scholarly journals A spatio-temporal cluster analysis of structurally deficient bridges in the contiguous USA

2020 ◽  
Vol 4 ◽  
pp. 100034
Author(s):  
R.W. Amin ◽  
S. Kocak ◽  
H.E. Sevil ◽  
G.P. Peterson ◽  
J.T. Hamilton ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Temporal Cluster ◽  
Spatio Temporal

Spatio-temporal cluster analyses of landslides in Italy at national and regional scale

2020 ◽  
Author(s):  
Marj Tonini ◽  
Kim Romailler ◽  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Cluster Analysis ◽  
Point Processes ◽  
Statistical Significance ◽  
Regional Scale ◽  
Regional Analysis ◽  
Scan Statistics ◽  
Economic Losses ◽  
Campania Region ◽  
Temporal Cluster ◽  
Spatio Temporal

<p><strong>Keywords:</strong> Landslides, FraneItalia, cluster analysis, spatio-temporal point process</p><p>In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.</p><p>In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan<sup>TM</sup> software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.</p><p>Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.</p><p>In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.</p><p>REFERENCES</p><p>Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. <em>Geoenvironmental Disasters</em>. 5: 13 (2018)</p><p>Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. <em>Landslides</em> 16 (2019)</p>

scholarly journals Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Congcong Xia ◽  
Robert Bergquist ◽  
Henry Lynn ◽  
Fei Hu ◽  
Dandan Lin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Poyang Lake ◽  
Temporal Cluster ◽  
Poyang Lake Region ◽  
Lake Region ◽  
Spatio Temporal

Risk analysis of veterinary drug residues in aquatic products in the Yangtze River Delta of China

2021 ◽  
Author(s):  
Sijing Xia ◽  
Bing Niu ◽  
Jiahui Chen ◽  
Xiaojun Deng ◽  
Qin Chen
Keyword(s):  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Aquatic Products ◽  
Temporal Cluster ◽  
Spatio Temporal ◽  
The Yangtze River Delta

Aquatic products are favored by people all over the world, but the potential quality and safety issues cannot be ignored. In order to determine the risk of veterinary drug residues in aquatic products in the Yangtze River Delta, this paper used the Geographic Information System (GIS) method to analyze Chinese veterinary drugs in aquatic products in Shanghai, Jiangsu, Zhejiang, and Anhui (Yangtze River Delta Urban Agglomerations) from 2017 to 2019. The spatial distribution pattern, hotspot detection and analysis and spatio-temporal cluster analysis of the residual excess rate and detection rate were studied. The results showed that the overall excess rate and detection rate of veterinary drug residues in aquatic products from 2017 to 2019 showed a spatial random distribution. The result of hotspot analysis and spatio-temporal cluster analysis showed that the rate of detection of veterinary drug residues and the rate of detection of residues in excess of regulatory standards were clustered. This study can provide a scientific basis for food safety evaluation and risk management suggestions.

Spatial–Temporal Cluster Analysis of Childhood Cancer in California

Epidemiology ◽  
2020 ◽  
Vol 31 (2) ◽  
pp. 214-223 ◽  
Author(s):  
Stephen Starko Francis ◽  
Catherine Enders ◽  
Rebecca Hyde ◽  
Xing Gao ◽  
Rong Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Childhood Cancer ◽  
Temporal Cluster
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