Relevant bibliographies by topics / Cluster analysis][Geographical data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

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

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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

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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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Dissertations / Theses on the topic "Cluster analysis][Geographical data"

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Eslava-Gomez, Guillermina. "Projection pursuit and other graphical methods for multivariate data." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236118.

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Ngai, Wang-kay, and 倪宏基. "Cluster analysis on uncertain data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4218261X.

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Ngai, Wang-kay. "Cluster analysis on uncertain data." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4218261X.

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Busse, Ludwig M. Orbanz Peter Buhmann Joachim M. Buhmann Joachim M. Buhmann Joachim M. "Cluster analysis of heterogeneous rank data." Zurich : ETH Department of Computer Science, Institute of Computational Sciences, 2007. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=350.

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Yeung, Ka Yee. "Cluster analysis of gene expression data /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6986.

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Molin, Felix. "Cluster analysis of European banking data." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219597.

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Credit institutions constitute a central part of life as it is today and has been doing so for a long time. A fault within the banking system can cause a tremendous amount of damage to individuals as well as countries. A recent and memorable fault is the global financial crisis 2007-2009. It has affected millions of people in different ways ever since it struck. What caused it is a complex issue which cannot be answered easily. But what has been done to prevent something similar to occur once again? How has the business models of the credit institutions changed since the crisis? Cluster analysis is used in this thesis to address these questions. Banking-data were processed with Calinski-Harabasz Criterion and Ward's method and this resulted in two clusters being found. A cluster is a collection of observations that have similar characteristics or business model in this case. The business models that the clusters represents are universal banking with a retail focus and universal banking with a wholesale focus. These business models have been analyzed over time (2007-2016), which revealed that the credit institutions have developed in a healthy direction. Thus, credit institutions were more financially reliable in 2016 compared to 2007. According to trends in the data this development is likely to continue.
Kreditinstituten utgör en central del av livet som det ser ut idag och har gjort det under en lång tid. Ett fel inom banksystemet kan orsaka enorma skador för individer likväl som länder. Ett nutida och minnesvärt fel är den globala finanskrisen 2007-2009. Den har påverkat millioner människor på olika vis ända sedan den slog till. Vad som orsakade den är en komplex fråga som inte kan besvaras med lätthet. Men vad har gjorts för att förebygga att något liknande händer igen? Hur har affärsmodellerna för kreditinstituten ändrats sedan krisen? Klusteranalys används i denna rapport för att adressera dessa frågor. Bankdata processerades med Calinski-Harabasz Kriteriet and Wards metod och detta resulterade i att två kluster hittades. Ett kluster är en samling observationer med liknande karakteristik eller affärsmodell i detta fall. De affärsmodeller som klustrena representerar är universella banker med retail fokus samt universella banker med wholessale fokus. Dessa affärsmodeller har analyserats över tid, vilket har avslöjat att kreditinstituten har utvecklats i en hälsosam riktning. Kreditinstituten var mer finansiellt pålitliga 2016 jämfört med 2007. Enligt trender i datan så är det troligt att denna utveckling forsätter.
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Jarvis, Paul S. "Determining geographical causal relationships through the development of spatial cluster detection and feature selection techniques." Thesis, University of South Wales, 2006. https://pure.southwales.ac.uk/en/studentthesis/determining-geographical-casual-relationships-through-the-development-of-spatial-cluster-detection-and-feature-selection-techniques(7a882804-5565-44d7-8635-e59c66e2e9bc).html.

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Spatial datasets contain information relating to the locations of incidents of a disease or other phenomena. Appropriate analysis of such datasets can reveal information about the distribution of cases of the phenomena. Areas that contain higher than expected incidence of the phenomena, given the background population, are of particular interest. Such clusters of cases may be affected by external factors. By analysing the locations of potential influences, it may be possible to establish whether a cause and effect relationship is present within the dataset. This thesis describes research that has led to the development and application of cluster detection and feature selection techniques in order to determine whether causal relationships are present within generic spatial datasets. The techniques are described and demonstrated, and their effectiveness established by testing them using synthetic datasets. The techniques are then applied to a dataset supplied by the Welsh Leukaemia Registry that details all cases of leukaemia diagnosed in Wales between 1990 and 2000. Cluster detection techniques can be used to provide information about case distribution. A novel technique, CLAP, has been developed that scans the study region and identifies the statistical significance of the levels of incidence in specific areas. Feature selection techniques can be used to identify the extent to which a selection of inputs impact upon a given output. Results from CLAP are combined with details of the locations of potential causal factors, in the form of a numerical dataset that can be analysed using feature selection techniques. Established techniques and a newly developed technique are used for the analysis. Results from such analysis allow conclusions to be drawn as to whether geographical causal relationships are apparent.
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Takahashi, Atsushi. "Hierarchical Cluster Analysis of Dense GNSS Data and Interpretation of Cluster Characteristics." Kyoto University, 2019. http://hdl.handle.net/2433/244510.

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Springuel, R. Padraic. "Applying Cluster Analysis to Physics Education Research Data." Fogler Library, University of Maine, 2010. http://www.library.umaine.edu/theses/pdf/SpringuelRP2010.pdf.

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Hegazy, Yasser Ali. "Delineating geostratigraphy by cluster analysis of piezocone data." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/20506.

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Books on the topic "Cluster analysis][Geographical data"

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Geographical data analysis. Chichester: J. Wiley & Sons, 1995.

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Ren, Jinwei. Cluster analysis of induced seismicity data. Dublin: University College Dublin, 1996.

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Analysis of longitudinal and cluster-correlated data. Beachwood, OH: Institute of Mathematical Statistics, 2004.

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Laird, Nan. Analysis of Longitudinal and Cluster-Correlated Data. Beechwood OH and Alexandria VA: Institute of Mathematical Statistics and American Statistical Association, 2004. http://dx.doi.org/10.1214/cbms/1462106075.

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Bhati, M. Ishaq. Cluster effects in mining complex data. Hauppauge, N.Y: Nova Science Publisher's, 2011.

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Matthews, M. H. Geographical data: Sources, presentation and analysis. Oxford: Oxford University Press, 1989.

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Moisl, Hermann. Cluster analysis for corpus linguistics. Berlin: De Gruyter, 2015.

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Backer, E. Computer-assisted reasoning in cluster analysis. New York: Prentice Hall, 1995.

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Cluster dissection and analysis: Theory, FORTRAN programs, examples. Chichester: Horwood, 1985.

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Kaufman, Leonard. Finding groups in data: An introduction to cluster analysis. Hoboken, N.J: Wiley, 2005.

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Book chapters on the topic "Cluster analysis][Geographical data"

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Leydesdorff, Loet. "The Measurement of Synergy." In Qualitative and Quantitative Analysis of Scientific and Scholarly Communication, 135–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59951-5_7.

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AbstractWhen policy-makers call for “interdisciplinarity,” they often mean “synergy.” Problem-solving requires crossing boundaries, such as those between disciplines. However, synergy can also be generated ininter-sectorial or geographical collaborations. Synergy is indicated when the whole offers more possibilities than the sum of its parts; “interdisciplinarity” can be an instrument for creating “synergy.” Synergy can be measured as an increase of redundancy; that is, the number of options which are available, but not-yet used. Instead of asking for the synergy among pre-defined categories, such as regions, sectors, size-classes, or nations, etc., I propose to let the most synergetic combinations among (potentially heterogenous) variables emerge from the data matrix. A synergy map can be drawn showing (cluster of) available but not-yet-realized options. A computer routine is made available at https://www.leydesdorff.net/software/synergy.triads which compares all possible triads in a data matrix in terms of their contributions to the synergy in a configuration.
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Aggarwal, Charu C. "Cluster Analysis." In Data Mining, 153–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_6.

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Murtagh, Fionn, and André Heck. "Cluster Analysis." In Multivariate Data Analysis, 55–109. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_3.

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Nokeri, Tshepo Chris. "Cluster Analysis." In Data Science Revealed, 163–84. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6870-4_10.

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Steele, Brian, John Chandler, and Swarna Reddy. "Cluster Analysis." In Algorithms for Data Science, 253–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45797-0_8.

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Wierzchoń, Sławomir T., and Mieczysław A. Kłopotek. "Cluster Analysis." In Studies in Big Data, 9–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69308-8_2.

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Everitt, Brian S., and Graham Dunn. "Cluster Analysis." In Applied Multivariate Data Analysis, 125–60. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch6.

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Gatignon, Hubert. "Cluster Analysis." In Statistical Analysis of Management Data, 295–322. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1270-1_11.

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Wendler, Tilo, and Sören Gröttrup. "Cluster Analysis." In Data Mining with SPSS Modeler, 587–712. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28709-6_7.

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Gatignon, Hubert. "Cluster Analysis." In Statistical Analysis of Management Data, 453–85. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-8594-0_12.

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Conference papers on the topic "Cluster analysis][Geographical data"

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Saygın, Muhammet, and Himmet Karadal. "An Analysis of Cultural Tourism Cluster: The Case of Aksaray Province." In International Conference on Eurasian Economies. Eurasian Economists Association, 2011. http://dx.doi.org/10.36880/c02.00292.

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A business cluster is a geographic concentration of interconnected businesses, suppliers, and associated institutions in a particular field. Clusters are considered to increase the productivity with which companies can compete, nationally and globally, so the cluster arises because it provides the increase of productivity and innovation abilities; for this reason its development should be supported not only by central governments but also by the local authorities. It is a known fact that tourism, one of the most effective aims for development, is considered to be a leading factor in cultural, economical and social perspectives through cluster types. In this study, the potential of cultural tourism cluster and competitiveness of Aksaray province has been analyzed in terms of Porter’s Diamond Model. Several data sources including strategic development plan and academic literature have been taken into consideration. In this regard, findings about whether there is really a tourism cluster in Aksaray province have been stated and recommendations have been suggested to empower the innovative advantage of cultural tourism cluster.
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Audivet, Cinthia, Horacio Pinzón, Jesus García, Marlon Consuegra, Javier Alexander, and Marco Sanjuán. "Data Segmentation Criteria Assessment for Fault Detection Techniques Based on Principal Component Analysis for Natural Gas Transmission System." In ASME 2018 Power Conference collocated with the ASME 2018 12th International Conference on Energy Sustainability and the ASME 2018 Nuclear Forum. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/power2018-7479.

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Statistical analytics, as a data extraction and fault detection strategy, may incorporate segmentation techniques to overcome its underlying limitations and drawbacks. Merging both techniques shall provide a more robust monitoring structure to address the proper identification of normal and abnormal conditions, to improve the extraction of fundamental correlation among variables, and to improve the separation of both main variation and natural variation (noise) subspaces. This additional feature is key to limit the false alarm rate and to optimize the fault detection time when it is implemented on industrial applications. This paper presents an analysis to determine whether a segmentation approach, as a previous step of detection, enhances the fault detection strategies, specifically the principal component analysis performance. The data segmentation criteria assessed in this study includes two approaches: a) Sources (well) of the transmitted natural gas and b) Promigas’ natural gas pipeline division defined by the Energy and Gas Regulation Commission (CREG in Spanish). The performance assessment of segmentation criteria was carried out evaluating the false alarm rate and detection time when the natural gas transmission network presents faults of different magnitude. The results show that the implementation of a segmentation criteria provides an advantage in terms of the detection time, but it depends of the fault magnitude and the number of clusters. The detection time is improved by 25% in the case scenario I, when transition zones are considered. On the other hand, the detection time is slightly better with less than 10% in the case scenario II, where the segmentation is geographical.
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D'Agata, Rosario, and Simona Gozzo. "#immigrants project: the on-line perception of integration." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11655.

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This paper analyses the content of Twitter’s comments during the period covering the last European elections. "#immigrants" is the extraction’s keyword in different national languages. With the exception of English and French, whose extraction would be misleading, all of the other languages have been chosen to catch the geographical area of reference. We made sure to extract at least two sentences for each Welfare area. Once the data have been extracted, three different strategies have been used. The first one, dealing with both a qualitative and a quantitative assessment; the second one, analysing automatically the content of the top 10 extracted tweets during the reference period and the third one based on network analysis. Through a deep analysis of the content, three clusters have been identified: the first one dealing with the cultural risks of multiculturalism; the second one (social risks) dealing with the fear of migrants stealing job vacancies and the third one dealing with economic risks. A deep network analysis of Italian and Spanish contexts follows. What emerges is that: communication is extremely heterogeneous; in Italy there unique and duplicated edges prevails; in Spain there are more groups than in Italy, more themes covered and different kind of users and nets.
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KHASAEV, Gabibulla, Alexandr VLASOV, Dariya VASILIEVA, and Velta PARSOVA. "CRITERIA OF ECONOMIC EFFICIENCY OF LAND STOCK MANAGEMENT." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.250.

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One of preconditions for sustainable socio-economic development of the region can be observed as much as possible involvement of land resources in economic turnover and increasing of efficiency of their use. On the example of Samara region which is the subject of the Russian Federation are made proposals for establishment of criteria for assessment of economic efficiency of land management in specific area. Statistical data on collection of land payments (land tax and leasehold payment) in 27 municipalities of Samara region in 2004-2014 are analysed. There is investigated common information on Samara region - location, total land stock area, agricultural land area, distance between main city of region and territory, number of inhabitants, density of population, etc. Methods of mathematical statistics, regression and factor cluster analysis are used. During the research, methods of mathematical statistics, including correlation and regression analysis, were used. The indicators most influencing the volume of land payments are determined and is developed the model of coherence between level of payments and geographical and demographic characteristics of the municipality (according to the 2012-2014 data). The model allows to estimate the “normative level” for each region according to objective characteristics and to rank the regions according to this indicator. The level corresponding to such a ranking can be considered as a criterion for the effectiveness of land management taking into account the different potentialities of the municipal districts of the Samara region. Land payments are local taxes, therefore they are an important source of the formation of financial basis of local governments in Russian Federation and play an important role in the socio-economic development of the regions.
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Nagasawa, Kazunori, Charles R. Upshaw, Joshua D. Rhodes, Chris L. Holcomb, David A. Walling, and Michael E. Webber. "Data Management for a Large-Scale Smart Grid Demonstration Project in Austin, Texas." In ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/es2012-91198.

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This paper presents a data management scheme for the Pecan Street smart grid demonstration project in Austin, Texas. In this project, highly granular data with 15-second resolution on resource generation and consumption, including total consumption of electricity, water, and natural gas and solar generation, are collected for more than 100 homes. Furthermore, this testbed, see Figure 1, of homes represents the nation’s highest density of rooftop solar PV and electric vehicles, and includes a substantial subset of homes that are highly instrumented with meters on up to 6 sub-circuits in addition to the whole-home meter. Consequently, this demonstration project generates a one-of-a-kind dataset with excellent temporal and geographic fidelity. One consequence of this extensive dataset is that there are hundreds of parallel data streams that need to be remotely (wirelessly) collected, filtered, processed, managed, stored and analyzed to be useful for researchers. Cumulatively, they represent 100s of gigabytes of data after just a few months of collection, which represents a formidable barrier to conducting research. In partnership with the Texas Advanced Computing Center (TACC), which is an NSF-sponsored cluster of supercomputers at UT-Austin, a data collection and management scheme has been developed. For storing the data, we have built a single column oriented database that so far has shown tremendous performance benefits. This paper shows the data schema, an example of MySQL query, and a developed program for rapid and automated data extraction, analysis and display. We expect that the findings of this work will be beneficial to researchers interested in grid-scale data management.
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Rajgure, Neeraj, Eric Platon, Cristian Borcea, and Shinichi Honiden. "Geographical data collection in sensor networks with self-organizing transaction cluster-heads." In the 2009 ACM symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1529282.1529553.

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Fauvet, M. C., S. Chardonnel, M. Dumas, P. C. Scholl, and P. Dumolard. "Applying temporal databases to geographical data analysis." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795229.

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Dong, Bin, Surendra Byna, and Kesheng Wu. "Expediting scientific data analysis with reorganization of data." In 2013 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2013. http://dx.doi.org/10.1109/cluster.2013.6702675.

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Wood, Jo, Aidan Slingsby, Naz Khalili-Shavarini, Jason Dykes, and David Mountain. "Visualization of uncertainty and analysis of geographical data." In 2009 IEEE Symposium on Visual Analytics Science and Technology. IEEE, 2009. http://dx.doi.org/10.1109/vast.2009.5333965.

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dos Santos, C. K., A. G. Evsukoff, and B. S. L. P. de Lima. "Cluster analysis in document networks." In DATA MINING 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/data080101.

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Reports on the topic "Cluster analysis][Geographical data"

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Chandar, Bharat, Ali Hortaçsu, John List, Ian Muir, and Jeffrey Wooldridge. Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings. Cambridge, MA: National Bureau of Economic Research, October 2019. http://dx.doi.org/10.3386/w26389.

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Jelski, Daniel A., Z. C. Wu, and Thomas F. George. An Inquiry into the Structure of the Si60 Cluster: Analysis of Fragmentation Data. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada215488.

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Mayo, Jackson R., W. Philip, Jr Kegelmeyer, Matthew H. Wong, Philippe Pierre Pebay, Ann C. Gentile, David C. Thompson, Diana C. Roe, Vincent De Sapio, and James M. Brandt. A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/992310.

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Harter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan, and Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, May 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.

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The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define strata focuses on relationships among stratification variables. Decision trees, regressions, and correlation approaches focus more on relationships between the stratification variables and prior outcome data, which may be available for just a sample of units. Using both principal components/cluster analysis and decision trees, we stratified primary sampling units for the 2009 Residential Energy Consumption Survey and compared the resulting strata.
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Kumar, Indraneel, Lionel Beaulieu, Annie Cruz-Porter, Chun Song, Benjamin St. Germain, and Andrey Zhalnin. An Assessment of the Workforce and Occupations in the Highway, Street, and Bridge Construction Industries in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284315018.

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This project explores workforce and occupations within the highway, street, and bridge construction industries (NAICS 237310) in Indiana. There are five specific deliverable comprised of three data reports, one policy document, and a website. The first data report includes an assessment of the workforce based on the eight-part framework, which are industry, occupations, job postings, hard-to-fill jobs, Classification of Instructional Programs (CIP), GAP Analysis, compatibility, and automation. The report defines a cluster followed by a detailed analysis of the occupations, skills, job postings, etc., in the NAICS 237310 industry in Indiana. The report makes use of specialized labor market databases, such as the Economic Modeling Specialists International (EMSI), CHMURA JobsEQ, etc. The analysis is based only on the jobs covered under the unemployment insurance or the Quarterly Census of Employment and Wages (QCEW) data. The second data report analyzes jobs to jobs flows to and from the construction industry in Indiana, with a particular emphasis on the Great Recession, by utilizing the Bureau of Labor Statistics (BLS) data. The third data report looks into the equal employment opportunity or Section 1391 and 1392 data for Indiana and analyzes specific characteristics of that data. The policy report includes a set of recommendations for workforce development for INDOT and a summary of the three data reports. The key data on occupations within the NAICS 237310 are provided in an interactive website. The website provides a data dashboard for individual INDOT Districts. The policy document recommends steps for development of the highways, streets and bridges construction workforce in INDOT Districts.
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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, February 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.
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Hunter, Fraser, and Martin Carruthers. Iron Age Scotland. Society for Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.09.2012.193.

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The main recommendations of the panel report can be summarised under five key headings:  Building blocks: The ultimate aim should be to build rich, detailed and testable narratives situated within a European context, and addressing phenomena from the longue durée to the short-term over international to local scales. Chronological control is essential to this and effective dating strategies are required to enable generation-level analysis. The ‘serendipity factor’ of archaeological work must be enhanced by recognising and getting the most out of information-rich sites as they appear. o There is a pressing need to revisit the archives of excavated sites to extract more information from existing resources, notably through dating programmes targeted at regional sequences – the Western Isles Atlantic roundhouse sequence is an obvious target. o Many areas still lack anything beyond the baldest of settlement sequences, with little understanding of the relations between key site types. There is a need to get at least basic sequences from many more areas, either from sustained regional programmes or targeted sampling exercises. o Much of the methodologically innovative work and new insights have come from long-running research excavations. Such large-scale research projects are an important element in developing new approaches to the Iron Age.  Daily life and practice: There remains great potential to improve the understanding of people’s lives in the Iron Age through fresh approaches to, and integration of, existing and newly-excavated data. o House use. Rigorous analysis and innovative approaches, including experimental archaeology, should be employed to get the most out of the understanding of daily life through the strengths of the Scottish record, such as deposits within buildings, organic preservation and waterlogging. o Material culture. Artefact studies have the potential to be far more integral to understandings of Iron Age societies, both from the rich assemblages of the Atlantic area and less-rich lowland finds. Key areas of concern are basic studies of material groups (including the function of everyday items such as stone and bone tools, and the nature of craft processes – iron, copper alloy, bone/antler and shale offer particularly good evidence). Other key topics are: the role of ‘art’ and other forms of decoration and comparative approaches to assemblages to obtain synthetic views of the uses of material culture. o Field to feast. Subsistence practices are a core area of research essential to understanding past society, but different strands of evidence need to be more fully integrated, with a ‘field to feast’ approach, from production to consumption. The working of agricultural systems is poorly understood, from agricultural processes to cooking practices and cuisine: integrated work between different specialisms would assist greatly. There is a need for conceptual as well as practical perspectives – e.g. how were wild resources conceived? o Ritual practice. There has been valuable work in identifying depositional practices, such as deposition of animals or querns, which are thought to relate to house-based ritual practices, but there is great potential for further pattern-spotting, synthesis and interpretation. Iron Age Scotland: ScARF Panel Report v  Landscapes and regions:  Concepts of ‘region’ or ‘province’, and how they changed over time, need to be critically explored, because they are contentious, poorly defined and highly variable. What did Iron Age people see as their geographical horizons, and how did this change?  Attempts to understand the Iron Age landscape require improved, integrated survey methodologies, as existing approaches are inevitably partial.  Aspects of the landscape’s physical form and cover should be investigated more fully, in terms of vegetation (known only in outline over most of the country) and sea level change in key areas such as the firths of Moray and Forth.  Landscapes beyond settlement merit further work, e.g. the use of the landscape for deposition of objects or people, and what this tells us of contemporary perceptions and beliefs.  Concepts of inherited landscapes (how Iron Age communities saw and used this longlived land) and socal resilience to issues such as climate change should be explored more fully.  Reconstructing Iron Age societies. The changing structure of society over space and time in this period remains poorly understood. Researchers should interrogate the data for better and more explicitly-expressed understandings of social structures and relations between people.  The wider context: Researchers need to engage with the big questions of change on a European level (and beyond). Relationships with neighbouring areas (e.g. England, Ireland) and analogies from other areas (e.g. Scandinavia and the Low Countries) can help inform Scottish studies. Key big topics are: o The nature and effect of the introduction of iron. o The social processes lying behind evidence for movement and contact. o Parallels and differences in social processes and developments. o The changing nature of houses and households over this period, including the role of ‘substantial houses’, from crannogs to brochs, the development and role of complex architecture, and the shift away from roundhouses. o The chronology, nature and meaning of hillforts and other enclosed settlements. o Relationships with the Roman world
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Cluster analysis of phytoplankton data collected from the National Stream Quality Accounting Network in the Tennessee River basin, 1974-81. US Geological Survey, 1988. http://dx.doi.org/10.3133/wri864029.

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