Spatiotemporal Modeling Approach Research Articles

Spatiotemporal Surface-Groundwater Interaction Simulation in South Florida

South Florida ecosystem is dictated by a large wetland, karst hydrogeology and extended coastal boundary with the Atlantic Ocean. The risks related to the ecosystem include: disruption of groundwater flow as a result of frequent sinkhole formation; flooding in urban areas as a result of the shallow water table; saltwater intrusion from the ocean; and excessive nutrient load to surficial water bodies and subsequently eutrophication because of the intensive utilization of wetlands for nutrient removal. Attempts to understand eco-hydrological processes primarily focus on extensive monitoring and use of distributed hydrological models. However, the relatively flat nature of the region and also the extended coastal boundary with the ocean, makes watershed-based approaches less realistic. A regional spatiotemporal groundwater level modeling approach was attempted using a Dynamic Factor Analysis (DFA) method. The daily water levels of 13 monitoring well sites from major hydrogeologic regions and different land uses were used to conduct the DFA analysis, and six dynamic factors were identified using minimum Akaike Information Criterion (AIC). Further exploratory analysis to relate the dynamic factors with physically attributable explanatory variables has helped to identify five of the major factors that govern the groundwater dynamics in south Florida. Three of the factors were attributable to the Lake Kissimmee water level in the north, Caloosahatchee River water level in the west, and Hillsboro canal in the east. The other two factors identified were the regional averaged rainfall and soil moisture. The spatiotemporal simulation involved interpolation of the loadings of the dynamic factors using an inverse distance weighted method and convoluting with the dynamic factors. The result has shown a good fit with the maximum RMSE of 0.12 m. Retrieval of rainfall, soil moisture, and surface water level from satellite imagery makes spatiotemporal modeling of the groundwater level achievable.

Spectral Based Spatio-Temporal Modeling for Thermal Crown of Aluminium Alloy Hot Rolling Processes

A spectral based low-dimensional spatio-temporal modeling approach is proposed for thermal crown of work rolling in aluminium alloy rolling processes. Firstly，the Karhunen-Loève (KL) decomposition is used for dimension reduction and time/space separation. The neural networks are used for dynamic modeling. The simulations have demonstrated the effectiveness of the proposed spatio-temporal modeling approach..

Probabilistic PCA-Based Spatiotemporal Multimodeling for Nonlinear Distributed Parameter Processes

Many industrial processes are nonlinear distributed parameter systems (DPSs). Data-based spatiotemporal modeling is required for analysis and control when the first-principles model is unknown. Because a DPS is infinite-dimensional and time–space coupled, a low-order model is necessary for prediction and control in practice. For low-order modeling, traditional principal component analysis (PCA) is often used for dimension reduction and time–space separation. However, it is a linear method and leads to only one set of fixed spatial basis functions. Therefore, it might not be always effective for nonlinear systems. In this study, a spatiotemporal multimodeling approach is proposed for unknown nonlinear DPSs. First, multimodel decomposition is performed, where probabilistic PCA (PPCA) is used to obtain multiple sets of spatial basis functions from the experimental data by maximizing a likelihood function. Using these multiple sets of PCA spatial bases for time–space separation, the high-dimensionality spatiotemporal data can be reduced to multiple sets of low-dimensionality temporal series. Then, multiple low-order neural models can be easily established to model these local dynamics. Finally, the original spatiotemporal dynamics can be reconstructed by multimodel synthesis. Because the proposed spatiotemporal modeling approach involves a multimodeling mechanism, it can achieve better performance than the traditional PCA-based single-modeling for nonlinear DPSs, which is demonstrated by numerical simulations.

Ambient Air Pollution and Cardiac Malformations; A Register-Based Spatio-temporal Analysis

ISEE-0120 Objective: To investigate the association between maternal exposure to ambient air pollution and the occurrence of congenital heart disease (CHD). Methods: This case-control study used registry-based data on all CHD cases arising in the population of the Northeast England (1985–2003). Exposure assessment and risk analysis were performed as two sub-studies. Sub-study I used exposure data on PM10, SO2, NO2, NO, CO, and ozone obtained from six automatic monitors for the period 1993–2003. Each study participant was assigned weekly average of pollutant levels measured by the closest operational monitor to the maternal residential postcode. Sub-study II used data on black smoke (BS) and SO2 obtained from 56 non-automatic monitors for the period 1985–1996. A two-stage spatiotemporal modeling approach was used to predict ambient pollutant levels at maternal residential postcodes for each week of their pregnancy. Controls were matched to cases by year of birth with a control/case ratio of four to one. Single and multiple pollutant logistic regression models were used to quantify the adjusted odds ratios of exposure to air pollution and occurrence of each ICD-10 class of CHD and for the five most frequent individual CHD. Adjustment was performed for the year of birth, socioeconomic status, sex, season of conception, degree of urbanity. In sub-study II, a further analysis was carried out by using 1000 simulated exposure scenarios. Results: In sub-study I, ventricular septal defect and congenital malformations of cardiac septa were found to be associated with exposure to CO and NO. Increased risk of congenital pulmonary stenosis and tetralogy of Fallot were detected due to exposure to CO and NO respectively. Sub-study II detected a weak association between maternal exposure to BS and congenital malformations of cardiac chambers and connections. Conclusion: This study has demonstrated that maternal exposure to ambient air pollution increases the risk of specific CHD.

A spatio-temporal Volterra modeling approach for a class of distributed industrial processes

It is difficult to model a distributed parameter system (DPS) due to the infinite-dimensional time/space nature and unknown nonlinear uncertainties. A low-dimensional and simple nonlinear model is often required for practical applications. In this paper, a spatio-temporal Volterra model is proposed with a series of spatio-temporal kernels for modeling unknown nonlinear DPS. To estimate these kernels, they are expanded onto spatial and temporal bases with unknown coefficients. To reduce the model dimension and parametric complexity in the spatial domain, the Karhunen–Loève (KL) method is used to find the dominant spatial bases. To reduce the parametric complexity in the temporal domain, the Laguerre polynomials are selected as temporal bases. Next, using the Galerkin method, this spatio-temporal modeling becomes a linear regression problem. Then unknown parameters can be easily estimated using the least-squares method in the temporal domain. After the time/space synthesis, the spatio-temporal Volterra model can be constructed. The convergence of parameter estimation can be guaranteed under certain conditions. This model has a low-dimensional and simple nonlinear structure, which is useful for the prediction and control of the DPS. The simulation and experiment demonstrate the effectiveness of the proposed modeling method.

Oscillatory NAD(P)H Waves and Calcium Oscillations in Neutrophils? A Modeling Study of Feasibility

The group of Howard Petty has claimed exotic metabolic wave phenomena together with mutually phase-coupled NAD(P)H- and calcium-oscillations in human neutrophils. At least parts of these phenomena are highly doubtful due to extensive failure of reproducibility by several other groups and hints that unreliable data from the Petty lab are involved in publications concerning circular calcium waves. The aim of our theoretical spatiotemporal modeling approach is to propose a possible and plausible biochemical mechanism which would, in principle, be able to explain metabolic oscillations and wave phenomena in neutrophils. Our modeling suggests the possibility of a calcium-controlled glucose influx as a driving force of metabolic oscillations and a potential role of polarized cell geometry and differential enzyme distribution for various NAD(P)H wave phenomena. The modeling results are supposed to stimulate further controversial discussions of such phenomena and potential mechanisms and experimental efforts to finally clarify the existence and biochemical basis of any kind of temporal and spatiotemporal patterns of calcium signals and metabolic dynamics in human neutrophils. Independent of Petty's observations, they present a general feasibility study of such phenomena in cells.

Spatiotemporal Modeling of Ambient Sulfur Dioxide Concentrations in Rural Western Canada

The provinces of Saskatchewan, Alberta, and British Columbia are major oil- and gas-producing regions in western Canada. With increasing oil and gas production activities, there has been a growing concern of the effect of oil and gas industry emissions on health. Nevertheless, lack of proper tools to estimate the exposure to these emissions has been a hindrance to epidemiological studies and risk assessment. This paper presents a spatiotemporal modeling approach to estimating ambient sulfur dioxide (SO2) levels based on environmental monitoring data (N = 10,295), which were collected at rural sites (591 per month on average) of this region from June 1, 2001 to May 31, 2002. Based on the model, illustrative maps consistently revealed high and low SO2 concentration sub-regions. The sub-regions with elevated SO2 concentrations had increased levels during the winter months from December 2001 to March 2002 and then decreased during the spring of 2002. This statistical modeling approach may help researchers estimate the SO2 levels within the study area for their epidemiological studies or risk assessment.

Geographic risk modeling of childhood cancer relative to county-level crops, hazardous air pollutants and population density characteristics in Texas

BackgroundChildhood cancer has been linked to a variety of environmental factors, including agricultural activities, industrial pollutants and population mixing, but etiologic studies have often been inconclusive or inconsistent when considering specific cancer types. More specific exposure assessments are needed. It would be helpful to optimize future studies to incorporate knowledge of high-risk locations or geographic risk patterns. The objective of this study was to evaluate potential geographic risk patterns in Texas accounting for the possibility that multiple cancers may have similar geographic risks patterns.MethodsA spatio-temporal risk modeling approach was used, whereby 19 childhood cancer types were modeled as potentially correlated within county-years. The standard morbidity ratios were modeled as functions of intensive crop production, intensive release of hazardous air pollutants, population density, and rapid population growth.ResultsThere was supportive evidence for elevated risks for germ cell tumors and "other" gliomas in areas of intense cropping and for hepatic tumors in areas of intense release of hazardous air pollutants. The risk for Hodgkin lymphoma appeared to be reduced in areas of rapidly growing population. Elevated spatial risks included four cancer histotypes, "other" leukemias, Central Nervous System (CNS) embryonal tumors, CNS other gliomas and hepatic tumors with greater than 95% likelihood of elevated risks in at least one county.ConclusionThe Bayesian implementation of the Multivariate Conditional Autoregressive model provided a flexible approach to the spatial modeling of multiple childhood cancer histotypes. The current study identified geographic factors supporting more focused studies of germ cell tumors and "other" gliomas in areas of intense cropping, hepatic cancer near Hazardous Air Pollutant (HAP) release facilities and specific locations with increased risks for CNS embryonal tumors and for "other" leukemias. Further study should be performed to evaluate potentially lower risk for Hodgkin lymphoma and malignant bone tumors in counties with rapidly growing population.

A multi-channel spatio-temporal Hammerstein modeling approach for nonlinear distributed parameter processes

Modeling of distributed parameter processes is a challenging problem because of their complex spatio-temporal nature, nonlinearities and uncertainties. In this study, a spatio-temporal Hammerstein modeling approach is proposed for nonlinear distributed parameter processes. Firstly, the static nonlinear and the distributed dynamical linear parts of the Hammerstein model are expanded onto a set of spatial and temporal basis functions. In order to reduce the parametric complexity, the Karhunen–Loève decomposition is used to find the dominant spatial bases with Laguerre polynomials selected as the temporal bases. Then, using the Galerkin method, the spatio-temporal modeling will be reduced to a traditional temporal modeling problem. Finally, the unknown parameters can be easily estimated using the least squares estimation and the singular value decomposition. In the presence of unmodeled dynamics, a multi-channel modeling framework is proposed to further improve the modeling performance. The convergence of the modeling can be guaranteed under certain conditions. The simulations are presented to show the effectiveness of this modeling method and its potential to a wide range of distributed processes.

An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology

There is great potential for the development of many new applications using data on mobile objects and mobile regions. To promote these kinds of applications advanced data management techniques for the representation and analysis of mobility-related data are needed. Together with application experts (behavioural ecologists), we investigate how two novel data management approaches may help. We focus on a case study concerning the analysis of fauna behaviour, in particular crested porcupines, which represents a typical example of mobile object monitoring. The first technique we experiment with is a recently developed conceptual spatio-temporal data modelling approach, MADS. This is used to model the schema of the database suited to our case study. Relying on this first outcome a subset of the problem is represented in the logical language MuTACLP. This allows us to formalise and solve the queries which enable the behavioural ecologists to derive crested porcupines behaviour from the raw data on animal movements. Finally, we investigate the support from a commercial Geographic Information System (GIS) for the analysis of spatio-temporal data. We present a way to integrate MuTACLP and a GIS, combining the advantages of GIS technology and the expressive power of MuTACLP.

A Spatio‐Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract. Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio‐temporal process with many sources of variation, both biological and technical. We present a spatio‐temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.

Aging changes and gender differences in response to median nerve stimulation measured with MEG

Objective The current study uses magnetoencephalography (MEG) to characterize age-related changes and gender differences in the amplitudes and timing of cortical sources evoked by median nerve stimulation. Methods Thirty-four healthy subjects from two age groups: 20–29 and >64 years of age were examined. After measuring the MEG responses, we modeled the data using a spatio-temporal multi-dipole modeling approach to determine the source locations and their associated timecourses. Results We found early, large amplitude responses in the elderly in primary somatosensory (∼20 ms) and pre-central sulcus timecourses (∼22 ms) and lower amplitude responses in the elderly later in primary somatosensory (∼32 ms) and contralateral secondary somatosensory timecourses (∼90 ms). In addition, females had larger peak amplitude responses than males in the contralateral secondary somatosensory timecourse (∼28 and 51 ms). Conclusions These results show that the median nerve stimulation paradigm provides considerable sensitivity to age- and gender-related differences. The results are consistent with the theory that increased amplitudes identified in the elderly may be associated with decreased inhibition. Significance The results emphasize that an examination of two discrete age groups, collapsed across gender, cannot provide a complete understanding of the fundamental changes that occur in the brain across the lifetime.

Bayesian spatio-temporal modelling of national milk-recording data of seasonal-calving New Zealand dairy herds

A spatio-temporal analysis was undertaken with the aim of identifying the dynamics of herd mean individual cow SCCs (MICSCC) in seasonally calving New Zealand dairy herds. Two datasets were extracted from the Livestock Improvement Corporation's extensive national dairy recording database: (1) milk-recording data aggregated at the herd-level and (2) sales questionnaire data containing information on the size, location and infrastructure of each farm. A Bayesian spatio-temporal modelling approach was applied to the analysis. The data were aggregated by 10 km 2 grid cells and linear regression models were developed with spatially structured and unstructured random effects, a linear temporal trend random effect and spatial-temporal interactions for log-transformed median MISCC (ln(median MISCC)). Significant associations were found between ln(median MISCC) and milk yield, milk fat, milk protein, farm area and number of cups in the dairy. This led us to suggest that SCCs should be adjusted for volume and constituents prior to determining a threshold MISCC for identification of subclinical mastitis (SCM) problem herds. Part, or all, of the temporal trend in MISCC in the spatio-temporal model was accounted for by inclusion of yield and milk constituents as independent variables. This supports the hypothesis of a dilution effect with potential consequences for misdiagnosis of SCM, particularly in late lactation. Unmeasured covariates were similarly likely to be spatially structured and unstructured.

Modeling Dynamic Polygon Objects in Space and Time: A New Graph-based Technique

The analysis of dynamic spatial systems requires an explicit spatio-temporal data model and spatio-temporal analysis tools. Event-based models have been developed to analyze discrete change in continuous and feature-based spatial data. In this paper, a spatio-temporal graph model is described that supports the analysis of continuous change in feature-based polygon spatial data. The spatio-temporal graph edges, called temporal links, track changes in polygon topology through space and time. The model also introduces the concept of a spatial-interaction region that extends a model's focus beyond short-term local events to encompass long-term regional events. The structure of the spatio-temporal graph is used to classify these events into five types of local polygon events and two types of spatial-interaction region events. To illustrate its utility, the model is applied to the ecological question of how patch size influences longevity in underwater plant communities in Chesapeake Bay, USA. Both a short-term local analysis and a longer-term regional analysis showed that patches of plants, or groups of patches, larger than one to two hectares in size were more likely to persist than smaller patches or groups of patches. Overall, the spatio-temporal graph model approach appears applicable to a variety of spatio-temporal questions.

Dipole modelling of eye activity and its application to the removal of eye artefacts from the EEG and MEG

The spatio-temporal dipole model approach has been used to identify the difference dipoles arising from changes in the ocular dipoles due to eye movements and blinks. Based on these results a method has been developed to remove eye artefacts from electrical or magnetic data. The method avoids distortions due to the head model by determining the spatial distribution of the signals from the eyes empirically. Using simultaneous modelling of the EEG or MEG activity with dipole sources distributed within the head together with the empirically determined spatial eye components, the eye activity can be estimated and removed from the EEG or MEG. This greatly reduces the distortion to the topography that is a concomitant of previous eye artefact correction methods. The advantages of the method are illustrated using simulated and real electrical data.