Advances in spatial functional statistics

Mateu, Jorge; Romano, Elvira

doi:10.1007/s00477-016-1346-z

Cited by 34 publications

(17 citation statements)

References 31 publications

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“…Therefore, unlike classical regionalization approaches, the complete time series is treated as a whole, without losing their dimensionality and temporal dependencies. When such curves are also observed through space, spatial dependence among locations can be made explicit by means of a spatial autocorrelation function (also called variogram) (Giraldo et al, ; Mateu and Romano, ). This approach is known as spatial functional data analysis (sFDA), and the regionalization with sFDA takes into consideration time as well space dependencies (Giraldo et al, ; Romano et al, ).…”

Section: Introductionmentioning

confidence: 99%

Spatial functional data analysis for regionalizing precipitation seasonality and intensity in a sparsely monitored region: Unveiling the spatio‐temporal dependencies of precipitation in Ecuador

Ballari

Giraldo

Campozano

et al. 2018

Intl Journal of Climatology

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The identification of area‐wise homogeneous precipitation regions helps to unveil similar precipitation patterns and amounts, where similar atmospheric processes at diverse temporal scales are likely to occur. However, although scientifically and socially relevant, the regionalization of precipitation is challenging, specially in areas of complex orography and with sparse monitoring. This limits our understanding of complex spatio‐temporal dependencies and hinders any information‐based resource management decision‐making. Gridded satellite precipitation products are useful in this context, even though they contain bias errors. Spatial functional data analysis (sFDA) is a novel technique that considers time as well as space dependencies by means of spatial autocorrelation and complete time functions, one for each spatial point. Therefore, the aim of this study is to evaluate sFDA as a tool to regionalize seasonality and intensity precipitation patterns, having Ecuador as a case study. The Tropical Rainfall Measuring Mission (TRMM 3B43) satellite precipitation is used to create an exhaustive spatial delineation. To the best of our knowledge, this is the first time that a sFDA regionalization approach is performed on gridded satellite precipitation. The complex orography and heat‐driven atmospheric processes in Ecuador's latitude make it a highly non‐trivial case to test the aforementioned technique. As a result, five relevant regions of precipitation seasonality were spatially delineated and temporally characterized. Three of them were zonally oriented, and two meridional‐wise in the coast. In addition, 20 relevant intensity regions across Ecuador were identified specially in regions with sparse monitoring. The regions were related to regional climate processes. However, limitations were found in regions with important orographic precipitation and locally variability patterns, probably due to the shortcomings of TRMM precipitation quantification. After the successful application of hierarchical regionalization using sFDA in a tropical region with sparse monitoring, it is reasonable to conclude that sFDA is a robust method to detect compact and meaningful homogeneous areas.

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Section: Introductionmentioning

confidence: 99%

Spatial functional data analysis for regionalizing precipitation seasonality and intensity in a sparsely monitored region: Unveiling the spatio‐temporal dependencies of precipitation in Ecuador

Ballari

Giraldo

Campozano

et al. 2018

Intl Journal of Climatology

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“…In the logarithmic scale, very small numerical variations in the mg/m 3 unit generate increased numerical variations, thus providing for greater numerical distinction amongst variations and facilitating cluster analysis. This logarithmic scale approach is also discussed in the work of [11] [13] [14]. In another approach, the monthly averages and standardized anomalies (Equation (4)) were calculated.…”

Section: Clusteringmentioning

confidence: 99%

Data Mining of Spatio-Temporal Variability of Chlorophyll-a Concentrations in a Portion of the Western Atlantic with Low Performance Hardware

Paolo¹,

Gouveia²,

Neto³

et al. 2019

JSEA

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The contemporary scientific literature that deals with the dynamics of marine chlorophyll-a concentration is already customarily employing data mining techniques in small geographic areas or regional samples. However, there is little focus on the issue of missing data related to chlorophyll-a concentration estimated by remote sensors. Intending to provide greater scope to the identification of the spatiotemporal distribution patterns of marine chlorophyll-a concentrations, and to improve the reliability of results, this study presents a data mining approach to cluster similar chlorophyll-a concentration behaviors while implementing an iterative spatiotemporal interpolation technique for missing data inference. Although some dynamic behaviors of said concentrations in specific areas are already known by specialists, systematic studies in large geographical areas are still scarce due to the computational complexity involved. For this reason, this study analyzed 18 years of NASA satellite observations in one portion of the Western Atlantic Ocean, totaling more than 60 million records. Additionally, performance tests were carried out in low-cost computer systems to check the accessibility of the proposal implemented for use in computational structures of different sizes. The approach was able to identify patterns with high spatial resolution, accuracy and reliability, rendered in low-cost computers even with large volumes of data, generating new and consistent patterns of spatiotemporal variability. Thus,

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“…Developing such a metric is non-trivial because functional data can manifest itself in a variety of ways, with the data subject to a large degree of variability due to the nature of the spatial component. The emerging characteristics for recently developed methods are mainly related to modelling correlated functional data using spatial structures (geostatistical data, point patterns and areal data) that can be combined with functional data [17]. For this reason, and based on the specific characteristics of the spatial component related to the functional data, the scientific community has focused on developing methods based on suitable measures of distance, or similarity, related to clustering ( [7], [8], [9], [24], [28]), to the definition of depth [1] and to kriging prediction problems [2].…”

Section: Introductionmentioning

confidence: 99%

Optimally weighted L2 distances for spatially dependent functional data

et al. 2020

Self Cite

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In recent years, in many application fields, extracting information from data in the form of functions is of most interest rather than investigating traditional multivariate vectors. Often these functions have complex spatial dependences that need to be accountied for using appropriate statistical analysis. Spatial Functional Statistics presents a fruitful analytics framework for this analysis.The definition of a distance measure between spatially dependent functional data is critical for many functional data analysis tasks such as clustering and classification. For this reason, and based on the specific characteristics of functional data, several distance measures have been proposed in the last few years.In this work we develop a weighted L 2 distance for spatially dependent functional data, with an optimized weight function. Assuming a penalized basis representation for the functional data, we consider weight functions depending also on the spatial location in two different situations: a classical georeferenced spatial structure and a connected network one. The performance of the proposed distances are compared using standard metrics applied to both real and simulated data analysis.

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Advances in spatial functional statistics

Cited by 34 publications

References 31 publications

Spatial functional data analysis for regionalizing precipitation seasonality and intensity in a sparsely monitored region: Unveiling the spatio‐temporal dependencies of precipitation in Ecuador

Spatial functional data analysis for regionalizing precipitation seasonality and intensity in a sparsely monitored region: Unveiling the spatio‐temporal dependencies of precipitation in Ecuador

Data Mining of Spatio-Temporal Variability of Chlorophyll-a Concentrations in a Portion of the Western Atlantic with Low Performance Hardware

Optimally weighted L2 distances for spatially dependent functional data

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