scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

scholarly journals Coastal change patterns from time series clustering of permanent laser scan data

2021 ◽  
Vol 9 (1) ◽  
pp. 89-103
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithms ◽  
Coastal Areas ◽  
Spatiotemporal Data ◽  
Small Scale ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes

Abstract. Sandy coasts are constantly changing environments governed by complex, interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and to support analysis of geomorphological deformation processes. This novel technique delivers 3-D representations of the coast at hourly temporal and centimetre spatial resolution and allows us to observe small-scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatiotemporal data set is needed. To enable automated data mining, we extract time series of surface elevation and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well-known clustering algorithms (k-means clustering, agglomerative clustering and density-based spatial clustering of applications with noise; DBSCAN), apply them on the set of time series and identify areas that undergo similar evolution during 1 month. We test if these algorithms fulfil our criteria for suitable clustering on our exemplary data set. The three clustering methods are applied to time series over 30 d extracted from a data set of daily scans covering about 2 km of coast in Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer, is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow us to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The level of detail found with these algorithms depends on the choice of the number of clusters k. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused, for example, by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatiotemporal data set for predominant deformation patterns with the associated regions where they occur.

scholarly journals Characterizing popularity dynamics of hot topics using micro-blogs spatio-temporal data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Lianren Wu ◽  
Jinjie Li ◽  
Jiayin Qi
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Power Law ◽  
Clustering Algorithm ◽  
Temporal Data ◽  
Literature Reference ◽  
Spectral Centroid ◽  
Popularity Dynamics ◽  
Temporal And Spatial ◽  
Spatio Temporal

AbstractIn this paper, a quantitative temporal and spatial analysis of the dynamics of hot topics popularity in Micro-blogging system was provided. Firstly, the popularity time series of 1167 hot topics were counted and calculated by Excel. Secondly, based on MATLAB software,the popularity time series were clustered into six clusters by K-spectral centroid (K-SC) clustering algorithm. Thirdly, we analyzed temporal patterns and spatial patterns of popularity dynamics of topics by statistical methods. The results show that temporal popularity of micro-blogging topics is rapidly dying, and the distribution of popularity is subject to the power law form. In addition, most of the Micro-blogging topics are global topic. Our results can provide a literature reference for studying the influence of online hot topics and the evolution of public opinion.

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

2020 ◽  
Vol 9 (4) ◽  
pp. 210
Author(s):  
Xiaojing Wu ◽  
Donghai Zheng
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Input Data ◽  
Clustering Algorithm ◽  
Temperature Data ◽  
Similar Data ◽  
Temporal Data ◽  
Clustering Methods ◽  
Spatio Temporal ◽  
Data Elements

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.

Analysing “Long Data” on Collective Violence in Indonesia

2015 ◽  
Vol 43 (5) ◽  
pp. 613-633
Author(s):  
David A. Meyer ◽  
Arthur Stein
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Model Building ◽  
Collective Violence ◽  
Temporal Data ◽  
Data Set ◽  
Time Correlations ◽  
Long Time ◽  
Short Time ◽  
Periodic Components

“Long data”, i.e., temporal data disaggregated to short time intervals to form a long time series, is a particularly interesting type of “big data”. Financial data are often available in this form (e.g., many years of daily stock prices), but until recently long data for other social, and even other economic, processes have been rare. Over the last decade, however, long data have begun to be extracted from (digitized) text, and then used to assess or formulate micro-level and macro-level theories. The UN Support Facility for Indonesian Recovery (UNSFIR) collected a long data set of incidents of collective violence in 14 Indonesian provinces during the 14 year period 1990–2003. In this paper we exploit the “length” of the UNSFIR data by applying several time series analysis methods. These reveal some previously unobserved features of collective violence in Indonesia—including periodic components and long time correlations—with important social/political interpretations and consequences for explanatory model building.

scholarly journals Using treemaps for variable selection in spatio-temporal visualisation

2008 ◽  
Vol 7 (3-4) ◽  
pp. 210-224 ◽  
Author(s):  
Aidan Slingsby ◽  
Jason Dykes ◽  
Jo Wood
Keyword(s):  
Variable Selection ◽  
Visual Analysis ◽  
Temporal Distribution ◽  
Temporal Data ◽  
Data Set ◽  
Raster Maps ◽  
Categorical Attributes ◽  
Interactive Interfaces ◽  
Road Maps ◽  
Spatio Temporal

We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km2 area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time-based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation.

scholarly journals Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

2016 ◽  
Vol 16 (5) ◽  
pp. 3399-3412 ◽  
Author(s):  
Thomas Schmidt ◽  
John Kalisch ◽  
Elke Lorenz ◽  
Detlev Heinemann
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Forecast Skill ◽  
Small Scale ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Forecast Performance ◽  
Electric Power Supply ◽  
Sky Imager ◽  
Spatio Temporal

Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

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