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Relevant bibliographies by topics / Cluster analysis][Geographical data

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Journal articles

Academic literature on the topic 'Cluster analysis][Geographical data'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Cluster analysis][Geographical data"

1

Putrenko, V., and V. Tyhohod. "Cluster analyzes for spatial modelling in Geographic information systems." Visnyk of the Lviv University. Series Geography, no. 46 (December 26, 2013): 312–19. http://dx.doi.org/10.30970/vgg.2013.46.1497.

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The problems of mathematical analysis of geographic information with using the procedure of cluster analysis were considered. A software module for various types of cluster analysis of geographical objects and the automated construction of thematic maps was programmed in ArcGIS. Based on the data about accumulation of waste in the regions of Ukraine method of cluster analysis using the geographical coordinates of the centroids of objects to take into account their position in content classification was tested. The results of cluster analysis identified groups of regions of Ukraine with similar

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2

Cho, Jaehyeong, Seng Chan You, Seongwon Lee, et al. "Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model." International Journal of Environmental Research and Public Health 17, no. 21 (2020): 7824. http://dx.doi.org/10.3390/ijerph17217824.

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Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality. Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and

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3

Ji, Min, Fuding Xie, and Yu Ping. "A Dynamic Fuzzy Cluster Algorithm for Time Series." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/183410.

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This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demons

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4

Lis, Anna Maria. "The significance of proximityin cluster initiatives." Competitiveness Review: An International Business Journal 29, no. 3 (2019): 287–310. http://dx.doi.org/10.1108/cr-08-2018-0050.

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Purpose The purpose of this paper is to analyse relations between geographical and competence proximity and development of cooperation in cluster initiatives. Design/methodology/approach The research was based on an original theoretical concept referring to the trajectory of the development of cooperative relations in cluster initiatives. The research was carried out in mid-2017, in four purposefully selected cluster initiatives. The research sample was 132 cluster enterprises. The main research strategy involved non-experimental models; the basic method of data collection was an online questi

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5

Maeda, Takahiro, and Hiroyuki Fujiwara. "Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data." Journal of Disaster Research 12, no. 2 (2017): 233–40. http://dx.doi.org/10.20965/jdr.2017.p0233.

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This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on t

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6

Kalinina, Alla, Elena Petrova, Marina Lapina, and Alexandra Rvacheva. "The Analysis of Foreign Experience in Implementing Cluster Policy." Regionalnaya ekonomika. Yug Rossii, no. 2 (August 2019): 13–26. http://dx.doi.org/10.15688/re.volsu.2019.2.2.

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The article represents the results of the comparative analysis of implementing cluster policy in foreign and Russian practice. The proposed methodology is based on the main characteristics of clusters (the presence of competitive enterprises, the presence of competitive advantages for cluster development in the region, geographical concentration and proximity, a wide range of participants and the presence of “critical mass”, the presence of links and interaction between cluster members) that characterize them as complex economic structures. Creating clusters involves a number of studies at the

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7

Subekti, Didik Tulus, Ichwan Yuniarto, and Sulinawati Sulinawati. "Perbandingan Metode Hierarchical Cluster Analysis untuk Analisis Keragaman Hayati Trypanosoma evansi dari Indonesia Berdasarkan Profil Protein (COMPARISON OF HIERARCHICAL CLUSTER ANALYSIS METHODS FOR BIODIVERSITY ANALYSIS OF TRYPANOSOMA EVANSI." Jurnal Veteriner 18, no. 4 (2018): 516. http://dx.doi.org/10.19087/jveteriner.2017.18.4.516.

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Hierarchical Clustering Analysis (HCA) has long been known to be useful for the analysis of biodiversity of microorganisms based on SDSPAGE protein profile (sodium dodecyl sulfate polyacrylamide gel electrophoresis). However, varying methods of HCA consequently produce variability of analysis results and interpretations. Therefore, it is necessary to evaluate and further determine the most appropriate method which could described the biodiversity based on protein profiles of T.evansi isolates from Indonesia. Eleven isolates of T.evansi from different geographic locations were run on SDS PAGE.

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8

Pita, Antonio, Francisco J. Rodriguez, and Juan M. Navarro. "Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data." International Journal of Environmental Research and Public Health 18, no. 16 (2021): 8271. http://dx.doi.org/10.3390/ijerph18168271.

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniq

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9

Makharashvili, I., and N. Lomaia. "Internationalization and clusters of cultures." Fundamental and applied researches in practice of leading scientific schools 31, no. 1 (2019): 129–34. http://dx.doi.org/10.33531/farplss.2019.1.26.

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The cluster term is a cluster analysis tool that develops from the mid-twentieth century and was used in areas where there was an enormous mass of primary data and this concept had no economic content. Cluster Analysis is a mathematical procedure based on a number of characteristics of the set of objects, which allow them to classify as classes (clusters) so that objects entered in one class are more homogeneous and similar to those in other classes. The distance between them is calculated on the basis of numerically expressed parameters. The method first appeared in 1939 in R. Trion used and

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10

Köppe, M., M. Hermann, C. A. M. Brenninkmeijer, et al. "Origin of aerosol particles in the mid-latitude and subtropical upper troposphere and lowermost stratosphere from cluster analysis of CARIBIC data." Atmospheric Chemistry and Physics 9, no. 21 (2009): 8413–30. http://dx.doi.org/10.5194/acp-9-8413-2009.

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Abstract. The origin of aerosol particles in the upper troposphere and lowermost stratosphere over the Eurasian continent was investigated by applying cluster analysis methods to in situ measured data. Number concentrations of submicrometer aerosol particles and trace gas mixing ratios derived by the CARIBIC (Civil Aircraft for Regular Investigation of the Atmosphere Based on an Instrument Container) measurement system on flights between Germany and South-East Asia were used for this analysis. Four cluster analysis methods were applied to a test data set and their capability of separating the

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