A novel analysis of spring phenological patterns over Europe based on co‐clustering

Wu, Xiaoshan; Zurita-Milla, R.; Kraak, M.J.

doi:10.1002/2015jg003308

Cited by 30 publications

(29 citation statements)

References 50 publications

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“…The SI-x models are widely used to study the timing of plant leafing and its changes in the northern hemisphere (Linkosalo et al 2008;Mehdipoor et al 2016;Wu et al 2016;Belmecheri et al 2017;Hufkens et al 2018). The outputs of the SI-x model, namely the estimated DOY of FL and first flower of indicator plants such as lilac, are used as an official indicator of climate change in the USA (Schwartz et al 2006(Schwartz et al , 2013Crimmins et al 2016).…”

Section: Si-x Lmmentioning

confidence: 99%

Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series

et al. 2019

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Phenological models are widely used to estimate the influence of weather and climate on plant development. The goodness of fit of phenological models often is assessed by considering the root-mean-square error (RMSE) between observed and predicted dates. However, the spatial patterns and temporal trends derived from models with similar RMSE may vary considerably. In this paper, we analyse and compare patterns and trends from a suite of temperature-based phenological models, namely extended spring indices, thermal time and photothermal time models. These models were first calibrated using lilac leaf onset observations for the period 1961-1994. Next, volunteered phenological observations and daily gridded temperature data were used to validate the models. After that, the two most accurate models were used to evaluate the patterns and trends of leaf onset for the conterminous US over the period 2000-2014. Our results show that the RMSEs of extended spring indices and thermal time models are similar and about 2 days lower than those produced by the other models. Yet the dates of leaf out produced by each of the models differ by up to 11 days, and the trends differ by up to a week per decade. The results from the histograms and difference maps show that the statistical significance of these trends strongly depends on the type of model applied. Therefore, further work should focus on the development of metrics that can quantify the difference between patterns and trends derived from spatially explicit phenological models. Such metrics could subsequently be used to validate phenological models in both space and time. Also, such metrics could be used to validate phenological models in both space and time.

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Section: Si-x Lmmentioning

confidence: 99%

Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series

et al. 2019

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“…By this means, BBAC_I enables the analysis of spatial and temporal patterns in a concurrent fashion. For a detailed explanation, refer to Wu, ZuritaMilla et al (2015) and Wu, Zurita-Milla et al (2016).…”

Section: Bbac_imentioning

confidence: 99%

Clustering-Based Approaches To The Exploration of Spatio-Temporal Data

Zurita-Milla

Kraak

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.

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“…timestamp). By simultaneously mapping locations to location-clusters and timestamps to timestamp-clusters in an iterative process, BBAC_I minimizes the loss and identifies the optimal locationtimestamp co-clusters that contain similar attribute values along both dimensions (for a detailed explanation, refer to Wu, ZuritaMilla et al (2015), Wu, Zurita-Milla et al (2016)). …”

Section: Bregman Block Average Co-clustering Algorithm With I-divergementioning

confidence: 99%

A Web-Based Interactive Platform For Co-Clustering Spatio-Temporal Data

Poorthuis

Zurita-Milla

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

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A novel analysis of spring phenological patterns over Europe based on co‐clustering

Cited by 30 publications

References 50 publications

Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series

Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series

Clustering-Based Approaches To The Exploration of Spatio-Temporal Data

A Web-Based Interactive Platform For Co-Clustering Spatio-Temporal Data

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