scholarly journals Stages of spatial dispersion of the COVID-19 epidemic in Poland in the first six months (4 March-20 September, 2020).

2021 ◽  
Vol 94 (3) ◽  
pp. 305-324
Author(s):  
Przemysław Śleszyński
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Spatial Distribution ◽  
Population Density ◽  
Spatial Patterns ◽  
Concentration Index ◽  
Spatial Dispersion ◽  
First Case ◽  
And Cluster Analysis ◽  
Day By Day

The article is a continuation of research published by the author elsewhere (Śleszyński, 2020). The elaboration presents the regularity of spatial distribution of infections during the first six months after the detection of SARS-CoV-2 coronovirus in Poland under strong lockdown conditions. The main aim is to try to determine the basic temporal-spatial patterns and to answer the questions: to what extent the phenomenon was ordered and to what extent it was chaotic, whether there are any particular features of spread, whether the infection is concentrated or dispersed and whether the spreading factors in Poland are similar to those observed in other countries. Day by day data were used according to the counties collected in Rogalski’s team (2020). The data were aggregated to weekly periods (7 days) and then the regularity of spatial distribution was searched for using the cartogram method, time series shifts, rope correlation between the intensity of infections in different periods, Herfindahl-Hirschman concentration index (HHI) and cluster analysis. A spatial typology of infection development in the population was also performed. Among other things, it was shown that during the first period (about 100 days after the first case), the infections became more and more spatially concentrated and then dispersed. Differences were also shown in relation to the spread of the infection compared to observations from other countries, i.e. no relation to population density and level of urbanization.

scholarly journals Spatial distribution and cluster analysis of sexual risk behaviors reported by young men in Kisumu, Kenya

2010 ◽  
Vol 9 (1) ◽  
pp. 24 ◽  
Author(s):  
Nelli Westercamp ◽  
Stephen Moses ◽  
Kawango Agot ◽  
Jeckoniah O Ndinya-Achola ◽  
Corette Parker ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Sexual Risk ◽  
Risk Behaviors ◽  
Sexual Risk Behaviors ◽  
Young Men ◽  
And Cluster Analysis

scholarly journals Market characteristics and cluster analysis of non-wood forest products

2017 ◽  
Vol 141 (3-4) ◽  
pp. 151-162
Author(s):  
Ljiljana Keča ◽  
Špela Pezdevšek-Malovrh ◽  
Sreten Jelić ◽  
Stjepan Posavec ◽  
Milica Marčeta
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Research Method ◽  
Forest Products ◽  
Aromatic Plants ◽  
Medicinal And Aromatic Plants ◽  
Main Research ◽  
And Cluster Analysis ◽  
Wild Berries

The share of small and medium-sized enterprises (SMEs) is largely present in forestry, especially in the segment related to non-wood forest products (NWFPs) in Europe. They are also a dominant category in entrepreneurship in Serbia. Therefore, the subjects of this research were the companies operating in the sector of NWFPs, within specific statistical regions of Serbia. The database of SMEs was obtained from 119 SMEs and the share of surveyed SMEs was 81.5%. The main research method was two-step cluster analysis. Questionnaire was used for the purpose of the research. The aim of the research was to identify clusters in order to establish similarities within the defined clusters and the differences among them. Spatial distribution of specific categories of NWFPs in nature (mushrooms, medicinal and aromatic plants, honey and wild berries), contributed to the portfolio of the companies. This largely influenced clusters that are created by categories of products that are typical for certain statistical regions in Serbia.

scholarly journals Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013

2018 ◽  
Vol 3 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Srinivasa Rao Mutheneni ◽  
Rajasekhar Mopuri ◽  
Suchithra Naish ◽  
Deepak Gunti ◽  
Suryanarayana Murty Upadhyayula
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Andhra Pradesh ◽  
Self Organizing Maps ◽  
And Cluster Analysis ◽  
Self Organizing

scholarly journals Spatial distribution of the macrozoobenthos in the Solís Grande Stream estuary (Canelones-Maldonado, Uruguay)

2001 ◽  
Vol 61 (3) ◽  
pp. 409-420 ◽  
Author(s):  
P. MUNIZ ◽  
N. VENTURINI
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Species Richness ◽  
Spatial Patterns ◽  
Fine Sand ◽  
Present Species ◽  
Multivariate Techniques ◽  
Natural Factors ◽  
Mean Diameter ◽  
Variation Explained

The analysis of 24 quantitative macrobenthic samples taken from the Solís Grande Stream estuary yielded 10 species from a total of 4,446 individuals. It was verified that both species richness and diversity was lower than those recorded in nearby regions with similar environmental conditions. In contrast with other studies, a marked dominance of any of the present species was not verified. All the species recorded correspond to typical estuarine organisms. Abundance data were analysed with multivariate techniques and the results showed a relationship with salinity, mean diameter and the percentage of fine sand. According to the cluster analysis and the canonical correspondence analysis (CCA) four groups of stations were defined. The partition out of total variation of the species data showed that the amount of variation explained by the space alone was low. Spatial patterns observed and their possible causes are analysed and discussed in relation to the natural factors that acts in this coastal ecosystem.

scholarly journals Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Huling Li ◽  
Hui Li ◽  
Zhongxing Ding ◽  
Zhibin Hu ◽  
Feng Chen ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Cumulative Number ◽  
Moran's I ◽  
Spatial Statistical Analysis ◽  
Global Concern ◽  
And Cluster Analysis ◽  
Global Moran’S I

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran’s I (Moran, 1950), Local Moran’s I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran’s I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran’s I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

scholarly journals The Spatial Distribution of Young Objects in the Large Magellanic Cloud - A Problem of Pattern Recognition

1984 ◽  
Vol 108 ◽  
pp. 93-94
Author(s):  
J. V. Feitzinger ◽  
E. Braunsfurth
Keyword(s):  
Cluster Analysis ◽  
Pattern Recognition ◽  
Spatial Distribution ◽  
Large Magellanic Cloud ◽  
Magellanic Cloud ◽  
Ob Associations ◽  
And Cluster Analysis

Methods used in pattern recognition and cluster analysis are applied to investigate the spatial distribution of OB associations and emission regions in the LMC. For our analysis we used the catalogue of associations of Lucke and Hodge (1970) and the catalogue of emission regions of Davies et al. (1976).

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