Visual Discovery of Synchronisation in Weather Data at Multiple Temporal Resolutions

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

doi:10.1179/1743277413y.0000000067

Cited by 12 publications

(13 citation statements)

References 13 publications

(19 reference statements)

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“…When applying SOMs for spatiotemporal analyses, the data used for training and mapping needs to be considered in a dual fashion: from a spatial perspective and from a temporal perspective [ 22 , 23 ]. A data organisation of the type space over time (SxT) allows the detection of spatial units (medical districts) that show similar behaviour over time; that are synchronized.…”

Section: Methodsmentioning

confidence: 99%

Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns

Augustijn

Zurita-Milla

2013

Int J Health Geogr

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BackgroundSelf-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon’s projection.MethodsSOMs were applied to identify synchrony between spatial locations, to group epidemic waves based on similarity of diffusion pattern and to construct sequence of maps of synoptic states. The Sammon’s projection was used to created diffusion trajectories from the SOM output. These methods were demonstrated with a dataset that reports Measles outbreaks that took place in Iceland in the period 1946–1970. The dataset reports the number of Measles cases per month in 50 medical districts.ResultsBoth stable and incidental synchronisation between medical districts were identified as well as two distinct groups of epidemic waves, a uniformly structured fast developing group and a multiform slow developing group. Diffusion trajectories for the fast developing group indicate a typical diffusion pattern from Reykjavik to the northern and eastern parts of the island. For the other group, diffusion trajectories are heterogeneous, deviating from the Reykjavik pattern.ConclusionsThis study demonstrates the applicability of SOMs (combined with Sammon’s Projection and GIS) in spatiotemporal diffusion analyses. It shows how to visualise diffusion patterns to identify (dis)similarity between individual waves and between individual waves and an overall time-series performing integrated analysis of synchrony and diffusion trajectories.

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Section: Methodsmentioning

confidence: 99%

Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns

Augustijn

Zurita-Milla

2013

Int J Health Geogr

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“…In 2011, Coltekin et al [27] first proposed Modifiable Temporal Unit Problem (MTUP) and defined it in an analogy to MAUP with temporal resolution as one essential aspect. Afterward, a few studies have analyzed the effects of temporal resolutions on the explored patterns [22,23,[28][29][30]. Among them, Cheng and Adepeju [22] examined the temporal resolution effects on the detected spatio-temporal clusters in point data.…”

Section: Of 13mentioning

confidence: 99%

Tri-Clustering Based Exploration of Temporal Resolution Impacts on Spatio-Temporal Clusters in Geo-Referenced Time Series

Zheng

2020

IJGI

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Unprecedented amounts of spatio-temporal data instigates an urgent need for patterns exploration in it. Clustering analysis is useful in extracting patterns from big data by grouping similar data elements into clusters. Compared with one-way clustering and co-clustering methods, tri-clustering methods are more capable of exploring complex patterns. However, the explored patterns or clusters could be different due to varying temporal resolutions of input data. This study presents a tri-clustering based method to explore the impacts of different temporal resolutions on spatio-temporal clusters identified in geo-referenced time series (GTS), one type of spatio-temporal data. Dutch daily temperature data at 28 stations over 20 years was used to illustrate this study. The temperature data at daily, monthly, and yearly resolutions were subjected to the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) to detect spatio-temporal clusters, which were then compared in terms of patterns exhibited, compositions, and changed elements. Results confirm the temporal resolution impacts on the spatio-temporal clusters identified in the Dutch temperature data: most compositions of clusters are varying when changing the temporal resolutions of input data in the GTS. Nevertheless, there is almost no change of elements in certain clusters (12 stations in the northeast of the country; years 1996, 2010) at all temporal resolutions, suggesting them as the “true” clusters in the case study dataset.

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“…In another word, the algorithm is scalable to big size of data tables. Co-clustering has been widely studied for information clustering, pattern structure discovery, et al [16][17][18][19].…”

confidence: 99%

Lossless Compression of Data Tables in Mobile Devices by Using Co-clustering

Han

2016

INT J COMPUT COMMUN

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Data tables have been widely used for storage of a collection of related records in a structured format in many mobile applications. The lossless compression of data tables not only brings benefits for storage, but also reduces network transmission latencies and energy costs in batteries. In this paper, we propose a novel lossless compression approach by combining co-clustering and information coding theory. It reorders table columns and rows simultaneously for shaping homogeneous blocks and further optimizes alignment within a block to expose redundancy, such that standard lossless encoders can significantly improve compression ratios. We tested the approach on a synthetic dataset and ten UCI real-life datasets by using a standard compressor 7Z. The extensive experimental results suggest that compared with the direct table compression without co-clustering and within-block alignment, our approach can boost compression rates at least 21% and up to 68%. The results also show that the compression time cost of the co-clustering approach is linearly proportional to a data table size. In addition, since the inverse transform of co-clustering is just exchange of rows and columns according to recorded indexes, the decompression procedure runs very fast and the decompression time cost is similar to the counterpart without using co-clustering. Thereby, our approach is suitable for lossless compression of data tables in mobile devices with constrained resources.

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Visual Discovery of Synchronisation in Weather Data at Multiple Temporal Resolutions

Cited by 12 publications

References 13 publications

Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns

Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns

Tri-Clustering Based Exploration of Temporal Resolution Impacts on Spatio-Temporal Clusters in Geo-Referenced Time Series

Lossless Compression of Data Tables in Mobile Devices by Using Co-clustering

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