate models.

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Sánchez-Balseca, Joseph, and Agustií Pérez-Foguet. "Compositional Spatio-Temporal PM2.5 Modelling in Wildfires." Atmosphere 12, no. 10 (October 7, 2021): 1309. http://dx.doi.org/10.3390/atmos12101309.

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Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

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Hock, Regine. "Glacier melt: a review of processes and their modelling." Progress in Physical Geography: Earth and Environment 29, no. 3 (September 2005): 362–91. http://dx.doi.org/10.1191/0309133305pp453ra.

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Modelling ice and snow melt is of large practical and scientific interest, including issues such as water resource management, avalanche forecasting, glacier dynamics, hydrology and hydrochemistry, as well as the response of glaciers to climate change. During the last few decades, a large variety of melt models have been developed, ranging from simple temperature-index to sophisticated energy-balance models. There is a recent trend towards modelling with both high temporal and spatial resolution, the latter accomplished by fully distributed models. This review discusses the relevant processes at the surface-atmosphere interface, and their representation in melt models. Despite considerable advances in distributed melt modelling there is still a need to refine and develop models with high spatial and temporal resolution based on moderate input data requirements. While modelling of incoming radiation in mountain terrain is relatively accurate, modelling of turbulent fluxes and spatial and temporal variability in albedo constitute major uncertainties in current energy-balance melt models, and thus need further research.

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HAO, BAILIN. ""SPATIAL-TEMPORAL" PATTERNS IN PROKARYOTE GENOMES." International Journal of Bifurcation and Chaos 12, no. 11 (November 2002): 2625–30. http://dx.doi.org/10.1142/s0218127402006096.

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We have developed a simple scheme to visualize the string composition of long DNA sequences and applied it to all available prokaryote complete genomes. Each species has a specific "portrait" and most clearly seen patterns in these portraits are determined by short avoided and under-represented strings. The "spatial" patterns in the space of short strings might reflect some "temporal" events in the evolutionary history.

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Chee, Yung En, Lauchlin Wilkinson, Ann E. Nicholson, Pedro F. Quintana-Ascencio, John E. Fauth, Dianne Hall, Kimberli J. Ponzio, and Libby Rumpff. "Modelling spatial and temporal changes with GIS and Spatial and Dynamic Bayesian Networks." Environmental Modelling & Software 82 (August 2016): 108–20. http://dx.doi.org/10.1016/j.envsoft.2016.04.012.

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Nazia, Nushrat, Zahid Ahmad Butt, Melanie Lyn Bedard, Wang-Choi Tang, Hibah Sehar, and Jane Law. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review." International Journal of Environmental Research and Public Health 19, no. 14 (July 6, 2022): 8267. http://dx.doi.org/10.3390/ijerph19148267.

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The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

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Lichtenwoehrer, Peter, Lore Abart-Heriszt, Florian Kretschmer, Franz Suppan, Gernot Stoeglehner, and Georg Neugebauer. "Evaluating Spatial Interdependencies of Sector Coupling Using Spatiotemporal Modelling." Energies 14, no. 5 (February 25, 2021): 1256. http://dx.doi.org/10.3390/en14051256.

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In light of global warming and the energy turn, sector coupling has gained increasing interest in recent years, from both the scientific community and politics. In the following article it is hypothesized that efficient multifaceted sector coupling solutions depend on detailed spatial and temporal characteristics of energy demand and supply. Hence, spatiotemporal modelling is used as a methodology of integrated spatial and energy planning, in order to determine favourable sector coupling strategies at the local level. A case study evaluation was carried out for both central and decentral renewable energy sources. Considering the high temporal resolutions of energy demand and supply, the results revealed a feasible operation of a district heating network in the central areas of the case study municipalities. Additionally, building integrated solar energy technologies are capable of providing large amount of excess energy that could serve other demand sectors, such as the mobility sector, or could be used for Power-to-X solutions. It is suggested that sector coupling strategies require spatial considerations and high temporal comparisons, in order to be reasonably integrated in spatial and urban planning.

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Cornacchia, Giuliano, and Luca Pappalardo. "A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together." ISPRS International Journal of Geo-Information 10, no. 9 (September 11, 2021): 599. http://dx.doi.org/10.3390/ijgi10090599.

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Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.

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Hamm, N. A. S. "SPATIAL TEMPORAL MODELLING OF PARTICULATE MATTER FOR HEALTH EFFECTS STUDIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (October 14, 2016): 1403–6. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1403-2016.

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Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 &mu;g m&minus;3 and 1.8 &mu;g m&minus;3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.

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Hall, C. A. S., H. Tian, Y. Qi, G. Pontius, and J. Cornell. "Modelling Spatial and Temporal Patterns of Tropical Land Use Change." Journal of Biogeography 22, no. 4/5 (July 1995): 753. http://dx.doi.org/10.2307/2845977.

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Pipe, Leonie Z., and Malcolm J. Grimson. "Spatial-temporal modelling of bacterial colony growth on solid media." Molecular BioSystems 4, no. 3 (2008): 192. http://dx.doi.org/10.1039/b708241j.

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Taib, Che Mohd Imran Che, and Mukminah Darus. "Spatial-Temporal Modelling of Temperature for Pricing Temperature Index Insurance." Asia-Pacific Financial Markets 26, no. 1 (November 13, 2018): 87–106. http://dx.doi.org/10.1007/s10690-018-9259-0.

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Morris, Lindsay R., and Nokuthaba Sibanda. "Pivotal discrepancy measures for Bayesian modelling of spatio-temporal data." Environmental and Ecological Statistics 29, no. 1 (February 12, 2022): 33–53. http://dx.doi.org/10.1007/s10651-022-00529-4.

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AbstractWithin the field of geostatistics, Gaussian processes are a staple for modelling spatial and spatio-temporal data. Statistical literature is rich with estimation methods for the mean and covariance of such processes (in both frequentist and Bayesian contexts). Considerably less attention has been paid to developing goodness-of-fit tests for assessment of model adequacy. Jun et al. (Environmetrics 25(8):584–595, 2014) introduced a statistical test that uses pivotal discrepancy measures to assess goodness-of-fit in the Bayesian context. We present a modification and generalization of their statistical test. The initial method involves spatial partitioning of the data, followed by evaluation of a pivotal discrepancy measure at each posterior draw to obtain a posterior distribution of pivotal statistics. Order statistics from this distribution are used to obtain approximate p-values. Jun et al. (Environmetrics 25(8):584–595, 2014) use arbitrary partitions based on pre-existing spatial boundaries. The partitions are made to be of equal size. Our contribution is two-fold. We use K-means clustering to create the spatial partitions and we generalise Jun et al.’s approach to incorporate unequal partition sizes. Observations from a spatial or spatio-temporal process are partitioned using an appropriate feature vector that incorporates the geographic location of the observations into subsets (not necessarily of the same size). The method’s viability is illustrated in a simulation study, and in an application to hoki (Macruronus novaezelandiae) catch data from a survey of the sub-Antarctic region.

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Henson, Michael A. "Genome-scale modelling of microbial metabolism with temporal and spatial resolution." Biochemical Society Transactions 43, no. 6 (November 27, 2015): 1164–71. http://dx.doi.org/10.1042/bst20150146.

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Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

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Máca, P., and P. Torfs. "The influence of temporal rainfall distribution in the flood runoff modelling." Soil and Water Research 4, Special Issue 2 (March 19, 2010): S102—S110. http://dx.doi.org/10.17221/471-swr.

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The rainfall input is one of the main factors influencing the magnitude of the runoff response during a flood event. Its temporal and spatial distribution significantly contributes to the formation of hydrograph shape, peak discharge and flood volume. A novel approach to the evaluation of the role of the temporal rainfall pattern of hydrograph is presented in this contribution. The methodology shown is based on the coupling of the deterministic event based runoff model with the stochastic rainfall disaggregation model. The rainfall model simulates the hyetograph ensemble, which is the direct input to the calibrated event based runoff model. The event based runoff model calibration is based on the evaluation of real flood events. The rainfall ensemble is simulated according to the preservation of important statistical properties, which are estimated from the real rainfall data inputs. The proposed combination of two simulation techniques enables to generate the hydrograph ensemble upon a single flood event. The evaluation of the temporal rainfall distribution impact on the flood runoff response is performed through the determination of the selected rainfall runoff characteristics of the simulated hydrograph ensemble. The main result confirms the importance of the rainfall volume inputs and its temporal distribution on the flood runoff generation. The methodology shown enables to evaluate the potential of the real flood event to generate the flood event within the conditions of the small catchment scale.

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Pion, Caroline, Robert Leconte, Jean Rousselle, and Sébastien Gagnon. "Modélisation de l'évapotranspiration réelle à l'échelle régionale pour des bassins versants situés dans la forêt boréale." Canadian Journal of Civil Engineering 32, no. 5 (October 1, 2005): 839–52. http://dx.doi.org/10.1139/l05-053.

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It is a known fact that evapotranspiration (ET) varies spatially and temporally and is affected by local and regional factors such as topography, soil properties, and vegetation. As the objective of this study is to model the spatial distribution of actual ET in a boreal ecosystem, a spatially distributed hydrological model employing the Priestley–Taylor approach was used. Spatial and temporal variations of the radiative fluxes at the study site were carefully modelled, as observed fluxes were scarce. Moreover, two modelling scenarios, each based on a different combination of input data sources, were examined. Observed flows were used to calibrate the hydrological model, and acceptable results were obtained. Results show that the modelling of actual ET is affected more by the spatially distributed precipitation than by radiative fluxes. This does not corroborate the experimental results of a previous study carried out over the same area on specific sites. However, the present study differs from the previous one, as the results are obtained at the regional scale.Key words: evapotranspiration, boreal forest, hydrological model, spatial and temporal heterogeneity.

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Yoshimoto, Atsushi, and Patrick Asante. "Inter-Temporal Aggregation for Spatially Explicit Optimal Harvest Scheduling under Area Restrictions." Forest Science 67, no. 5 (September 17, 2021): 587–606. http://dx.doi.org/10.1093/forsci/fxab025.

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Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration.

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Zhong, Sheng-hua, Yan Liu, Feifei Ren, Jinghuan Zhang, and Tongwei Ren. "Video Saliency Detection via Dynamic Consistent Spatio-Temporal Attention Modelling." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 1063–69. http://dx.doi.org/10.1609/aaai.v27i1.8642.

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Human vision system actively seeks salient regions and movements in video sequences to reduce the search effort. Modeling computational visual saliency map provides im-portant information for semantic understanding in many real world applications. In this paper, we propose a novel video saliency detection model for detecting the attended regions that correspond to both interesting objects and dominant motions in video sequences. In spatial saliency map, we in-herit the classical bottom-up spatial saliency map. In tem-poral saliency map, a novel optical flow model is proposed based on the dynamic consistency of motion. The spatial and the temporal saliency maps are constructed and further fused together to create a novel attention model. The pro-posed attention model is evaluated on three video datasets. Empirical validations demonstrate the salient regions de-tected by our dynamic consistent saliency map highlight the interesting objects effectively and efficiency. More im-portantly, the automatically video attended regions detected by proposed attention model are consistent with the ground truth saliency maps of eye movement data.

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Gorman, Arthur D. "Space-time caustics." International Journal of Mathematics and Mathematical Sciences 9, no. 3 (1986): 531–40. http://dx.doi.org/10.1155/s0161171286000662.

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The Lagrange manifold (WKB) formalism enables the determination of the asymptotic series solution of linear differential equations modelling wave propagation in spatially inhomogeneous media at caustic (turning) points. Here the formalism is adapted to determine a class of asymptotic solutions at caustic points for those equations modelling wave propagation in media with both spatial and temporal inhomogeneities. The analogous Schrodinger equation is also considered.

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Malamud, Bruce D., Donald L. Turcotte, and Harold E. Brooks. "Spatial–temporal clustering of tornadoes." Natural Hazards and Earth System Sciences 16, no. 12 (December 21, 2016): 2823–34. http://dx.doi.org/10.5194/nhess-16-2823-2016.

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Abstract. The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial–temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial–temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial–temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial–temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated individual tornadoes with specified supercell thunderstorms. Our analysis of the 3 May 1999 tornado outbreak directly associated linear features in the largely random spatial–temporal analysis with several supercell thunderstorms, which we then confirmed using model scenarios of synthetic tornado outbreaks. We suggest that it may be possible to develop a semi-automated modelling of tornado touchdowns to match the type of observations made on the 3 May 1999 outbreak.

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Guesgen, Hans W., Gérard Ligozat, Jochen Renz, and Rita V. Rodríguez. "Spatial and Temporal Reasoning." Spatial Cognition & Computation 8, no. 1-2 (May 22, 2008): 1–3. http://dx.doi.org/10.1080/13875860801959547.

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Shen, Yuan, Stephen D. Mayhew, Zoe Kourtzi, and Peter Tiňo. "Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models." NeuroImage 84 (January 2014): 657–71. http://dx.doi.org/10.1016/j.neuroimage.2013.09.003.

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Kuria Waitara, Joseph, Gregory Kerich, John Kihoro, and Anne Korir. "Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties." American Journal of Theoretical and Applied Statistics 10, no. 4 (2021): 175. http://dx.doi.org/10.11648/j.ajtas.20211004.11.

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Senior, M. L., H. C. W. L. Williams, and G. Higgs. "Spatial and Temporal Variation of Mortality and Deprivation 2: Statistical Modelling." Environment and Planning A: Economy and Space 30, no. 10 (October 1998): 1815–34. http://dx.doi.org/10.1068/a301815.

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Building on the tabular analyses exemplified in our first paper and widely used in the medical literature, we use generalised linear models to provide a formal, statistical approach to the analysis of mortality and deprivation relationships, and their change over time. Three types of fixed effects model are specified and estimated with the same ward-level data sets for Wales examined in our first paper. They are: Poisson models for analysing mortality and deprivation at a single cross section in time; repeated-measures Poisson models for analysing mortality–deprivation relations, not only at cross sections in time, but also their changes over time; and logit models focusing on temporal changes in mortality–deprivation relationships. Nonlinear effects of deprivation on mortality have been explored by using dummy variables representing deprivation categories to establish the connection between formal statistical models and the tabular approach.

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Hui, Fengming, Bing Xu, Huabing Huang, Qian Yu, and Peng Gong. "Modelling spatial‐temporal change of Poyang Lake using multitemporal Landsat imagery." International Journal of Remote Sensing 29, no. 20 (September 20, 2008): 5767–84. http://dx.doi.org/10.1080/01431160802060912.

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Ji, Xiaoli, Mitsuru Utsugi, Hiroki Shirai, Akira Suzuki, Jinlan He, Satoshi Fujiwara, and Yoshihiro Fukuzaki. "Modelling of spatial-temporal changes of the geomagnetic field in Japan." Earth, Planets and Space 58, no. 6 (June 2006): 757–63. http://dx.doi.org/10.1186/bf03351979.

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Geertman, S., M. Hagoort, and H. Ottens. "Spatial‐temporal specific neighbourhood rules for cellular automata land‐use modelling." International Journal of Geographical Information Science 21, no. 5 (May 2007): 547–68. http://dx.doi.org/10.1080/13658810601064892.

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Danson, F. M., R. P. Armitage, and C. G. Marston. "Spatial and temporal modelling for parasite transmission studies and risk assessment." Parasite 15, no. 3 (September 2008): 463–68. http://dx.doi.org/10.1051/parasite/2008153463.

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Rollins, D. K., D. J. Rollins, and A. D. Jones. "Spatial-Temporal Semi-Empirical Dynamic Modelling of Thermal Gradient CVI Processes." Chemical Engineering Research and Design 85, no. 10 (January 2007): 1390–96. http://dx.doi.org/10.1016/s0263-8762(07)73179-9.

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Kiselev, Andrey V., Vladislav A. Karbovskii, and Sergey V. Kovalchuk. "Agent-based Modelling Using Ensemble Approach with Spatial and Temporal Composition." Procedia Computer Science 80 (2016): 530–41. http://dx.doi.org/10.1016/j.procs.2016.05.333.

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FASTENRATH, M., K. FRISTON, and S. KIEBEL. "Dynamical causal modelling for M/EEG: Spatial and temporal symmetry constraints." NeuroImage 44, no. 1 (January 1, 2009): 154–63. http://dx.doi.org/10.1016/j.neuroimage.2008.07.041.

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Petihakis, George, George Triantafyllou, Icarus J. Allen, Ibrahim Hoteit, and Costas Dounas. "Modelling the spatial and temporal variability of the Cretan Sea ecosystem." Journal of Marine Systems 36, no. 3-4 (October 2002): 173–96. http://dx.doi.org/10.1016/s0924-7963(02)00186-0.

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Xia, Jianhong (Cecilia), Panlop Zeephongsekul, and David Packer. "Spatial and temporal modelling of tourist movements using Semi-Markov processes." Tourism Management 32, no. 4 (August 2011): 844–51. http://dx.doi.org/10.1016/j.tourman.2010.07.009.

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Deng, Minfeng, and George Athanasopoulos. "Modelling Australian domestic and international inbound travel: a spatial–temporal approach." Tourism Management 32, no. 5 (October 2011): 1075–84. http://dx.doi.org/10.1016/j.tourman.2010.09.006.

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Journal articles on the topic 'Spatial and temporal modelling'

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