scholarly journals Multivariate Kalman Filtering for Spatio-Temporal Processes

2021 ◽  
Author(s):  
Guillermo Ferreira ◽  
Jorge Mateu ◽  
Emilio Porcu ◽  
Alfredo Alegría
Keyword(s):  
Kalman Filtering ◽  
State Space Models ◽  
Statistical Efficiency ◽  
Wold Decomposition ◽  
Simulation Experiments ◽  
Central Chile ◽  
South Central ◽  
Spatial Correlation Structure ◽  
Temporal Process ◽  
Spatio Temporal

Abstract An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models. In this context, a multivariate spatio-temporal process is represented through the well-known Wold decomposition. Such an approach allows for an easy implementation of the Kalman filter to estimate linear temporal processes exhibiting both short and long range dependencies, together with a spatial correlation structure. We illustrate, through simulation experiments, that our method offers a good balance between statistical efficiency and computational complexity. Finally, we apply the method for the analysis of a bivariate dataset on average daily temperatures and maximum daily solar radiations from 21 meteorological stations located in a portion of south-central Chile.

scholarly journals Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration

2021 ◽  
Vol 13 (11) ◽  
pp. 2022
Author(s):  
Mario Lillo-Saavedra ◽  
Viviana Gavilán ◽  
Angel García-Pedrero ◽  
Consuelo Gonzalo-Martín ◽  
Felipe de la Hoz ◽  
...  
Keyword(s):  
Supply And Demand ◽  
Landsat 8 ◽  
Multiple Sources ◽  
Central Chile ◽  
South Central ◽  
Ex Post ◽  
Source Data ◽  
Spatio Temporal ◽  
The Difference

In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed.

The Representation and Organizational of Geographical Spatio-temporal Process Case

2010 ◽  
Vol 11 (6) ◽  
pp. 845-853
Author(s):  
Xinzhong YANG ◽  
Yunyan DU ◽  
Fenzhen SU ◽  
Min JI ◽  
Lijing WANG
Keyword(s):  
Temporal Process ◽  
Spatio Temporal

THE NEW RADIOCARBON DATASET FOR TRILITH MONUMENTS OF SOUTHEASTERN ARABIA

Radiocarbon ◽  
2020 ◽  
pp. 1-11
Author(s):  
R Garba ◽  
P Demján ◽  
I Svetlik ◽  
D Dreslerová
Keyword(s):  
Iron Age ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Coastal Plains ◽  
Spatial And Temporal Patterns ◽  
Two Phase ◽  
Stone Monuments ◽  
South Central ◽  
Geographical Expansion ◽  
Spatio Temporal

ABSTRACT Triliths are megalithic monuments scattered across the coastal plains of southern and southeastern Arabia. They consist of aligned standing stones with a parallel row of large hearths and form a space, the meaning of which is undoubtedly significant but nonetheless still unknown. This paper presents a new radiocarbon (14C) dataset acquired during the two field seasons 2018–2019 of the TSMO (Trilith Stone Monuments of Oman) project which investigated the spatial and temporal patterns of the triliths. The excavation and sampling of trilith hearths across Oman yielded a dataset of 30 new 14C dates, extending the use of trilith monuments to as early as the Iron Age III period (600–300 BC). The earlier dates are linked to two-phase trilith sites in south-central Oman. The three 14C pairs collected from the two-phase trilith sites indicated gaps between the trilith construction phases from 35 to 475 years (2 σ). The preliminary spatio-temporal analysis shows the geographical expansion of populations using trilith monuments during the 5th to 1st century BC and a later pull back in the 1st and 2nd century AD. The new 14C dataset for trilith sites will help towards a better understanding of Iron Age communities in southeastern Arabia.

scholarly journals Improved inference for areal unit count data using graph-based optimisation

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Duncan Lee ◽  
Kitty Meeks ◽  
William Pettersson
Keyword(s):  
Random Effects ◽  
Count Data ◽  
Prior Distribution ◽  
Disease Surveillance ◽  
Sharing Rule ◽  
Markov Random ◽  
Spatial Correlation Structure ◽  
Conditional Autoregressive ◽  
Spatio Temporal ◽  
Special Case

AbstractSpatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

scholarly journals Cardiorespiratory Fitness: Reference on the Six-Minute Walk Test and Oxygen Consumption in Adolescents from South-Central Chile

2021 ◽  
Vol 18 (5) ◽  
pp. 2474
Author(s):  
Jaime Vásquez-Gómez ◽  
Nelson Gatica Salas ◽  
Pedro Jiménez Villarroel ◽  
Luis Rojas-Araya ◽  
Cesar Faundez-Casanova ◽  
...  
Keyword(s):  
Cardiorespiratory Fitness ◽  
Reference Values ◽  
Estimation Error ◽  
Walk Test ◽  
Central Chile ◽  
Six Minute Walk Test ◽  
Body Adiposity ◽  
South Central ◽  
Males And Females ◽  
Minute Walk

Cardiorespiratory fitness (CRF) provides oxygen to the exercising muscles and is related to body adiposity, with cardiometabolic variables. The aim was to develop reference values and a predictive model of CRF in Chilean adolescents. A total of 741 adolescents of both genders (15.7 years old) participated in a basic anthropometry, performance in the six-minute walk test (SMWT), and in Course Navette was measured. Percentiles were determined for the SMWT, for the V̇O2max, and an equation was developed to estimate it. The validity of the equation was checked using distribution assumptions and the Bland–Altman diagram. The STATA v.14 program was used (p < 0.05). The 50th percentile values for males and females in the SMWT and in the V̇O2max of Course Navette were, respectively, from 607 to 690 and from 630 to 641 m, and from 43.9 to 45 and from 37.5 to 31.5 mlO2·kg·min−1, for the range of 13 to 17 years. For its part, the model to predict V̇O2max incorporated gender, heart rate, height, waist-to-height ratio (WHR), and distance in the SMWT (R2 = 0.62; estimation error = 0.38 LO2·min−1; p <0.001). Reference values can guide physical fitness in Chilean adolescents, and V̇O2max was possible to predict from morphofunctional variables.

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