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
2010 ◽  
Vol 9 (1) ◽  
pp. 24 ◽  
Author(s):  
Nelli Westercamp ◽  
Stephen Moses ◽  
Kawango Agot ◽  
Jeckoniah O Ndinya-Achola ◽  
Corette Parker ◽  
...  

2017 ◽  
Vol 141 (3-4) ◽  
pp. 151-162
Author(s):  
Ljiljana Keča ◽  
Špela Pezdevšek-Malovrh ◽  
Sreten Jelić ◽  
Stjepan Posavec ◽  
Milica Marčeta

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.


2021 ◽  
Vol 94 (3) ◽  
pp. 305-324
Author(s):  
Przemysław Śleszyński

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.


2011 ◽  
Vol 8 (2) ◽  
pp. 3047-3083 ◽  
Author(s):  
R. Ley ◽  
M. C. Casper ◽  
H. Hellebrand ◽  
R. Merz

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.


2011 ◽  
Vol 15 (9) ◽  
pp. 2947-2962 ◽  
Author(s):  
R. Ley ◽  
M. C. Casper ◽  
H. Hellebrand ◽  
R. Merz

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.


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