Journal articles: 'Regional clustering'

1

Enright, Michael J., and Brian H. Roberts. "Regional Clustering in Australia." Australian Journal of Management 26, no. 1_suppl (August 2001): 65–85. http://dx.doi.org/10.1177/031289620102601s04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Dolores Gadea-Rivas, M., Ana Gómez-Loscos, and Eduardo Bandrés. "Clustering regional business cycles." Economics Letters 162 (January 2018): 171–76. http://dx.doi.org/10.1016/j.econlet.2017.10.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Юрий Николаевич, Лапыгин, and Ковалев Евгений Александрович. "CLUSTERING HEALTH IN REGIONAL STRATEGIES." STATE AND MUNICIPAL MANAGEMENT SCHOLAR NOTES 1, no. 4 (December 25, 2020): 29–34. http://dx.doi.org/10.22394/2079-1690-2020-1-4-29-34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Vertakova, Yu V., and Yu S. Polozhentseva. "Clustering algorithm of regional economic space." St. Petersburg State Polytechnical University Journal. Economics 235, no. 1 (March 2016): 75–85. http://dx.doi.org/10.5862/je.235.7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Tavares, Rodrigo. "Regional clustering of peace and security." Global Change, Peace & Security 21, no. 2 (June 2009): 153–64. http://dx.doi.org/10.1080/14781150902872000.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Sonğur, Cuma, and Mehmet Top. "Regional clustering of medical imaging technologies." Computers in Human Behavior 61 (August 2016): 333–43. http://dx.doi.org/10.1016/j.chb.2016.03.056.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Charykova, O. G., and E. S. Markova. "Regional Clustering in the Digital Economy." Economy of Region 15, no. 2 (June 2019): 409–19. http://dx.doi.org/10.17059/2019-2-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Mitina, Ella, and Olga Yarosh. "Forecasting Organic Consumption Based on Regional Clustering." Regionalnaya ekonomika. Yug Rossii, no. 2 (August 2020): 171–82. http://dx.doi.org/10.15688/re.volsu.2020.2.16.

Full text

Abstract:

One of the most important strategic tasks of the agrarian policy of the Russian Federation is to provide the population of the country with high-quality and safe food. They first of all determine the health state of people. This situation is connected with the fact that 70% of harmful substances enter the human body with food. Consumers must be given the opportunity to purchase quality food products grown by organic farms. The organic market in Russia is at the initial stage, so there is a huge shortage of statistics to identify consumption of these products and their statistical records are not included into a separate group of agricultural products. In this regard the authors have developed and tested a methodology for the normative forecast of the potential consumption of organic products based on the clustering of the regions of the Republic of Crimea, which includes six consecutive stages. They combine the use of a complex of marketing and statistical analysis methods implemented in the SPSS environment; mapping using modern GIS technologies with the allocation of geographic information profiles of the region, which allow differentiating the territory of the peninsula in terms of the potential consumption of organic products. The gradation of the limits of potential consumption of these goods is made automatically by means of the method of classification of natural boundaries. The main results obtained in the work include: identification of the amount of money spent by the population on the consumption of organic products; establishment of the limits on growth of potential consumption for a given product in the region; determination of its share and dynamics of the increase in the structure of the total commodity circulation of the peninsula, identification of areas that form potential organic consumption in the Republic of Crimea.

APA, Harvard, Vancouver, ISO, and other styles

9

Smirnov, V. V., and A. V. Mulendeeva. "Regional clustering of the Russian higher education." Regional Economics: Theory and Practice 17, no. 2 (February 15, 2019): 266–82. http://dx.doi.org/10.24891/re.17.2.266.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Yelkikalan, Nazan, Ergül Söylemezoğlu, Abdullah Kıray, Rukiye Sönmez, Bilal Ezilmez, and Melike Altun. "Clustering Approach as a Regional Development Tool." Procedia - Social and Behavioral Sciences 58 (October 2012): 503–13. http://dx.doi.org/10.1016/j.sbspro.2012.09.1027.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Maravalle, Maurizio, and Bruno Simeone. "A spanning tree heuristic for regional clustering." Communications in Statistics - Theory and Methods 24, no. 3 (January 1995): 625–39. http://dx.doi.org/10.1080/03610929508831512.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Corduas, Marcella. "Clustering streamflow time series for regional classification." Journal of Hydrology 407, no. 1-4 (September 2011): 73–80. http://dx.doi.org/10.1016/j.jhydrol.2011.07.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Barton, Yannick, Paraskevi Giannakaki, Harald von Waldow, Clément Chevalier, Stephan Pfahl, and Olivia Martius. "Clustering of Regional-Scale Extreme Precipitation Events in Southern Switzerland." Monthly Weather Review 144, no. 1 (January 1, 2016): 347–69. http://dx.doi.org/10.1175/mwr-d-15-0205.1.

Full text

Abstract:

Abstract Temporal clustering of extreme precipitation events on subseasonal time scales is of crucial importance for the formation of large-scale flood events. Here, the temporal clustering of regional-scale extreme precipitation events in southern Switzerland is studied. These precipitation events are relevant for the flooding of lakes in southern Switzerland and northern Italy. This research determines whether temporal clustering is present and then identifies the dynamics that are responsible for the clustering. An observation-based gridded precipitation dataset of Swiss daily rainfall sums and ECMWF reanalysis datasets are used. Also used is a modified version of Ripley’s K function, which determines the average number of extreme events in a time period, to characterize temporal clustering on subseasonal time scales and to determine the statistical significance of the clustering. Significant clustering of regional-scale precipitation extremes is found on subseasonal time scales during the fall season. Four high-impact clustering episodes are then selected and the dynamics responsible for the clustering are examined. During the four clustering episodes, all heavy precipitation events were associated with an upper-level breaking Rossby wave over western Europe and in most cases strong diabatic processes upstream over the Atlantic played a role in the amplification of these breaking waves. Atmospheric blocking downstream over eastern Europe supported this wave breaking during two of the clustering episodes. During one of the clustering periods, several extratropical transitions of tropical cyclones in the Atlantic contributed to the formation of high-amplitude ridges over the Atlantic basin and downstream wave breaking. During another event, blocking over Alaska assisted the phase locking of the Rossby waves downstream over the Atlantic.

APA, Harvard, Vancouver, ISO, and other styles

14

MINDLIN, Yu B. "COMPARATIVE CHARACTERISTICS OF CLUSTERS AND REGIONAL ECONOMIC SOVIET." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1, no. 5 (2020): 124–29. http://dx.doi.org/10.36871/ek.up.p.r.2020.05.01.017.

Full text

Abstract:

The article considers the essence and properties of clustering, examines clusters and their impact on the innovative development of the economy. The author claims that systemicity as a property indicates the structuring of clustering, the relationship between its elements, subordination to the achievement of a single goal, dependence on the external environment, and the integrity of the subjects involved in clustering. All this is summarized in the concept of clusterization of the national economy.

APA, Harvard, Vancouver, ISO, and other styles

15

Doni, A. F., Y. D. P. Negera, and O. A. H. Maria. "K-Means Clustering Algorithm for Determination of Clustering of Bangkalan Regional Development Potential." Journal of Physics: Conference Series 1569 (July 2020): 022078. http://dx.doi.org/10.1088/1742-6596/1569/2/022078.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Xu, Xiang, Jun Li, Changshan Wu, and Antonio Plaza. "Regional clustering-based spatial preprocessing for hyperspectral unmixing." Remote Sensing of Environment 204 (January 2018): 333–46. http://dx.doi.org/10.1016/j.rse.2017.10.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Dinh, Christoph, Daniel Strohmeier, Martin Luessi, Daniel Güllmar, Daniel Baumgarten, Jens Haueisen, and Matti S. Hämäläinen. "Real-Time MEG Source Localization Using Regional Clustering." Brain Topography 28, no. 6 (March 18, 2015): 771–84. http://dx.doi.org/10.1007/s10548-015-0431-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

HARRIS, THOMAS R., SHAWN W. STODDARD, and JAMES C. BEZDEK. "Application of Fuzzy-Set Clustering for Regional Typologies." Growth and Change 24, no. 2 (April 1993): 155–65. http://dx.doi.org/10.1111/j.1468-2257.1993.tb00958.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Oh, Kanghan, Myungeun Lee, Yura Lee, and Soohyung Kim. "Salient object detection using recursive regional feature clustering." Information Sciences 387 (May 2017): 1–18. http://dx.doi.org/10.1016/j.ins.2017.01.019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Patrusheva, Elena G., and Anna V. Rajhlina. "CLUSTERING AS A COMPETITIVE ADVANTAGE OF REGIONAL ECONOMICS." Journal of Regional and International Competitiveness, no. 2 (2021): 33–41. http://dx.doi.org/10.52957/27821927_2021_2_33.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Huang, Qiang, Jiahan Li, and Jingsi Gao. "The risks assessment via a regional approach using multivariate regional frequency clustering method." Cluster Computing 20, no. 4 (September 6, 2017): 3441–57. http://dx.doi.org/10.1007/s10586-017-1126-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

He, Yan Xiang, Rui Cai, Li Bing Wu, and Fei Li. "K-Means Optimization Algorithms of Initial Clustering Center Based on Regional Density." Applied Mechanics and Materials 513-517 (February 2014): 478–82. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.478.

Full text

Abstract:

The accuracy of K-means algorithm is greatly influenced by initial clustering center which is related to the density distribution of the sample to some extent. In order to obtain the gradually optimized initial clustering center, the present study implement the loop fusion of sub regions based on their density and correlation by dividing the region into sub ones. The improved K-means algorithm is tested in two dimensions, and the results show that the improved algorithm obtains more optimized initial clustering center with higher accuracy of clustering.

APA, Harvard, Vancouver, ISO, and other styles

23

Saha, Nibedita, and Petr Sáha. "The Synergies Influence of Clustering and Smart Specialization Strategy: Do They Really Stimulate Entrepreneurship and Regional Development?" Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 159–68. http://dx.doi.org/10.18844/gjbem.v7i1.1365.

Full text

Abstract:

This paper reveals on the significance of clustering concept and smart specialization strategy as an effective tool that stimulates entrepreneurship and creates regional value. The worth of linking different firms, people, and knowledge at the regional level- are one of the ways of making regions, organizations and institutions more innovative and competitive. The mode of a classifying clustering impression to be the voice of smart specialization for developing strategic goals and criteria related to regional growth. Thus, the result of this study shows clustering in addition to smart specialization strategies are powerful instruments to foster industrial competitiveness, institutional innovation, and provincial growth due to their intrinsic competence. Both clusters and smart specialization are gaining its prominence in the EU's regional policy discourse, owing to its role in the new program period of the EU structural policy from 2014 to 2020. This paper suggested some recommendations based on the highlighted research assumptions that the added value of the regional knowledge development process will lead the regions and enterprises to achieve their competitive advantages through the transfer of specific individual knowledge to the collective knowledge, and vice versa.. Keywords: Clustering, entrepreneurship, regional development, smart specialization strategy.

APA, Harvard, Vancouver, ISO, and other styles

24

Gorbatiuk, Kateryna, Olha Mantalyuk, Oksana Proskurovych, and Oleksandr Valkov. "Analysis of regional development disparities in Ukraine with fuzzy clustering technique." SHS Web of Conferences 65 (2019): 04008. http://dx.doi.org/10.1051/shsconf/20196504008.

Full text

Abstract:

Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.

APA, Harvard, Vancouver, ISO, and other styles

25

Abdyrov, T. Sh, and N. S. Dosmaganbetov. "ASSESSMENT OF CLUSTERING POTENTIAL OF REGIONAL ECONOMIES IN KAZAKHSTAN." Science of the Person: Humanitarian Researches 4, no. 38 (December 2019): 176–83. http://dx.doi.org/10.17238/issn1998-5320.2019.38.176.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

ZHOU, Hong-fang, Xue-han ZHAO, and Yang ZHOU. "Density-based clustering algorithm combined with limited regional sampling." Journal of Computer Applications 32, no. 8 (May 6, 2013): 2182–85. http://dx.doi.org/10.3724/sp.j.1087.2012.02182.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Sun, Yongke. "A Clustering Algorithm of Regional Character Based on SURF." Recent Patents on Computer Science 9, no. 1 (March 4, 2016): 13–18. http://dx.doi.org/10.2174/2213275908666150701190521.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Basu, Bidroha, and V. V. Srinivas. "Regional Flood Frequency Analysis Using Entropy-Based Clustering Approach." Journal of Hydrologic Engineering 21, no. 8 (August 2016): 04016020. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001351.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Santos, Rita. "Interaction between innovation systems, clustering and sustainability: regional implications." International Journal of Foresight and Innovation Policy 2, no. 2 (2006): 184. http://dx.doi.org/10.1504/ijfip.2006.009322.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Owsiński, Jan W., and Sławomir Zadrozny. "Structuring a regional problem: Aggregation and clustering in orderings." Applied Stochastic Models and Data Analysis 2, no. 1-2 (1986): 83–95. http://dx.doi.org/10.1002/asm.3150020108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Sabater, Cristina Rueda, Pedro C. Alvarez Esteban, Agustín Mayo Iscar, and Ana López Díez. "Clustering to reduce regional heterogeneity: A spanish case-study." Journal of Population Research 21, no. 1 (March 2004): 73–93. http://dx.doi.org/10.1007/bf03032211.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Tamimy, Z., S. T. Kevenaar, J. J. Hottenga, M. D. Hunter, E. L. de Zeeuw, M. C. Neale, C. E. M. van Beijsterveldt, C. V. Dolan, Elsje van Bergen, and D. I. Boomsma. "Multilevel Twin Models: Geographical Region as a Third Level Variable." Behavior Genetics 51, no. 3 (February 27, 2021): 319–30. http://dx.doi.org/10.1007/s10519-021-10047-x.

Full text

Abstract:

AbstractThe classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

APA, Harvard, Vancouver, ISO, and other styles

33

Smeeton, Nigel, and Greg Wilkinson. "The Identification of Clustering in Parasuicide." British Journal of Psychiatry 153, no. 2 (August 1988): 218–21. http://dx.doi.org/10.1192/bjp.153.2.218.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Zhao, Bing. "Research on the Measurements of the Production Factors between the Hi-Tech Zones and the Hinterlands." Applied Mechanics and Materials 556-562 (May 2014): 3043–46. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3043.

Full text

Abstract:

The regional transfer of production factors was the foundation of regional economy, which was connected with non-balance-development theory. The paper, from the angle of regional economy, made a deep study and established the transfer measurements of the production factors of the High-tech zones.Empirical study of the panel data of the High-tech Zone, results showed that the clustering rate in the zones is already good, but the clustering quality of production factors was not ideal.

APA, Harvard, Vancouver, ISO, and other styles

35

Fan, Xinghua, Huihui Xu, Cheng Ning, and Liangjie Wu. "Regional variability in water use intensity in China by clustering analysis." Water Supply 16, no. 4 (March 7, 2016): 1102–9. http://dx.doi.org/10.2166/ws.2016.028.

Full text

Abstract:

Water use intensity (WUI) reveals water withdrawals with respect to economic output. Decomposing WUI into factors provides inner-system information affecting the indicator. The present study investigates variability in WUI among provinces in China by clustering the principal components of the decomposed factors. Motivated by the index decomposition method, the authors decomposed WUI into seven factors: water use in agricultural, industrial, household and ecological sectors, exploitation rate of water resources, per capita water resources and population intensity. Those seven factors condense into four principal components under application of principal component analysis. Comprehensive WUI is calculated by these four components. Then the cluster analysis is applied to get different patterns in WUI. The principal components and the comprehensive intensity are taken as cluster variables. The number of clusters is determined to be three by applying the k-means clustering method and the F-statistic value. Variability in WUI is detected by implementing three clustering algorithms, namely k-means, fuzzy c-means and the Gaussian mixture model. WUI in China is clustered into three clusters by the k-means clustering method. Characteristics of each cluster are analyzed.

APA, Harvard, Vancouver, ISO, and other styles

36

Gunanto, Gunanto, Dhomas Hatta Fudholi, and Lizda Iswari. "ANALISIS SPASIAL PERSEBARAN REKLAME." JITU : Journal Informatic Technology And Communication 3, no. 1 (May 31, 2019): 1–10. http://dx.doi.org/10.36596/jitu.v3i1.56.

Full text

Abstract:

Regional autonomy is the surrender of authority from the center to the regions to regulate and manage the interests of the local community according to their own initiatives based on the aspirations of the people, as stated in Law No. 32 of 2004 concerning Regional Government. With the existence of regional autonomy, the regional government is expected to be better to explore the potential of local revenue sources in financing all regional development activities through increasing Original Local Government Revenue (OLGR). One component of OLGL that has a contribution in Pekalongan Regency is Regional Tax. Regional tax, one of which is advertisement tax, is one component of the OLGL that contributes to regional development. Clustering algorithm, one of which is k-means clustering can be applied to advertisement tax data so that it can be known that ad grouping is based on distance from the market, distance to traffic light and vehicle volume. From each of these groupings can also be seen each of the characteristics so that it is known which groups have the largest amount of tax and the number of tax donations. From this research, a web-based system has been successfully developed that is able to process the spatial analysis of the distribution of billboards with the clustering method in Pekalongan Regency. From the results of clustering analysis, it can be seen that the Subdistrict passed by the coastline has a correlation with the high amount of advertisement tax in Pekalongan Regency, this can be seen in the results of clustering using the k-means algorithm, where advertisements are in clusters that have average quantities the highest taxes are all in the sub-district that is crossed by north coast way. The closeness to the market and traffic light has a correlation with the high amount of billboard bill advertising tax in Pekalongan Regency, wherein the clusters that have the highest volume of vehicles the average size of the billboard tax is high.

APA, Harvard, Vancouver, ISO, and other styles

37

Muromtseva, Nataliya, Tatiana Simankina, and Irina Alpackaya. "Parametric clustering of cities." E3S Web of Conferences 164 (2020): 04005. http://dx.doi.org/10.1051/e3sconf/202016404005.

Full text

Abstract:

The article analyzes the master plans and functional areas of 42 cities from different federal districts of the Russian Federation. To identify the dependencies between the characteristics of localities and the allocation of functional zones in them, the data clustering method was applied. Also criteria for clustering were identified – these are quantitative characteristics that are universal for various localities: total area of a locality, population, population density and gross regional product. With these criteria clustering was carried out using the software package «Deductor», based on algorithms of neural network modeling. Self-organizing Kohonen maps (SOM) were used to visualize the obtained data. As a result of the clustering the connection between the characteristics of localities and the ratio of functional zones in them is revealed.

APA, Harvard, Vancouver, ISO, and other styles

38

Lee, Taemin, Woosung Choi, Jongryuel Sohn, Kyongwhan Moon, Sanghoon Byeon, Wookyun Lee, and Soonyoung Jung. "Spatial Clustering based Meteorological Fields Construction for Regional Vulnerability Assessment." International Journal on Advanced Science, Engineering and Information Technology 8, no. 4-2 (September 30, 2018): 1686. http://dx.doi.org/10.18517/ijaseit.8.4-2.5759.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Song, Tao, Tingguo Zheng, and Kai Xia. "Regional Clustering and Synchronization of Provincial Business Fluctuations in China." Chinese Geographical Science 28, no. 4 (July 14, 2018): 571–83. http://dx.doi.org/10.1007/s11769-018-0975-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Zaifoglu, H., B. Akintug, and A. M. Yanmaz. "Regional Frequency Analysis of Precipitation Using Time Series Clustering Approaches." Journal of Hydrologic Engineering 23, no. 6 (June 2018): 05018007. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001659.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Kuznetsova, Victorovna, and Alexandrovna Vorobeva. "Clustering as modern concept of regional development: Experience of Japan." Ekonomika 62, no. 1 (2016): 97–112. http://dx.doi.org/10.5937/ekonomika1601097v.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Basu, Bidroha, and V. V. Srinivas. "Regional flood frequency analysis using kernel-based fuzzy clustering approach." Water Resources Research 50, no. 4 (April 2014): 3295–316. http://dx.doi.org/10.1002/2012wr012828.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Chung, François, and Hervé Delingette. "Regional appearance modeling based on the clustering of intensity profiles." Computer Vision and Image Understanding 117, no. 6 (June 2013): 705–17. http://dx.doi.org/10.1016/j.cviu.2013.01.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Saha, Moumita, Arun Chakraborty, and Pabitra Mitra. "Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/9031625.

Full text

Abstract:

Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

APA, Harvard, Vancouver, ISO, and other styles

45

Fu, Wan Jun, and Gui Jin Wang. "Assessment of Regional Geological Disaster on Grey Clustering Methed in Shulan." Applied Mechanics and Materials 295-298 (February 2013): 673–77. http://dx.doi.org/10.4028/www.scientific.net/amm.295-298.673.

Full text

Abstract:

Gray clustering method was used to establish the regional geological disaster risk assessment model by factor of the mean annual rainfall, relative height, ground slope ，geomorphological type, rock and soil type, fracture density, earthquake intensity, vegetation cover rate, human activity. This model was used to Predictive assessment of geological disasters in the Jilin Shulan region which is divided into the Ⅰ, Ⅱ, Ⅲ, Ⅳ districts according to the disaster assessment level. The Comparison of the model predicted disaster area and the actual geological disaster areas was correlation (accuracy) of 75% or more. The study confirms that the gray clustering method to predict geological disasters is effective to reduce the loss of geological disasters and to prevent geological disasters.

APA, Harvard, Vancouver, ISO, and other styles

46

Chaudhry, Muhammad A., Richard Wahl, Lujaien Al-Rubaiey Kadhim, and Atif Zaheer. "Contrast Enhanced Computed Tomography Characterization of Fluorodeoxygluocose-Avid Regional and Non-Regional Lymph Nodes in Patients with Suspicion of Metastatic Bladder Cancer." Journal of Clinical Imaging Science 3 (December 31, 2013): 66. http://dx.doi.org/10.4103/2156-7514.124104.

Full text

Abstract:

Objective: The objective of this study is to assess if size alone can predict the presence of metastatic disease within lymph nodes seen on contrast enhanced-computed tomography (CE-CT) in patients with suspicion of metastatic bladder cancer and also to evaluate the nodal distribution and morphological characteristics of fluorodeoxygluocose (FDG) avid lymph nodes on CE-CT. Materials and Methods: A retrospective analysis from 2002 to 2009 was performed on patients with suspicion of recurrent disease undergoing restaging FDG-positron emission tomography (PET)/CT. Standardized uptake value (SUVmax) adjusted for lean body mass was recorded in abnormal lymph nodes in the abdominopelvic region. Distribution, size, shape, presence of necrosis and clustering of the FDG-avid lymph nodes was assessed on CE-CT obtained within 4 weeks of the PET/CT. The abnormal nodes were then compared with non-FDG avid lymph nodes on the contralateral side serving as control. Results: A total of 103 lymph nodes were found to be FDG-avid in 14 patients on 17 PET/CT examinations. Overall, mean SULmax was 4.7 (range: 1.6-10.7), which is significantly higher than background of 1.5 (P < 0.05). Regional pelvic lymph nodes were FDG-avid in 93% of patients and metastatic extra-pelvic in 100% of patients. The overall average size of the FDG avid lymph nodes on CE-CT was 11 mm with a third of these measuring 3-8 mm. The average size of FDG-avid lymph nodes was 11 mm in the paraaortic region 13 mm in the common iliac 9 mm in the internal iliac and 13 mm in the external iliac regions. Nearly 88.4% of lymph nodes were round in shape, clustering was present in 68% and necrosis in 7% and average size of lymph nodes that served as controls was 6 mm with reniform morphology in 92% and absence of clustering and necrosis. Conclusion: Overlap in size exists between FDG-avid pathological and non-pathological lymph nodes seen on CE-CT in patients with metastatic bladder cancer. Other characteristic such as abnormal morphology and clustering are useful adjuncts in the evaluation of nodal metastatic disease.

APA, Harvard, Vancouver, ISO, and other styles

47

Tang, Liyang, Yang Zhao, Kwok Leung Tsui, Yuxin He, and Liwei Pan. "A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data." Applied Sciences 10, no. 16 (August 13, 2020): 5606. http://dx.doi.org/10.3390/app10165606.

Full text

Abstract:

Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.

APA, Harvard, Vancouver, ISO, and other styles

48

Michael, E. J. "Tourism Micro-Clusters." Tourism Economics 9, no. 2 (June 2003): 133–45. http://dx.doi.org/10.5367/000000003101298312.

Full text

Abstract:

This paper presents an approach to regional development policy that may serve to stimulate the growth of tourism-based industries. The context for this discussion lies in the experiences of Australia's rural towns, but the applications seem broadly international. The paper explains the concepts of clustering in a model for micro-markets that is consistent with the global demands for competition. The focus is on the creation of economic and social opportunity in small communities through the development of clusters of complementary firms that can collectively deliver a bundle of attributes to make up a specialized regional product. Micro-market clustering theory offers one alternative for enhancing regional economic growth, but it seems suited to tourism development when the regional product requires a combination of many services to deliver the single experience the tourist seeks.

APA, Harvard, Vancouver, ISO, and other styles

49

Gorina, Ganna O., Valentyna V. Barabanova, Galina A. Bohatyryova, Olga A. Nikolaichuk, and Aleksander M. Romanukha. "Clustering of regional tourism service markets according to indicators of the functioning of subjects of tourism activity." Journal of Geology, Geography and Geoecology 29, no. 4 (December 25, 2020): 684–92. http://dx.doi.org/10.15421/112061.

Full text

Abstract:

Clustering of regional tourism service markets of Ukraine is performed according to the indicators of functioning of subjects of tourism activity (number of subjects of tourism activity; number of stuff members of tourist activities; income from tourism; number of tourists served by tour operators and travel agents; cost of travel packages sold). The clustering was performed in the software STATISTICA 10, the Ward method was chosen as the clustering algorithm, and the Euclidean distance was chosen as a measure of distance. The calculations made it possible to distinguish 6 clusters of regional tourism servicemarkets of Ukraine, which are formed at a threshold distance of 0.5, namely: cluster 1 is Kyiv city, cluster 2 is Zaporizhia, Kyiv, Poltava, Ivano-Frankivsk regions, cluster 3 is Dnipropetrovsk , Lviv regions, cluster 4 is Odessa, Kharkiv regions, cluster 5 is formed from Zhytomyr, Ternopil, Chernihiv, Kirovograd and Luhansk regions, cluster 6 unites Vinnytsia, Chernivtsi, Donetsk, Cherkasy, Transcarpathian, Rivne, Khmelnytsky, Volyn, Sumy, Kherson and Mykolaiv regions. The clusters on the vertical dendogram of hierarchical clustering are visualized, the spatial distribution of clusters of regional markets of tourist services is shown on the map of Ukraine. It is established that the high degree of regional differentiation of tourist service markets in Ukraine is caused by differences in the distribution of natural resources, climatic features of the regions, cultural and historical conditionality, and uneven placement of transport, communication, resort and entertainment infrastructures, asymmetrical distribution of recreational resources. It is suggested to take into account the restrictions of development of regional tourism service markets of Ukraine (clusters of regional tourism service markets) in terms of spatial polarization, to distribute them into internal (historically-formed basic and innovative determinants) and external ones.

APA, Harvard, Vancouver, ISO, and other styles

50

Collins, Steven W. "Academic Research and Regional Innovation." Industry and Higher Education 15, no. 3 (June 2001): 169–78. http://dx.doi.org/10.5367/000000001101295623.

Full text

Abstract:

According to a large body of research, academic R&D has an important inductive effect on industrial innovation. However, the extent to which the returns to university-induced innovation are captured locally remains poorly understood. This paper introduces and assesses technology transfer at the University of Washington in Seattle, Washington, in the context of the problems associated with appropriating the benefits of academic R&D for the regional economy. The large and growing number of university-related start-up companies in the region and the clustering of firms near the university are among the indicators of success. Areas of concern are also discussed. The paper concludes by suggesting lessons for regional planners and technology transfer managers.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Regional clustering'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles