Spatial clustering of events on a network

2010 ◽  
Vol 18 (3) ◽  
pp. 411-418 ◽  
Author(s):  
Thérèse Steenberghen ◽  
Koen Aerts ◽  
Isabelle Thomas
Keyword(s):  
Spatial Clustering ◽  
Clustering Of Events

scholarly journals Seismogenic structure behaviour revealed by spatial clustering of seismicity in the Umbria-Marche Region (Central Italy)

1998 ◽  
Vol 41 (2) ◽  
Author(s):  
P. Tosi
Keyword(s):  
Spatial Clustering ◽  
Central Italy ◽  
Large Area ◽  
Seismogenic Structure ◽  
Marche Region ◽  
Background Seismicity ◽  
Clustering Of Events ◽  
Time Variations ◽  
Definition Of ◽  
Correlation Dimension Method

Time variations in the spatial distribution of earthquake epicentres are analyzed by application of the fractal correlation dimension method. The zone under investigation is located in Central Italy, bounded in longitude by 12.0 and 14.4 degrees east and in latitude by 42.0 and 43.6 degrees north. From 1st January 1978 to 5th October 1997, 2028 events with a magnitude above Ml= 2.5 constitute the database.Evolution of the spatial fractal dimension Ds permits the identification of seismic cycles that are connected to the occurrence of main earthquakes.In particular, it is possible to recognize a division within each cycle, between a period of random background seismicity and a spatial clustering of events where shocks of magnitude Ml ³occur. Moreover, the decrease in Ds prior to such events, evidences a structural relationship between foreshocks and the occurrence of a main shock, even if not in close territorial proximity.This feature indicates a new, more extensive definition of seismogenic structure which can includes several interconnected structures within a large area.

Temporal Clustering of Earthquakes: Examples from the Basin and Range Province

1988 ◽  
Vol 59 (4) ◽  
pp. 316-316
Author(s):  
James Ni ◽  
Terry Wallace
Keyword(s):  
Spatial Clustering ◽  
Late Quaternary ◽  
Basin And Range ◽  
Earthquake Hazards ◽  
Basin And Range Province ◽  
Temporal Clustering ◽  
Temporal Cluster ◽  
Clustering Of Events ◽  
Morphologic Analysis ◽  
Fault Scarps

Abstract Accurate forecasting of earthquake hazards depends on whether seismicity is time-stationary or variable. Detailed studies of late Quaternary faulting in two parts of the Basin and Range suggest that seismicity cannot be considered time-stationary, and there may be temporal clustering of earthquakes. In west central Nevada there appear to be three “peaks” of activity, (1) the historical activity along the Nevada Seismic Belt, (2) a temporal cluster at 2,000–3,000 ya, (3) a duster at 5,000–6,000 ya. For the two older episodes the ages of faulting are determined by morphologic analysis of fault scarps. Similar analysis of Quaternary fault scarps in southeastern Arizona show clustering of events at 20,000–25,000 ya and 80,000–90,000 ya. In both Arizona and Nevada the temporal clustering is not related to spatial clustering. However, the historical Nevada activity shows a spatial correlation. Two basic issues need to be resolved: (1) why is there temporal clustering, and (2) how much communication is there between faults?

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

scholarly journals Spatial clustering of drug-resistant tuberculosis in Hunan province, China: an ecological study

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e043685
Author(s):  
Kefyalew Addis Alene ◽  
Zuhui Xu ◽  
Liqiong Bai ◽  
Hengzhong Yi ◽  
Yunhong Tan ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Cumulative Incidence ◽  
Ecological Study ◽  
Spatial Clustering ◽  
Hunan Province ◽  
Drug Resistant ◽  
Drug Resistant Tuberculosis ◽  
The North ◽  
Resistant Tuberculosis ◽  
Over Time

ObjectiveThis study aimed to investigate the spatial distribution of drug-resistant tuberculosis (DR-TB) in Hunan province, China.MethodsAn ecological study was conducted using DR-TB data collected from the Tuberculosis Control Institute of Hunan Province between 2012 and 2018. Spatial clustering of DR-TB was explored using the Getis-Ord statistic. A Poisson regression model was fitted with a conditional autoregressive prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo simulation, to quantify associations with possible risk factors and identify clusters of high DR-TB risk.ResultsA total of 2649 DR-TB patients were reported to Hunan TB Control Institute between 2012 and 2018. The majority of the patients were male (74.8%, n=1983) and had a history of TB treatment (88.53%, n=2345). The proportion of extensively DR-TB among all DR-TB was 3.3% (95% CI 2.7% to 4.1%), which increased from 2.8% in 2012 to 4.4% in 2018. Of 1287 DR-TB patients with registered treatment outcomes, 434 (33.8%) were cured, 198 (15.3%) completed treatment, 92 (7.1%) died, 108 (8.3%) had treatment failure and 455 (35.3%) were lost to follow-up. Half (50.9%, n=655) had poor treatment outcomes. The annual cumulative incidence rate of notified DR-TB increased over time from 0.25 per 100 000 people in 2012 to 0.83 per 100 000 people in 2018. Substantial spatial heterogeneity was observed, and hotspots were detected in counties located in the North and East parts of Hunan province. The cumulative incidence of notified DR-TB was significantly associated with urban communities.ConclusionThe annual incidence of notified DR-TB increased over time in Hunan province. Spatial clustering of DR-TB was detected and significantly associated with urbanisation. This finding suggests that targeting interventions to the highest risk areas and population groups would be effective in reducing the burden and ongoing transmission of DR-TB.

scholarly journals Spatial Distribution and Land Use of Traditional Villages in Southwest China

2021 ◽  
Vol 13 (11) ◽  
pp. 6326
Author(s):  
Xiye Zheng ◽  
Jiahui Wu ◽  
Hongbing Deng
Keyword(s):  
Cultural Heritage ◽  
Influencing Factors ◽  
Landscape Pattern ◽  
Southwest China ◽  
Spatial Clustering ◽  
Interaction Mechanism ◽  
Cultivated Land ◽  
River Population ◽  
Local Conditions ◽  
Countermeasures And Suggestions

Traditional villages are the historical and cultural heritage of people around the world. With the increases in urbanization and industrialization, the continuation of traditional villages and the inheritance of historical and cultural heritage are facing risk. Therefore, to grasp the spatial characteristics of them and the human–nature interaction mechanism in Southwest China, we analyzed the distribution pattern of traditional villages using the ArcGIS software. Then, we further analyzed the spatial clustering characteristics, influencing factors and landscape pattern, and put forward relevant protection countermeasures and suggestions. The results revealed that traditional villages in Southwest China were clustered, being mainly distributed in areas with relatively low elevation, gentle slopes, low relative positions, nearby water sources, and convenient transportation. They can be divided into four categories due to obvious differences in influencing factors such as elevation, slope, relative position, distance to the nearest river, population density, etc. The landscape pattern of traditional villages differed among the different clusters, being mainly composed of forests, shrubs, and cultivated land. With the increase in the buffer radius, the landscape pattern of them changed significantly. The results of this study reflect that traditional villages and the natural environment are interdependent, so the protection of traditional villages should carry out measures according to local conditions.

Sign in / Sign up

Export Citation Format

Share Document