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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 indicators for waste management and the potential hazards to the environment. Key words: mathematical methods, cluster analysis, GIS, waste management.
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Cho, Jaehyeong, Seng Chan You, Seongwon Lee, DongSu Park, Bumhee Park, George Hripcsak, and Rae Woong Park. "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 (October 26, 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 outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States). Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran’s I (0.44; p < 0.001) was 17.4 (10.3–26.9). The malarial endemic cluster was identified in Paju-si, Korea (p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified (p < 0.001). Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.
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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 demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.
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Lis, Anna Maria. "The significance of proximityin cluster initiatives." Competitiveness Review: An International Business Journal 29, no. 3 (May 20, 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 questionnaire. Findings The results indicated that the role of geographical and competence proximity depends on the level of cooperation in a cluster initiative. In both these dimensions, proximity was important during the initial stage of cluster development: to start cooperation between the members, however, when more mature forms of cooperation were undertaken, the factor of common location was not so crucial any longer. It was also recommended to maintain some competence distance between the partners. Research limitations/implications The main limitations referred to the static character of the data, the use of original measurement tools, which had not been tested before, the small and little differentiated research sample and the subjective nature of the research. The above-mentioned limitations should be viewed as a starting point for further empirical research. Practical implications Knowledge on the significance of geographical and competence proximity at various levels of cooperation in clusters is valuable for efficient management of a cluster and for higher competitiveness that it can achieve. Originality/value The research study contributes to the literature, which refers to the question of proximity in clusters through the analysis of relations between geographical and competence proximity and development of cooperation in cluster initiatives. The results of the research point out that the role of geographical and competence proximity evolves with the development of cooperation in cluster initiatives.
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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 (March 16, 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 the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually; they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.
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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 territorial level, which, above all, relate to determining the competitive advantages of the territory in a particular industry. Therefore, at the first stage of benchmarking, the authors propose to systematize theoretical approaches to the definition of “cluster” category. The second stage involves identifying the features of the cluster approach as a tool to improve the competitiveness of individual territories, regions, economies. At the last stage, authors determine structuring of foreign and Russian experience in the development of clusters and analyze the approaches to implementing cluster policy and identify their strengths and weaknesses. In contrast to the existing methods for assessing the potential of a cluster, the approach implemented in this article makes it possible to identify not only promising territories from the point of view of the industry clusterization, but also to identify possible participants of such a cluster, which is the most promising in forming regional cluster development programs in the regional economy. The article presents the approbation of the proposed methodology for the Russian Federation based on statistical data for 2014–2016. The authors highlight industries and enterprises that can be clustered, which will ensure adequate support of regional authorities.
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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 (February 1, 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. Furthermore, SDS PAGE protein profiles from eleven isolates were converted into binary data and analyzed using five different methods of HCA i.e. Average Linkage, Complete Linkage, Single Linkage, Ward Linkage and McQuitty Linkage, respectively.Data were also analyzed by multidimensional scaling (MDS) and densitogram. The analysis showed that the dendrogram constructed with Ward Linkage gives the best results and corresponding with densitogram, MDS and able to describe the geographical origins of isolates.
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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 (August 4, 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 techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.
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Makharashvili, I., and N. Lomaia. "Internationalization and clusters of cultures." Fundamental and applied researches in practice of leading scientific schools 31, no. 1 (February 28, 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 called this method cluster analysis. Cluster is a complex concept that involves "industrial, geographically close, intercontinental companies and other organizations that act in a certain area and characterized by unity and / or mutual filling". In some works clusters are called "industrial" or "industrial areas". According to the Porter, the cluster is a group of geographically close interdependent companies and related organizations operating in a certain area and characterized by unity, and complemented by each other. In this definition, the main focus is on the three main features of enterprise clusters: geographical localization, interconnection between enterprises and technological interaction of sectors.
The Eastern European cluster in which Georgia enters, is characterized by a high level of power distance and collectivism. Members of this community maintain close family connections and are characterized by low orientation of orientation and final outcome. Also, the distinctive features of this cluster are the charismatic and team-oriented style of leadership. Such dimensions and leadership styles, such as personality orientation, institutional collectivity and gender equity, occupy the middle position among the clusters. According to the GLOBE project, members of the Eastern European Clusters do not expect that power will be distributed between the citizens, focusing on the group and family, paying attention to the power and status of the person. Compared to other clusters, they are confused and aggressive during interpersonal relationships. Despite the fact that the personality orientation of the leadership and the participatory styles are positively perceived in the clusters for clusters, the charismatic and group-oriented style of leadership gains more importance.
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Köppe, M., M. Hermann, C. A. M. Brenninkmeijer, J. Heintzenberg, H. Schlager, T. Schuck, F. Slemr, 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 (November 5, 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 data points into scientifically reasonable clusters was assessed. The best method was applied to seasonal data subsets for summer and winter resulting in five cluster or air mass types: stratosphere, tropopause, free troposphere, high clouds, and boundary layer influenced. Other source clusters, like aircraft emissions could not be resolved in the present data set with the used methods. While the cluster separation works satisfactory well for the summer data, in winter interpretation is more difficult, which is attributed to either different vertical transport pathways or different chemical lifetimes in both seasons. The geographical distribution of the clusters together with histograms for nucleation and Aitken mode particles within each cluster are presented. Aitken mode particle number concentrations show a clear vertical gradient with the lowest values in the lowermost stratosphere (750–2820 particles/cm3 STP, minimum of the two 25% – and maximum of the two 75%-percentiles of both seasons) and the highest values for the boundary-layer-influenced air (4290–22 760 particles/cm3 STP). Nucleation mode particles are also highest in the boundary-layer-influenced air (1260–29 500 particles/cm3 STP), but are lowest in the free troposphere (0–450 particles/cm3 STP). The given submicrometer particle number concentrations represent the first large-scale seasonal data sets for the upper troposphere and lowermost stratosphere over the Eurasian continent.
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Köppe, M., M. Hermann, C. A. M. Brenninkmeijer, J. Heintzenberg, H. Schlager, T. Schuck, F. Slemr, 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 Discussions 9, no. 3 (June 18, 2009): 13523–67. http://dx.doi.org/10.5194/acpd-9-13523-2009.
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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 data points into scientifically reasonable clusters was assessed. The best method was applied to seasonal data subsets for summer and winter resulting in five cluster or air mass types: stratosphere, tropopause, free troposphere, high clouds, and boundary layer influenced. Other source clusters, like aircraft emissions could not be resolved in the present data set with the used methods. While the cluster separation works satisfactory well for the summer data, in winter interpretation is more difficult, which is attributed to either different vertical transport pathways or different chemical lifetimes in the two seasons. The geographical distribution of the clusters together with histograms for nucleation and Aitken mode particles within each cluster are presented. Aitken mode particle number concentrations show a clear vertical gradient with the lowest values in the lowermost stratosphere (750–2820 particles/cm3 STP, minimum of the two 25%- and maximum of the two 75%-percentiles of both seasons) and the highest values for the boundary-layer-influenced air (4290–22 760 particles/cm3 STP). Nucleation mode particles are also highest in the boundary-layer-influenced air (1260–29 500 particles/cm3 STP, but are lowest in the free troposphere (0–450 particles/cm3 STP). The given submicrometer particle number concentrations represent the first statistically sound data set for the upper troposphere and lowermost stratosphere over the Eurasian continent.
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Coleman, Warren K., Xin-Qing Li, Esther Tremblay-Deveau, and Shirlyn Coleman. "Chemical maturation and storage performance of eleven Russet Burbank clones." Canadian Journal of Plant Science 83, no. 4 (October 1, 2003): 893–902. http://dx.doi.org/10.4141/p02-172.
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Although introduced as a cultivar from a somaclonal mutation of smooth Burbank in the 19th century, the subsequent identification and commercial selection of distinct Russet Burbank clones or strains has not been evident in the scientific literature. The present study examined chemical maturity of tubers (sucrose rating values and glucose content) and subsequent storage performance over a 3-yr period (1999–2001) of eleven Russet Burbank clones collected from across North America in order to determine if subtle quality traits could be associated consistently with specific clones. Although considered a late maturity cultivar (140 d), all clones were chemically mature (sucrose rating values of approximately 1) at the New Brunswick growing site by 125 d. Exploratory cluster analyses using hierarchical or nonhierarchical methods applied to pre-harvest and harvest sucrose and glucose levels in tubers during the 3-yr study allowed the North American clones to be separated into clusters that reflected geographical biases and this hypothesis was supported by canonical discriminant analysis. Exploratory cluster analysis of post-harvest data, however, did not find any consistent structure in the clones based on sucrose or glucose levels during storage at 4 or 10°C for up to 8 mo or after reconditioning from 4°C. Polymerase chain reaction analysis of 10 selected simple sequence repeat (SSR) regions confirmed that the 11 clones belonged to the cultivar Russet Burbank. The limited geographically featured clustering based on pre-harvest sugar changes observed among the clones may have their origins in cultural practices with somaclonal variation leading to geographical strains of the cultivar. Key words: Potato, Solanum tuberosum, cluster analysis, microsatellite DNA markers
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Astuti, Cindy Cahyaning, and Rahmania Sri Untari. "Applied Hierarchical Cluster Analysis with Average Linkage Algoritm." CAUCHY 5, no. 1 (November 30, 2017): 1. http://dx.doi.org/10.18860/ca.v5i1.3862.
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This research was conducted in Sidoarjo District where source of data used from secondary data contained in the book <em>"Kabupaten Sidoarjo Dalam Angka 2016"</em> .In this research the authors chose 12 variables that can represent sub-district characteristics in Sidoarjo. The variable that represents the characteristics of the sub-district consists of four sectors namely geography, education, agriculture and industry. To determine the equitable geographical conditions, education, agriculture and industry each district, it would require an analysis to classify sub-districts based on the sub-district characteristics. Hierarchical cluster analysis is the analytical techniques used to classify or categorize the object of each case into a relatively homogeneous group expressed as a cluster. The results are expected to provide information about dominant sub-district characteristics and non-dominant sub-district characteristics in four sectors based on the results of the cluster is formed.
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Zhu, Jie, Jing Yang, Shaoning Di, Jiazhu Zheng, and Leying Zhang. "A novel dual-domain clustering algorithm for inhomogeneous spatial point event." Data Technologies and Applications 54, no. 5 (October 28, 2020): 603–23. http://dx.doi.org/10.1108/dta-08-2019-0142.
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PurposeThe spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.Design/methodology/approachIn this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.FindingsThe effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.Originality/valueTraditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.
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Zhong, Shaobo, Zhanhui Sun, Quanyi Huang, and Chunxiang Cao. "A framework for geographical surveillance of disease in China." International Journal of Disaster Resilience in the Built Environment 2, no. 3 (October 4, 2011): 256–67. http://dx.doi.org/10.1108/17595901111167123.
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PurposeThe purpose of this paper is to address the urgent need for guiding the construction of information systems for disease surveillance and early warning, given the latest efforts of the report system of public health information over China.Design/methodology/approachA system framework for disease surveillance and early warning, based on disease clustering test and cluster detection techniques, geographical information system, network and communication is conceived. Through geographical surveillance analysis of severe acute respiratory syndrome occurring in Beijing in 2003, an application example of the framework is illustrated.FindingsThrough approaches such as integrating spatial‐time clustering test and cluster detection algorithms, spatial visualization, computer network, wireless communication, it is feasible to build a systematic, automatic, real‐time surveillance and early warning system for prevention and control of disease.Research limitations/implicationsThe present study provides an underlying framework for the development of disease surveillance and early warning system enabling data acquisition, data analysis and alarm publishing.Originality/valueThe framework integrates report system of public health information, GIS and disease clustering test and cluster detection techniques into an application, which will significantly enhance the resilience of healthcare facilities. It is supposed to be implemented in near future and provides fundamental support for nation‐wide disease surveillance and early warning.
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Thewissen, J. G. M. "Temporal data in phylogenetic systematics: an example from the mammalian fossil record." Journal of Paleontology 66, no. 1 (January 1992): 1–8. http://dx.doi.org/10.1017/s0022336000033424.
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A method of phylogenetic inference is proposed for taxa that are known from large samples spaced closely in time. The method employs elements of cladistic and stratophenetic methods, and consists of four steps. 1) Morphologically homogeneous clusters are recognized within temporally and geographically constrained samples. 2) Temporally disjunct and geographically dispersed taxa are recognized, and their anagenetic evolution and geographic variation documented. 3) A character matrix is constructed for the taxa and analyzed cladistically. 4) Resulting cladograms are used to construct a phylogenetic tree with additional input from temporal, morphological, ecological, and geographical data. This method supplements the use of cladistically analyzed morphological data with data that are not suited for cladistic analysis, and thus reduces the amount of unused data.
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Martin, Roman, and Jerker Moodysson. "Comparing knowledge bases: on the geography and organization of knowledge sourcing in the regional innovation system of Scania, Sweden." European Urban and Regional Studies 20, no. 2 (December 22, 2011): 170–87. http://dx.doi.org/10.1177/0969776411427326.
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This paper deals with knowledge flows and collaboration between firms in the regional innovation system of southern Sweden. The aim is to analyse how the functional and spatial organization of knowledge interdependencies among firms and other actors varies between different types of industries that draw on different types of knowledge bases. We use data from three case studies of firm clusters in the region: (1) the life science cluster represents an analytical (science-based) industry, (2) the food cluster includes mainly synthetic (engineering-based) industries, and (3) the moving media cluster is considered to be symbolic (artistic based). Knowledge sourcing and knowledge exchange in each of the cases are explored and compared using social network analysis in association with data gathered through interviews with firm representatives. Our findings reveal that knowledge exchange in geographical proximity is especially important for industries that rely on a symbolic or synthetic knowledge base, because the interpretation of the knowledge they deal with tends to differ between places. This is less the case for industries drawing on an analytical knowledge base, which rely more on scientific knowledge that is codified, abstract and universal and are therefore less sensitive to geographical distance. Thus, geographical clustering of firms in analytical industries builds on rationales other than the need for proximity for knowledge sourcing.
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Novotná, Jana, and Ladislav Novotný. "Industrial clusters in a post-socialist country: The case of the wine industry in Slovakia." Moravian Geographical Reports 27, no. 2 (June 1, 2019): 62–78. http://dx.doi.org/10.2478/mgr-2019-0006.
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Abstract Research on clusters, unlike cluster initiatives, has not been comprehensively addressed in European post-socialist countries. The aim of this paper is to explore and to analyse quantitatively the spatial organisation of economic activities in the wine industry in Slovakia, and to assess it in terms of the concept of an industrial cluster. The wine industry is considered as a production sector in which location is determined by geographical factors. The research is based on a case study of a wine region located north-east of Bratislava, Slovakia. The primary identification of the cluster potential is based on the assessment of geographic conditions and statistical analyses focused on the spatial concentration of the industry within the defined area. An extensive questionnaire survey provided data for assessing the spatial organisation of economic activities and their impact on regional competitive advantage. Despite the spatial distribution of economic activities and relations among business entities affected by socialist industrialisation and post-socialist transformation, the results show that the industrial cluster was formed in the wine industry and its performance converges with the wine clusters in traditional Western European wine regions.
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Vatansever, Metin, İbrahim Demir, and Ali Hepşen. "Cluster and forecasting analysis of the residential market in Turkey." International Journal of Housing Markets and Analysis 13, no. 4 (January 23, 2020): 583–600. http://dx.doi.org/10.1108/ijhma-11-2019-0110.
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Purpose The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices. Design/methodology/approach In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices. Findings The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort. Research limitations/implications In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation. Practical implications The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account. Social implications From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market. Originality/value There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.
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Bardea, Florina, Felix Arion, and Patryk Szabelski. "CLUSTERS, EUROPEAN POLICY IN EXCELLENCE OF MANAGEMENT." Proceedings of CBU in Economics and Business 1 (November 16, 2020): 8–14. http://dx.doi.org/10.12955/peb.v1.10.
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The European Union (EU) plays an important role in the developing of clusters, defined by the European Commission as groups of specialized enterprises – often SMEs – and other related supporting actors that cooperate closely together in a particular location. As a result, the EU launched the pan-European initiative to support cluster management (European Cluster Excellence Initiative). It recognizes the performance of cluster management by quality labels such as the Bronze, Silver, and Gold issued by The European Secretariat of Cluster Analysis. With reference to these certifications, the authors analyzed the cluster management excellence by critically studying the labels granted in terms of trends, numbers, sectors, countries, and regions. Based on the gathered results, the clusters initiatives (new or already existed) can benchmark themselves. Regional, national, and European policymakers will be able to estimate how specific factors of political, geographical, demographic, access to raw materials, and level of development can influence the number of clusters, their quality of management, and cluster typology. The goal of the research is to identify the number and type (bronze, silver, and gold label) of clusters in the EU and UK. As research methods, analyzes were performed using the European Cluster Collaboration Platform (ECCP) and European Secretariat for Cluster Analysis (ESCA) data. The main results of the research show that clusters differ not only in size or activity but also in quality. Most clusters are found in the rich countries of Western Europe. Most clusters in Europe that have a bronze label are often located on the Iberian Peninsula, the Balkans, and Central Europe.
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Abdel-Fattah, Mohamed K., Elsayed Said Mohamed, Enas M. Wagdi, Sahar A. Shahin, Ali A. Aldosari, Rosa Lasaponara, and Manal A. Alnaimy. "Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt." Sustainability 13, no. 4 (February 8, 2021): 1824. http://dx.doi.org/10.3390/su13041824.
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Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.
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Devkota, Jyoti U. "Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data." e-Journal of Analysis and Applied Mathematics 2020, no. 1 (January 1, 2020): 98–117. http://dx.doi.org/10.2478/ejaam-2020-0008.
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Abstract The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.
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Getmanets, O., A. Nekos, and M. Pelikhatyi. "CLUSTER ANALYSIS AND RADIATION MONITORING OF ENVIRONMENT." Visnyk of Taras Shevchenko National University of Kyiv. Geology, no. 3 (86) (2019): 75–79. http://dx.doi.org/10.17721/1728-2713.86.11.
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Building a background radiation field on the ground on the basis of measurement data taken at a finite number of points is one of the most important tasks of radiation monitoring. The aim of the work: to study the possibility of applying cluster analysis for the tasks of radiation monitoring of the environment. Cluster analysis is a multidimensional statistical analysis. Its main purpose is to split the set of objects under study (observation points) into homogeneous groups or clusters, that is, the task of classifying data and identifying the corresponding structure in them is solved. Methods of research: the measurements of the power of the ambient dose of continuous X-ray and gamma radiation on the terrain by using the MKS-05 dosimeter "TERRA-0"; processing of the obtained data by cluster analysis methods using the computer program "Statistics-10", wherein each cluster point is characterized by three coordinates: two coordinates on the ground and the power of the ambient dose of radiation at a given point; Euclidean distance was chosen as the distance between two points. Results: after processing data using various clustering methods: the method of Complete Linkage, the method of Weighted pair-group average and the Ward's method, it was found that the results of the analysis practically coincide with each other, that proves the reliability of the application of cluster analysis for the tasks of radiation monitoring of the environment and mapping of radiation pollution. Conclusions: the concept of a "radiation cluster" was first formulated in this work, combining coordinates on a plane with an ambient dose rate;the possibility of using cluster analysis to construct a map of radiation pollution of the environment has been proved by sequential projectionfrom more connected to less connected radiation clusters onto the plane of the controlled zone. In this sense, cluster analysis is similar to the operator approach to the construction of the radiation field. For further research, it is of some interest to study the issues of integration of cluster analysis with geographic information systems.
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Patzak, Josef, František Paprštein, Alena Henychová, and Jiří Sedlák. "Comparison of genetic diversity structure analyses of SSR molecular marker data within apple (Malus × domestica) genetic resources." Genome 55, no. 09 (September 2012): 647–65. http://dx.doi.org/10.1139/g2012-054.
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The aim of this study was to compare traditional hierarchical clustering techniques and principal coordinate analysis (PCoA) with the model-based Bayesian cluster analyses in relation to subpopulation differentiation based on breeding history and geographical origin of apple (Malus × domestica Borkh.) cultivars and landraces. We presented the use of a set of 10 microsatellite (SSR) loci for genetic diversity structure analyses of 273 apple accessions from national genetic resources. These SSR loci yielded a total of 113 polymorphic SSR alleles, with 5–18 alleles per locus. SSR molecular data were successfully used in binary and allelic input format for all genetic diversity analyses, but allelic molecular data did not reveal reliable results with the NTSYS-pc and BAPS softwares. A traditional cluster analysis still provided an easy and effective way for determining genetic diversity structure in the apple germplasm collection. A model-based Bayesian analysis also provided the clustering results in accordance to traditional cluster analysis, but the analyses were distorted by the presence of a dominant group of apple genetic resources owing to the narrow origin of the apple genome. PCoA confirmed that there were no noticeable differences in genetic diversity structure of apple genetic resources during the breeding history. The results of our analyses are useful in the context of enhancing apple collection management, sampling of core collections, and improving breeding processes.
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Monés, Jordi, and Marc Biarnés. "Geographic atrophy phenotype identification by cluster analysis." British Journal of Ophthalmology 102, no. 3 (July 20, 2017): 388–92. http://dx.doi.org/10.1136/bjophthalmol-2017-310268.
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Background/aimsTo identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis.MethodsThis was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared.ResultsData were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm2/year, respectively, p=0.0005).ConclusionCluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern.
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Guo, Xiaogang, Zhijie Xu, Jianqin Zhang, Jian Lu, and Hao Zhang. "An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data." ISPRS International Journal of Geo-Information 9, no. 2 (February 22, 2020): 128. http://dx.doi.org/10.3390/ijgi9020128.
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Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found.
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Novak, I. P. "Basic vocabulary of the Karelian and Vepsian languages in the linguistic and geographical aspect." Bulletin of Ugric studies 11, no. 1 (2021): 90–101. http://dx.doi.org/10.30624/2220-4156-2021-11-1-90-101.
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Introduction: Karelian and Vepsian vocabulary has been collected and studied by linguists from Russia and Finland for two centuries. An invaluable source for research in the dialectology of the North-East group of the Baltic-Finnish languages is the «Comparative and Onomasiological Dictionary of the Karelian, Vepsian and Sami Languages» (2007). The dictionary was prepared by staff of the Institute of Linguistics, Literature and History of the Karelian Research Centre of the Russian Academy of Sciences using field research data from 1979–1981. The article reports the main results of applying the statistical method of cluster analysis to the dictionary entries. Objective: the analysis of the basic vocabulary of the dialects of the Karelian and Vepsian languages in the linguistic and geographical aspect using the statistical method of cluster analysis (dialectometry method). Research materials: pre-encoded for being uploaded to the clustering software database lexical data from the «Comparative and Onomasiological Dictionary of the Karelian, Vepsian and Sami Languages» (about 43 thousand units). Results and novelty of the research: the scientific novelty of the research is the application of the statistical method of cluster analysis to large volumes of pre-encoded lexical dialect material. The results of the calculation confirm the conclusions made by linguists earlier regarding the unity of the Vepsian and Karelian languages, as well as the presence of a clear border between them. The question of determination of the linguistic status of the Ludic dialects, which has been the subject of discussions among Russian and Finnish linguists for decades, is resolved in favor of the Karelian dialect on the basis of the material involved in the analysis. The boundaries between clusters outlined by the clustering program for the Vepsian language coincided with its dialect classification. On the Karelian part of the final map, the main bundle of isoglosses shifted north of the border between the dialects of the language, which indicates a more mobile character of its lexical level. The results presented in the article and the method of obtaining them will be later used to develop a linguistically grounded classification of Karelian language dialects.
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Hyde, Richard, Ryan Hossaini, and Amber A. Leeson. "Cluster-based analysis of multi-model climate ensembles." Geoscientific Model Development 11, no. 6 (June 4, 2018): 2033–48. http://dx.doi.org/10.5194/gmd-11-2033-2018.
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Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.
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Lu, Y. L., C. W. Liu, J. W. Li, and J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.
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Abstract. In recent years, with high accuracy, high frequency, considerable coverage of remote sensing images, map tiles, video surveillance, web crawlers, social networking platforms and other types of spatiotemporal data have exploded in geometric progression. Human society has come into the era of big data in time and space. In view of the characteristics of multi-attribute, multi-dimensional, multisource and heterogeneous spatiotemporal big data, how to make use of the emerging information technology means, combined with the geographic information data analysis means, the rapid mining and utilization of spatiotemporal big data has become a key problem to be solved. Built on the background of spatiotemporal big data and the process of geospatial cognition, this paper proposes a "cell-cube" spatiotemporal object data model. This paper constructs a model system of geo-spatiotemporal big data from the aspects of data organization, data storage and data partition, and abstracts the geo-space into an infinite number of geo-cells, and the adjacent geo-cells gather around the core cells to form geographical clusters, and the geographical clusters with similar attributes are clustered into geographical blocks. At the level of data organization, the spatial and temporal characteristics of structured data and unstructured data are considered as organizational dimensions, and a multi-factor extended cube data model is proposed. In the aspect of data storage, the organization model is further abstracted into the cell-cube structure of distributed data warehouse, and then the spatiotemporal data is stored uniformly. At the level of data segmentation, the mathematical table and space calculation method of multi-feature extended cube are proposed, and the geographical cell data division model based on connection is established. It solves the organization and management problem of spatiotemporal big data, provides a more complete data organization framework and solution for the application of geo-spatiotemporal big data, and promotes the development of deep mining of spatiotemporal extensive data in GIS. And to achieve space-time big data in the geographical space microscopic and the macroscopic unification cognition.
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Burgher, Karen L., Andrew R. Jamieson, and Xuewen Lu. "Genetic Relationships among Lowbush Blueberry Genotypes as Determined by Randomly Amplified Polymorphic DNA Analysis." Journal of the American Society for Horticultural Science 127, no. 1 (January 2002): 98–103. http://dx.doi.org/10.21273/jashs.127.1.98.
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Twenty-six genotypes of lowbush blueberry (Vaccinium angustifolium Aiton) representing four geographical zones (Maine, United States; New Brunswick, northern Nova Scotia, and western Nova Scotia, Canada) were selected to obtain DNA fingerprints and to estimate genetic similarity by randomly amplified polymorphic DNA analysis. The genotypes were either native accessions or selections from crosses involving native accessions as parents or grandparents. Thirty 10-base RAPD primers were initially screened; 11 proved to be polymorphic, resulting in 73 consistent RAPD bands. All 26 genotypes could be distinguished by their unique RAPD banding patterns and three unlabeled samples were correctly identified. The RAPD band data set was analyzed with Genstat5 to calculate similarity and distance matrices. Average similarity across all genotypes was 56%. Results from average linkage cluster analysis were used to construct a dendogram which demonstrated six main clusters with an average similarity linkage of 70%. The selection `Fundy' and its parent `Augusta' clustered at 77% similarity. The corresponding principal coordinate analysis supported the clusters and identified two distinct outliers. There was a small association by geographic grouping for five genotypes from Maine. It was concluded that RAPD analysis is a useful tool for genotypic identification and estimates of genetic similarity in lowbush blueberry.
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Belenov, Nikolai V. "Geographical vocabulary of the Shilan dialect of the Erzya-Mordovian language." Finno-Ugric World 12, no. 4 (December 25, 2020): 358–67. http://dx.doi.org/10.15507/2076-2577.012.2020.04.358-367.
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Introduction. The article presents the results of research of the geographical vocabulary of the Shilan dialect, one of the Erzya-Mordovian dialects of the Samara region, common among Erzya population of Shilan village in Krasnoyarsk region. The dialect belongs to rare Mordovian dialects of the Samara Volga region that were formed in the region since the middle of the XIX century, and therefore its research is of extra interest. Materials and Methods. The research methods are determined by the purpose and objectives of the study. The analysis of the geographical vocabulary of the Shilan dialect is carried out with the involvement of relevant items made in other Mordovian dialects of Samara region, adjacent territories of neighboring regions, as well as other territories of settlement of the Mordovians. Data on geographical vocabulary of the dialect introduced into research for the first time. The main source materials for the article is based on field studies in Silane village during the field seasons in 2017 and 2020, as well as in other Erzya-Mordovian and Moksha-Mordovian villages of Samara region and adjacent territories in 2015 – 2020. Results and Discussion. The study showed that the geographical vocabulary of the Shilan dialect of the Erzya-Mordovian language is significantly different from the corresponding lexical clusters in other dialects of the Mordovian region, which can be explained by natural geographical conditions surrounding Shilan village and the original composition of this lexical cluster of Erzya immigrants who founded this village. Conclusion. The analysis of the geographical vocabulary of the Shilan dialect allowed, on the one hand, to identify specific features of this cluster that distinguish it from the corresponding materials of other Mordovian dialects of the region, and, on the other hand, to identify common isoglosses between it and a number of the Erzya-Mordovian dialects of the Samara Volga region.
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Newnham, R. M. "Cluster analysis: An application in forest management planning." Forestry Chronicle 68, no. 5 (October 1, 1992): 628–33. http://dx.doi.org/10.5558/tfc68628-5.
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The basic principle of cluster analysis is illustrated with a simple example. The method is used to aggregate stands into clusters, each of which is relatively homogeneous and geographically compact but distinct from the remainder. Such aggregation is required to convert the output from a harvest scheduling model to a form suitable as input for a one-year, operational planning model. An application of the method, based on data from the Iroquois Falls Management Unit in northern Ontario, is given.
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Erlebach, Martin, Marián Halás, Jan Daniel, and Pavel Klapka. "Is there congruence in the spatial patterns of regions derived from scalar and vector geographical information?" Moravian Geographical Reports 27, no. 1 (March 1, 2019): 2–14. http://dx.doi.org/10.2478/mgr-2019-0001.
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Abstract Selected traits of the spatial organisation of a geographical environment which stem from two types of human behaviour (locational and interactive) are examined in this paper. An attempt is made to find and account for similarities in the spatial patterns of scalar and vector geographical data. In doing so, the paper analyses a core-periphery dichotomy, based on socio-economic information, and travel-to-work patterns. The paper uses the concept of a region as an integrating and focusing framework for the study. Formal regions (peripheral areas) are defined through the application of principal components analysis and cluster analysis; functional regions are defined by a standard rule-based regionalisation algorithm. The territory of the Czech Republic is used as an area for testing the basic hypotheses. The results show that there is some form of interrelationship and complementarity between the spatial distribution of scalar data and vector data, i.e. between spatial structure and spatial interaction patterns, which together form the spatial organisation of a geographical environment.
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Sarturi, Greici, Carlos Augusto França Vargas, João Maurício Gama Boaventura, and Silvio Aparecido dos Santos. "Competitiveness of clusters." International Journal of Emerging Markets 11, no. 2 (April 18, 2016): 190–213. http://dx.doi.org/10.1108/ijoem-11-2013-0195.
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Purpose – The purpose of this paper is to deepen the discussion regarding the competitiveness of clusters based on a theoretical and empirical study that compares the level of competitiveness of the Brazilian wine cluster located in Serra Gaúcha with the competitiveness of the Chilean cluster located in Valle del Maule. Design/methodology/approach – A qualitative-descriptive approach was applied to the study, and data collection was conducted through secondary sources. Findings – The analysis employed a competitiveness analysis model consisting of 11 competitiveness factors. The Chilean cluster presented a higher level of competitiveness in four competitiveness factors (“scope of viable and relevant business,” “introduction of new technologies,” “balance with no privileged positions” and “oriented strategy”), while the Brazilian cluster presented a higher level of competitiveness in three competitiveness factors (“concentration,” “cooperation” and “replacement”). For four of the competitiveness factors of the model, both clusters presented similar levels of competitiveness. Practical implications – By comparing the two wine clusters, it was possible to identify aspects that can be improved to increase competitiveness, especially in the Brazilian cluster. These aspects include, first, the need for bottle manufacturers in Serra Gaúcha, which would have a positive impact on production costs; second, the expansion of the geographical indication registration for the entire Serra Gaúcha region, resulting in an enhanced image of Brazilian wine abroad; and third, greater incentives for exports, which would result in an increase in market share. Originality/value – The paper proposes an explanation for the superior level of competitiveness of the Chilean cluster regarding the “scope of viable and relevant business,” “balance with no privileged positions,” “introduction of new technologies” and “strategy focussed on cluster development.” In terms of its contribution, the study developed additional metrics for the model adopted, which can be used for the competitive analysis of other agribusiness clusters.
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Aliyu, O. M., and J. A. Awopetu. "Multivariate Analysis of Cashew (Anacardium occidentale L.) Germplasm in Nigeria." Silvae Genetica 56, no. 1-6 (December 1, 2007): 170–79. http://dx.doi.org/10.1515/sg-2007-0026.
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Abstract Multivariate analyses were carried out on fifty-nine germplasm accessions of cashew derived from both local and exotic populations established at the research plots of Cocoa Research Institute of Nigeria (CRIN), Ibadan, southwestern Nigeria, to assess the extent of variability and pattern of genetic diversity among these cashew populations. Data collected on 36 quantitative and 33 qualitative plant characters were subjected to taximetric tools of Euclidean distance of complete linkage (furthest neighbour) and principal component analysis (PCA). The multivariate analyses tentatively grouped the selections into four distinct morphogenetically diverse clusters. The groupings appear to be a function of origin, eco-geographical distribution, genetic and/or agronomic affinity of the selections. Brazilian populations distinctly clustered together in two major groups while local clones and Indian selections dominated the other two major clusters with each group having its unique fruiting and tree growth habits. The clustering pattern at sub-cluster levels clearly reflects affinity of each genetic population. The principal component analysis and the potency indices showed that fruit characters are the most discriminating parameters for delineating cashew at the varietal level.
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Zhang, Jinting, Xiu Wu, and T. Edwin Chow. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties." International Journal of Environmental Research and Public Health 18, no. 11 (May 22, 2021): 5541. http://dx.doi.org/10.3390/ijerph18115541.
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As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).
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Lachkhem, Yacine, Étienne Minvielle, and Stéphane Rican. "Geographic Variations of Stroke Hospitalization across France: A Diachronic Cluster Analysis." Stroke Research and Treatment 2018 (July 18, 2018): 1–6. http://dx.doi.org/10.1155/2018/1897569.
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Background. This study evaluates the clustering of hospitalization rates for stroke and compares this clustering with two different time intervals 2009-2010 and 2012-2013, corresponding to the beginning of the French National Stroke Plan 2010–2014. In addition, these data will be compared with the deployment of stroke units as well as socioeconomic and healthcare characteristics at zip code level. Methods. We used the PMSI data from 2009 to 2013, which lists all hospitalizations for stroke between 2009 and 2013, identified on the most detailed geographic scale allowed by this database. We identify statistically significant clusters with high or low rates in the zip code level using the Getis-Ord statistics. Each of the significant clusters is monitored over time and evaluated according to the nearest stroke unit distance and the socioeconomic profile. Results. We identified clusters of low and high rate of stroke hospitalization (23.7% of all geographic codes). Most of these clusters are maintained over time (81%) but we also observed clusters in transition. Geographic codes with persistent high rates of stroke hospitalizations were mainly rural (78% versus 17%, P < .0001) and had a least favorable socioeconomic and healthcare profile. Conclusion. Our study reveals that high-stroke hospitalization rates cluster remains the same during our study period. While access to the stroke unit has increased overall, it remains low for these clusters. The socioeconomic and healthcare profile of these clusters are poor but variations were observed. These results are valuable tools to implement more targeted strategies to improve stroke care accessibility and reduce geographic disparities.
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Solohub, Yurii, Sergey Uliganets, and Olha Bezpala. "DEVELOPMENT OF THE CAPITAL SOCIO-GEOGRAPHICAL REGION SETTLEMENT SYSTEM AS A FACTOR FOR THE FORMATION OF THE REGION TOURIST MARKET." GEOGRAPHY AND TOURISM, no. 53 (2019): 84–91. http://dx.doi.org/10.17721/2308-135x.2019.53.84-91.
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Main goal: To analyze the level of the urban settlement system development of the Capital Socio-Geographical region by means of a cluster analysis method and by selecting the optimal number of capacitive indicators. It is assumed that the most significant characteristics, may be the most important and have a determining function. Methodology: The use of special statistical and mathematical methods of research, in particular, the method of cluster analysis is the basis of the study. This method has gained wide popularity for the study of both the general socio-economic development of the administrative-territorial units of the state and the corresponding systems of settlement of different taxonomic ranks. Cluster analysis is a research tool for analyzing data to solve classification problems. Its purpose is to sort cases into groups or clusters in such a way that the degree of dependency is strong between members within one cluster and weak between members of different clusters. The process of clustering involves the selection of optimal indicators, which most fully and objectively reflect the situation of the manifestation of a phenomenon in the studied area.Results: It is established that the presence of agglomerated settlements around the agglomeration center, namely the city of Kyiv, significantly increase its concentration potential, which leads to an increase in the area of both direct and indirect influence of the city center. Thus, the zone of influence of the city of Kyiv is not limited to the boundaries of the administrative Kyiv region, but extends beyond it, involving the territories of Chernihiv, Zhytomyr, Cherkasy and, to a lesser extent, Vinnitsa and Poltava regions. Scientific novelty: The clusterization of administrative-territorial units of the Capital Socio-Geographical region is carried out. Clustering was based on the degree of manifestation in them of the main indicators of the development of regional urban settlement systems.It is revealed that the presence of agglomerated settlements around the agglomeration center, the city of Kiev, significantly increase its concentration potential, which leads to an increase in the area of both direct and indirect influence of the city center. Thus, the zone of influence of the city of Kyiv is no longer confined to the boundaries of the administrative Kyiv region, but extends beyond it, involving the territories of Chernihiv, Zhytomyr, Cherkasy and, to a lesser extent, Vinnytsia and Poltava regions. The degree of localization of the urban population of the district and the cluster analysis of its administrative-territorial units in accordance with the levels of development of their settlement systems were considered to present the situation regarding the concentration of urban population of the Capital Socio-Geographical region. Practical relevance: Publication materials can be used in the development of measures to optimize the settlement system of the Capital Socio-Geographical region and to adjust the administrative and territorial reform of the state.
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Di Martino, Ferdinando, Vincenzo Loia, and Salvatore Sessa. "Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/808361.
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We present a method by using the hierarchical cluster-based Multispecies particle swarm optimization to generate a fuzzy system of Takagi-Sugeno-Kang type encapsulated in a geographical information system considered as environmental decision support for spatial analysis. We consider a spatial area partitioned in subzones: the data measured in each subzone are used to extract a fuzzy rule set of above mentioned type. We adopt a similarity index (greater than a specific threshold) for comparing fuzzy systems generated for adjacent subzones.
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Brustad, M., CL Parr, M. Melhus, and E. Lund. "Childhood diet in relation to Sámi and Norwegian ethnicity in northern and mid-Norway – the SAMINOR study." Public Health Nutrition 11, no. 2 (February 2008): 168–75. http://dx.doi.org/10.1017/s1368980007000432.
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AbstractObjectiveThe purpose of this work was to identify dietary patterns in the past using cluster analysis of reported diet in childhood, and to assess predictors for dietary patterns in relation to ethnicity in the population in the Sámi core areas in Norway. The Sámis are an indigenous population living in the border areas of Norway, Sweden, Finland and Russia.DesignPopulation-based, cross-sectional study, using self-administered questionnaires. A food-frequency questionnaire covering selected food items eaten in childhood was used. The questionnaire also provided data on ethnicity.Subjects and settingThis study was based on data collected from 7614 subjects participating in The Population Based Study of Health and Living Conditions in Areas with a Mixed Sámi and Norwegian Population (the SAMINOR study) who grew up in the SAMINOR geographical areas, i.e. areas with mixed Sámi and Norwegian populations in Norway.ResultsFour dietary clusters were identified: a reindeer meat cluster; a cluster with high intakes of fish, traditional fish products and mutton, in addition to food sources from the local environment; a Westernised food cluster with high intakes of meat balls and sausages; and a cluster with a high intake of fish, but not any other foods in the questionnaire. The cluster distribution differed by ethnicity, but the effect of ethnicity on diet differed by coastal and inland residence.ConclusionOur study has shown that data gathered through the limited questionnaire could be used to group the study sample into different dietary clusters, which we believe will be useful for further research on relationships between diet in childhood and health in the Sámi core areas in Norway.
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Mazdziarz, Artur. "Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method." Journal of Telecommunications and Information Technology 2 (June 29, 2018): 95–102. http://dx.doi.org/10.26636/jtit.2018.124518.
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Event correlation and root cause analysis play a fundamental role in the process of troubleshooting all technical faults and malfunctions. An in-depth, complicated multiprotocol analysis can be greatly supported or even replaced by a troubleshooting methodology based on data analysis approaches. The mobile telecommunications domain has been experiencing rapid development recently. Introduction of new technologies and services, as well as multivendor environment distributed across the same geographical area create a lot of challenges in network operation routines. Maintenance tasks have been recently becoming more and more complicated, time consuming and require big data analyses to be performed. Most network maintenance activities are completed manually by experts using raw network management information available in the network management system via multiple applications and direct database queries. With these circumstances considered, identification of network failures is a very difficult, if not an impossible task. This explains why effective yet simple tools and methods providing network operators with carefully selected, essential information are needed. Hence, in this paper efficient approximated alarm correlation algorithm based on the k-means cluster analysis method is proposed.
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Elexa, Ľuboš, Ľubica Lesáková, Vladimíra Klementová, and Ladislav Klement. "Identification of prospective industrial clusters in Slovakia." Engineering Management in Production and Services 11, no. 2 (July 30, 2019): 31–42. http://dx.doi.org/10.2478/emj-2019-0009.
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Abstract Clusters became an integral part of regional policies intended to build and strengthen competitive advantages within specifically identified geographical areas. They are still considered crucial for economic development and employment, although their orientation has slightly changed as the distance and geographical boundaries lost their importance. This article analyses crucial regional data that indicates potentially beneficial economic concentrations as an assumption for the preparation of prospective clusters in Slovakia. Potential clusters were identified based on significant employment concentrations of particular regional industries that appear extraordinary when compared with national employment and the dynamic development within the selected time frame. Prospective clusters were identified, and opportunities of their development were described, including the harmonisation with the current regional and urban strategy. Analysing absolute and relative quantities in employment, sections and divisions of SK NACE were used for the proper identification of industries. The location quotient served as a tool for the spatial concentration of employment in the Banská Bystrica region, the threshold value for the selection of cluster candidates was set to 2. The shift–share analysis was used for the identification of long-term changes in employment, and 10% of the most dynamic industries were presented at the level of divisions once and then, at the level of sections of SK NACE. Forestry and logging, the manufacture of wood products and the manufacture of basic metals were confirmed by both methods as significant concentrations. The result partially corresponded with the previously active and currently inactive cluster in Banská Bystrica, which was focused on mechanical engineering, still significant when considering numbers of companies and employees as well as sales. Forestry was the most concentrated industry, while the wholesale and retail trades were the most dynamic. Forestry, logging and manufacture of wood products might be strongly interlinked with the current entrepreneurial and social strategy of self-governing regions that is still at the stage of potential cluster identification and fitting to its priorities. The article assumed basic quantitative methods utilised for the identification of prospective clusters. It confirmed the practicality of their application, the gravity of data processing and also certain possible limitations due to the extraordinary focus on the employment concentration. According to the analysis and gained results, the former cluster in the Banská Bystrica region was confirmed as the potentially significant actor in the regional policy (although, currently, having no industrial or public interest) and the new cluster candidates were identified. Outcomes indicated the need to continue the research with a more detailed examination of qualitative aspects that could complete the effort by focusing on clusters not only having higher employment statistics but also the support from regional institutions, also reflecting the preferences of businesses.
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HAN, Jing, Ming GAO, and Yawen SUN. "Measurement of City Clusters’ Economic Growth Effects and Analysis of the Influencing Factors." Chinese Journal of Urban and Environmental Studies 08, no. 01 (March 2020): 2050006. http://dx.doi.org/10.1142/s2345748120500062.
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Based on the panel data of 202 prefecture-level cities within 14 national-level city clusters in China from 2007 to 2016, we established a dynamic panel model to measure the economic growth effects of city clusters and analyzed the main influencing factors. The results show that: (i) Technology has a significant impact on the economic growth of city clusters; the narrowing development gap between regions can help city clusters produce good economic growth effects; the city clusters, if more agglomerated, can help better utilize factors, and thus promote coordinated regional development. (ii) City clusters with multiple central cities boast a stronger engine of economic growth, and the impacts of factors such as technology and clustering degree on their economic growth are more noticeable. (iii) Geographical factors will also affect city clusters’ economic growth. The economic growth of city clusters in Southern China has been more strongly powered by the factors such as technology, clustering degree and human capital than those in Northern China. From the spatial perspective and by using the threshold panel method, we further explored the mechanisms with which the central cities within a city cluster can influence economic growth depending on their accessibility. The results manifest that the more accessible the central cities within a city cluster are, the stronger role they can play in leading and driving the economic growth of surrounding areas. In the future, it is important to promote the transformation of single-core and dual-core city clusters into multi-core city clusters, and give full play to the role of central cities in leading the development of surrounding areas. It is also necessary to vigorously develop technology and transportation to further facilitate the high-quality growth of city clusters.
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TURNBULL, BRUCE W., ERIC J. IWANO, WILLIAM S. BURNETT, HOLLY L. HOWE, and LARRY C. CLARK. "MONITORING FOR CLUSTERS OF DISEASE: APPLICATION TO LEUKEMIA INCIDENCE IN UPSTATE NEW YORK." American Journal of Epidemiology 132, supp1 (July 1, 1990): 136–43. http://dx.doi.org/10.1093/oxfordjournals.aje.a115775.
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Abstract The authors propose a procedure for the detection of significant clusters of chronic diseases, with particular reference to cancer. The procedure allows for variations in population density and avoids the problem of “post hoc” formation of hypotheses or self-defined populations. This accounts for several of the principal problems of cluster evaluations. The techniques are practical but “computer-intensive.” The procedure, termed the “duster evaluation permutation procedure”, is applied to leukemia incidence data for an Upstate New York region obtained from the New York State Cancer Registry and census files. Comparisons are made with two other recently proposed clustering methods, namely the U-statistic method of WhKtemore et al. (Biometrika 1987;74:631–7) and the “geographical analysis machine” of Openshaw et al. (Lancet 1988; 1:272–3). Routine examination of disease occurrence with the cluster evaluation permutation procedure would allow state health officials to prioritize case investigations and to respond in a timely and efficient manner to inquiries of reported clusters.
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Hatami, Maleki, Kiomars Rouhrazi, Gholam Khodakaramian, and Naser Sabaghnia. "Characterization and molecular diversity of Iranian rhizobia isolated from faba bean." Genetika 50, no. 1 (2018): 231–42. http://dx.doi.org/10.2298/gensr1801231m.
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The diversity and phylogeny of 30 rhizobia isolated from nodules of faba bean plants grown on 5 geographic regions located in the East Azerbaijan province of Iran were examined using rep-PCR fingerprinting, sequence analysis of 16S rRNA accompanied with nodC genes. Based on cluster analysis of rep-PCR fingerprints, faba bean rhizobia isolates were differentiated into five clusters (A to E) at 80% similarity level. The cophenetic correlation coefficient for the dendrogram obtained from the combined dataset of BOX and ERIC primers was 0.942. The percentage of polymorphic loci was 59.2% using the BOX-PCR primer and 67.3% using the ERIC-PCR primers. The data obtained by rep-PCR fingerprinting showed high apparent correlation between genetic diversity and geographical origin of the isolates. The phylogenetic analysis based on 16S rRNA and nodC sequences showed that representative isolates were closely related to R. leguminosarum bv. viciae and R. fabae. To the best of our knowledge, this is first report of isolation and characterization of R. fabae from Iran.
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Jasim, Abbas F., Hao Wang, and Thomas Bennert. "Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 13, 2019): 332–48. http://dx.doi.org/10.1177/0361198119853557.
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Truck traffic is one of the significant inputs in design and analysis of pavement structures. This paper focuses on comprehensive cluster analysis of truck traffic in New Jersey for implementation of mechanistic-empirical pavement design. Multiple year traffic data were collected from a large number of weigh-in-motion stations across New Jersey. Statistical analysis was first conducted to analyze directional and temporal (yearly) variations of traffic data. Hierarchical cluster analysis was conducted and three optimum clusters were found for axle load spectra (single, tandem, tridem), vehicle class distribution, and axle/truck ratio, respectively. Road functional classifications were employed to identify different clusters as no common geographic trend could be perceived. The results illustrate that the predicted performance using the site-specific traffic data is comparable with that using the traffic cluster for the selected 10 sites. Among four different traffic inputs, the cluster traffic inputs generated the closest predictions of pavement life as compared with those using site-specific traffic input and the default traffic inputs yielded the highest error. It is recommended to use traffic clusters in mechanistic-empirical pavement design when site-specific data is unavailable.
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Porębski, Andrzej. "Application of Cluster Analysis in Research on the Spatial Dimension of Penalised Behaviour." Acta Universitatis Lodziensis. Folia Iuridica 94 (March 30, 2021): 97–120. http://dx.doi.org/10.18778/0208-6069.94.06.
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This paper is focused on some of the possibilities of the use of cluster analysis (clustering) in criminology and the sociology of law. Cluster analysis makes it possible to divide even a large dataset into a specified number of subsets in such a way that the resulting subsets are as homogenous as possible, and at the same time differ from each other substantially. When analysing geographical data, e.g. describing the location of crimes, the result of cluster analysis is a division of a territory into a certain number of coherent areas based on an objective criterion. The division of the territory under study into smaller parts is more insightful when the clustering method is applied compared to an arbitrary division into official administrative units.
The paper provides a detailed description of hierarchical cluster analysis methods and an example of using the Ward’s hierarchical method and the k-means combinational method to divide data on crime reports in the city of Baltimore between 2014 and 2019. The analysis demonstrates that the resulting division differs considerably from the administrative division of Baltimore, and that increasing the number of groups emerging as a result of cluster analysis leads to an increase of variance of variables describing the structure of crime in individual parts of the city. The divisions obtained using clustering are used to verify the hypothesis on differences in crime structure in different areas of Baltimore.
The main aim of the paper is to encourage the use of modern methods of data analysis in social sciences and to present the usefulness of cluster analysis in criminology and the sociology of law research.
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Sabah, L., and M. Şimşek. "INVESTIGATION OF SPATIAL DATA WITH OPEN SOURCE SOCIAL NETWORK ANALYSIS AND GEOGRAPHIC INFORMATION SYSTEMS APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 81–83. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-81-2017.
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Social networks are the real social experience of individuals in the online environment. In this environment, people use symbolic gestures and mimics, sharing thoughts and content. Social network analysis is the visualization of complex and large quantities of data to ensure that the overall picture appears. It is the understanding, development, quantitative and qualitative analysis of the relations in the social networks of Graph theory. Social networks are expressed in the form of nodes and edges. Nodes are people/organizations, and edges are relationships between nodes. Relations are directional, non-directional, weighted, and weightless. The purpose of this study is to examine the effects of social networks on the evaluation of person data with spatial coordinates. For this, the cluster size and the effect on the geographical area of the circle where the placements of the individual are influenced by the frequently used placeholder feature in the social networks have been studied.
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Vilinová, Katarína. "Spatial Autocorrelation of Breast and Prostate Cancer in Slovakia." International Journal of Environmental Research and Public Health 17, no. 12 (June 20, 2020): 4440. http://dx.doi.org/10.3390/ijerph17124440.
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Cancer is one of the dominant causes of death in the Slovak population. Monitoring the course of the cancer death rate in Slovakia can be considered as a relevant subject for geographical research. Relatively little is known about the geographic distribution of breast and prostate cancer incidence in Slovakia. In the submitted paper, it is hypothesized that breast and prostate cancer in the examined territory are characterized by different intensities, incidences, and spatial differences. The spatial patterns of breast and prostate cancer in Slovakia were examined by means of spatial autocorrelation analyses with the Local Moran’s I and Anselin Local Moran’s statistics. Data on standardized death rates of breast and prostate cancer in Slovakia between 2001 and 2018 were used. Prostate cancer in men and breast cancer in women show a positive statistically significant Global Moran’s I, whose values indicate a tendency to cluster. The Anselin Local Moran’s I analysis indicates significant clusters of breast cancer in the western part of Slovakia, and prostate cancer clusters mostly in the central part of Slovakia. The findings we have obtained in this study may help us investigate further hypotheses regarding the causes and identification of spatial differences in breast and prostate cancer incidence. Our findings might stimulate further research into the possible causes which underlie the clusters.
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Nguyen, Margaret B. "Aligning Partners in Pediatric Health: Using Geographical Information Systems to Plan Community Coalitions." Journal of Primary Care & Community Health 11 (January 2020): 215013272094051. http://dx.doi.org/10.1177/2150132720940513.
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Introduction: Compared with adults, children have higher emergency department (ED) utilization for asthma exacerbation. While community coalitions have been shown to prevent ED visits for asthma, there is little guidance on where to best implement these efforts. Geographical information systems (GIS) technology can help in the selection and coordination of potential coalition partners. This report proposes a model to be used by clinicians and child health equity advocates to strategize high-impact community health interventions. The aims were to identify the clusters of ED utilization for pediatric asthma, evaluate sociodemographic features of the population within the clusters, and identify potential primary care and school community partners. Methods: This model uses ED visit data from 450 nonmilitary California hospitals in 2012. We obtained ZIP code–level counts and rates for patients younger than 18 years discharged with a diagnosis code of 493 for asthma conditions from the California Office of Statewide Health Planning and Development’s Open Portal. We applied GIS spatial analysis techniques to identify statistically significant cluster for pediatric asthma ED utilization. We then locate the candidate community partners within these clusters. Results: There were 181 720 ED visits for asthma for all age groups in 2012 with 70 127 visits for children younger than 18 years. The top 3 geographic clusters for ED utilization rates were located in Fresno, Inglewood, and Richmond City, respectively. Spatial analysis maps illustrate the schools located within 0.5– and 1-mile radii of primary care clinics and provide a visual and statistical description of the population within the clusters. Conclusion: This study demonstrates a model to help clinicians understand how GIS can aid in the selection and creation of coalition building. This is a potentially powerful tool in the addressing child health disparities.