Spatiotemporal clustering: a review

Ansari, Mohd Yousuf; Ahmad, Amir; Khan, Shehroz S.; Bhushan, Gopal; Mainuddin,

doi:10.1007/s10462-019-09736-1

Cited by 85 publications

(40 citation statements)

References 88 publications

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“…Literature mention that space-time scan statistics allow identifying statistically significant hotspots. The hotspots are evaluated by a significance value 1 and are helpful for pattern understanding 14 . In this manner, the space-time scan statistic of this study identified the locations of the most-significant clusters.…”

Section: Discussionmentioning

confidence: 99%

“…Spatio-temporal is a type of clustering in which data values, including the time dimension, are introduced into spatial data. Accordingly, the objects are grouped as per their spatial and temporal similarity 1 . Vesicular stomatitis (VS) is a disease of livestock commonly observed in adult animals caused by vesicular stomatitis virus (VSV) belonging to Rhabdoviridae, genera Lyssavirus and Vesiculovirus 2,3 .…”

Section: Introductionmentioning

confidence: 99%

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A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

Salinas¹,

Torre²,

Moreno³

et al. 2021

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Vesicular stomatitis (VS) is a viral disease primarily affecting cattle, swine, and equine causing economic losses. It is of particular interest because its outward signs are similar to those of foot-and-mouth disease. Outbreaks of VS occurred in several herds in Ecuador in 2018, affecting principally bovines. In this sense, the present study was conducted to characterize the temporal and spatial dynamics of Vesicular stomatitis occurrence between January and December 2018. During the study period, 583 animals with symptoms of VS were reported. In this way, tissue samples were collected, VS was diagnosed, and outbreaks were defined as herds with a confirmed positive test for the disease. Outbreaks were georeferenced, and Space-time clusters were used to determine zones where the number of reported outbreaks was more significant than expected. A space-time permutation scan statistic (STPSS) was used to identify hot spots of space-time interaction within patterns of the cases reported. Standard Monte Carlo Critical Value was used to test for the cluster's significance. A total of 399 outbreaks were presented in 18 provinces. Spatial scan statistics allowed the detection of four significant space-time clusters of VS outbreaks. The highest incidence was reported around week 35 and week 44, which were observed outbreaks increase in the country's north region. In this sense, clusters coincided with the areas with the highest incidence of outbreaks. Besides, maps showed places where the disease is not shared. The information showed in the present study may contribute to prevents VS spread into regions of Ecuador that is only sporadically affected by the disease. Monitoring in affected zones may lead to quick responses to possible outbreaks issuing alerts when there is a greater than typical risk of spreading the disease

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

Salinas¹,

Torre²,

Moreno³

et al. 2021

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“…There are methods of clustering that use the approach of spatial clustering objects and spatial and temporal objects. Although our time series have a geo-location, we used only the time series points as a similarity measure to identify the clusters [46,47]. Then, the geo-localization and period of each time series were used to explore the spatiotemporal patterns.…”

Section: Overviewmentioning

confidence: 99%

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

et al. 2021

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The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

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“…Clustering is broadly defined as any process of grouping data by their similarities (Ansari et al, 2020). It is fundamental to data analysis in various disciplines (e.g., biology, epidemiology, communication, criminology) (Xu and Tian, 2015).…”

Section: Spatiotemporal Clustering Brief Backgroundmentioning

confidence: 99%

“…2 illustrates compound hazard event spatially, but one can also examine compound hazard events overlapping in time, and both space and time together. The large increase in spatiotemporal data now available has created increased opportunities for spatiotemporal clustering approaches (Shi and Pun-Cheng, 2019;Ansari et al, 2020). Many methods have been developed to cluster and classify data (e.g., partition, hierarchical, density-based, model-based clustering; see for a review Milligan and Cooper, 1987;Xu and Tian, 2015).…”

Section: Spatiotemporal Clustering Brief Backgroundmentioning

confidence: 99%

A Methodology for the Spatiotemporal Identification of Compound Hazards: Wind and Precipitation Extremes in Great Britain (1979–2019)

Tilloy¹,

Malamud²,

Joly-Laugel

2021

Preprint

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Abstract. Compound hazards are two different natural hazards that impact the same time period and spatial area. Compound hazards can have a footprint that can operates on different spatial and temporal scales than their component single hazards. This article proposes a definition of compound hazards in space and time and presents a methodology for the Spatiotemporal Identification of Compound Hazards (SI–CH). The approach is applied to the analysis of compound precipitation and wind extremes in Great Britain from which we create a database. Hourly precipitation and wind gust values for 1979–2019 are extracted from climate reanalysis (ERA5) within a region including Great Britain and the British channel. Extreme values (above the 99 % quantile) of precipitation and wind gust are clustered with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, creating clusters for precipitation and for wind gusts. Compound hazard clusters that correspond to the spatial overlap of single hazard clusters during the aggregated duration of the two hazards are then identified. Our ERA5 Hazard Clusters Database (given as a supplement) consists of 18,086 precipitation clusters, 6190 wind clusters, and 4555 compound hazard clusters. The methodology’s ability to identify extreme precipitation and wind events is assessed with a catalogue of 157 significant events (96 extreme precipitation and 61 extreme wind events) that occurred in Great Britain over the period 1979–2019 (also given as a supplement). We find a good agreement between the SI–CH outputs and the catalogue with an overall hit rate (ratio between the number of joint events and the total number of events) of 93.7 %. The spatial variation of hazard intensity within wind, precipitation and compound hazard clusters are then visualised and analysed. The study finds that the SI–CH approach (given as R code in supplement) can accurately identify single and compound hazard events and represent spatial and temporal properties of compound hazard events. We find that compound wind and precipitation extremes, despite occurring on smaller scales than single extremes, can occur on large scales in Great Britain with a decreasing spatial scale when the combined intensity of the hazards increases.

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Spatiotemporal clustering: a review

Cited by 85 publications

References 88 publications

A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

A Spatio-temporal distribution analysis of vesicular stomatitis outbreak in Ecuador, 2018

Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

A Methodology for the Spatiotemporal Identification of Compound Hazards: Wind and Precipitation Extremes in Great Britain (1979–2019)

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