• Knowledge Management Research
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• v.2 no.1
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• pp.133-144
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• 2001

Clusters mean geographic concentrations of interconnected companies and institutions in a particular field. Geographic, cultural, and institutional proximity provides companies with special access, closer relationships, better information, powerful incentives, and other advantages that are difficult to tap from a distance. And clusters are the knowledge-sharing networks which are composed of co-existence of related industries and supporting industries, sophisticated demand, sponsor of various exhibitions and events, liaison of peripheries and clusters, liaison of clusters and clusters, and governments' willingness for promoting clusters' development.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.53-62
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• 2013

This study focuses on spatial/temporal relationship deaths caused by Human Immunodeficiency Virus (HIV) and Alcohol Use Disorder (AUD). Several studies have found links between these two diseases. By looking for clusters in mortality of Alcohol and HIV related deaths this study contributes to the field through the identification of exact spatial/temporal time of high and low occurrence risks based on the observed over the expected number of deaths. This study does not provide political or social interpretations of the data. It merely wants to show where clusters are found.

• Spatial Information Research
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• v.20 no.1
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• pp.81-90
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• 2012

When analyzing geographical phenomena, two properties need to be considered. One is the spatial dependence structure and the other is a variation or an uncertainty inhibited in a geographic space. Two problems are encountered due to the properties. Firstly, spatial dependence structure, which is conceptualized as spatial autocorrelation, generates heterogeneous geographic landscape in a spatial process. Secondly, generic statistics, although suitable for dealing with stochastic uncertainty, tacitly ignores location information im plicit in spatial data. GIS is a versatile tool for manipulating locational information, while spatial statistics are suitable for investigating spatial uncertainty. Therefore, integrating spatial statistics to GIS is considered as a plausible strategy for appropriately understanding geographic phenomena of interest. Geographic hot-spot analysis is a key tool for identifying abnormal locations in many domains (e.g., criminology, epidemiology, etc.) and is one of the most prominent applications by utilizing the integration strategy. The article aims at reviewing spatial statistical perspective for analyzing spatial processes in the framework of GIS by carrying out empirical analysis. Illustrated is the analysis procedure of using spatial scan statistic for detecting clusters in the framework of GIS. The empirical analysis targets for identifying spatial clusters of breast cancer incidents in Erie and Niagara counties, New York.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.835-848
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• 2011

In this paper, we propose a client server based distributed/parallel system to effectively visualize huge geographic data. The system consists of a web-based client GUI program and a distributed/parallel server program which runs on multiple PC clusters. To make the client program run on mobile devices as well as PCs, the graphical user interface has been designed by using JOGL, the java-based OpenGL graphics library, and sending the information about current available memory space and maximum display resolution the server can minimize the amount of tasks. PC clusters used to play the role of the server access requested geographic data from distributed disks, and properly re-sample them, then send the results back to the client. To minimize the latency happened in repeatedly access the distributed stored geography data, cache data structures have been maintained in both every nodes of the server and the client.

• Journal of Industrial Convergence
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• v.14 no.1
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• pp.39-45
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• 2016

This paper deals with the consideration of mathematical models with regards to growth of cluster and firms by reviewing the Metcalf and Breuner's articles. prior studies have been argued the phenomenon of local industrial clusters and districts. Several concepts have been adopted to support the success of and changes to these clusters and firm growth. Through the review of two papers, evolution of both cluster and firm growth may be achieved in terms of utilizations of the different local aspects and mechanisms. This paper supports the theoretical back bone with regards to the regional cluster policy implementing in Korea for the purpose of regional developments. In particular, a mathematical model that, on a more abstract level, captures the fundamental dynamic structure of all the observed mechanisms. On the basis of this model, the emergence and evolution of local clusters can be described. Also this model has given that the knowledge sharing between firms has an important role to firms and cluster' growth.

• Journal of the Korean Geographical Society
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• v.48 no.1
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• pp.56-71
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• 2013

The purpose of this study is to examine the spatial distributional characteristics of industries among the Korean cities and to conduct industry classification using the findings. For this purpose, 82 cities in Korea are investigated with respect to 15 industrial sectors. In the analysis, concentration of and association between industries are recognized using both geographic and non-geographic measures. In order to measure concentration and association, locational Gini coefficient, Moran's I, correlation coefficient, and bivatiate Moran are used and 15 industrial sectors are classified based on these estimates. The findings reveal that the chemical sector shows strong geographic and non-geographic concentrations while the assembly, machinery and electronics sector only shows a strong geographic concentration. Printing and publishing, wholesale, and business services show a strong non-geographic association with other sectors. The remaining ten sectors show no explicit distribution patterns among cities. This study contributes to providing the methodology that analyzes the spatial distribution patterns of industries in a comprehensive way and is able to provide useful information in implementing industrial location policies including industrial clusters.

• Journal of Distribution Science
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• v.20 no.6
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• pp.1-10
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• 2022

Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

• Journal of the Korean Geographical Society
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• v.45 no.4
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• pp.502-520
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• 2010

The main objective of the paper is to develop a GIS-based method for delineating spatial clusters. Major tasks are: (i) to devise a sustainable algorithm with reference to various methods developed in the fields of geographic boundary analysis and cluster detection; (ii) to develop a GIS-based program to implement the algorithm. The main results are as follows. First, it is recognized that the AMOEBA technique utilizing LISA is the best candidate. Second, a modified version of the AMOEBA technique is proposed and implemented in a GIS environment. Third, the validity and usefulness of the modified AMOEBA algorithm is assured by its applications to test and real data sets.

• International Journal of High-Rise Buildings
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• v.8 no.2
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• pp.125-136
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• 2019

This paper offers new ways to learn about popular places in the city. Using locational data from Social Media platforms platforms, including Twitter, Facebook, and Instagram, along with participatory field visits and combining insights from architecture and urban design literature, this study reveals popular socio-spatial clusters in the City of Chicago. Locational data of photographs were visualized by using Geographic Information Systems and helped in producing heat maps that showed the spatial distribution of posted photographs. Geo-intensity of photographs illustrated areas that are most popularly visited in the city. The study's results indicate that the city's skyscrapers along open spaces are major elements of image formation. Findings also elucidate that Social Media plays an important role in promoting places; and thereby, sustaining a greater interest and stream of visitors. Consequently, planners should tap into public's digital engagement in city places to improve tourism and economy.

• Journal of Microbiology
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• v.44 no.6
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• pp.600-606
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• 2006

Spore forming actinobacteria (sporoactinobacteria) isolated from soils with an acidic pH in Pinus thunbergii forests and coal mine waste were subjected to taxonomic characterization. For the isolation of acidophilic actinobacteria, acidified starch casein agar (pH adjusted to 4-5) was used. The numbers of actinobacteria growing in acidic media were between $3.2{\times}10^4$ and $8.0{\times}10^6$ CFU/g soil. Forty three acidophilic actinobacterial strains were isolated and their 16S rDNA sequences were determined. The isolates were divided into eight distinctive phylogenetic clusters within the variation encompassed by the family Streptomycetaceae. Four clusters among them were assigned to the genus Streptacidiphilus, whereas the remaining four were assigned to Streptomyces. The clusters belonging to either Streptomyces or Streptacidiphilus did not form a monophyletic clade. The growth pH profiles indicated that the representative isolates grew best between pH 5 and 6. It is evident from this study that acidity has played a critical role in the differentiation of the family Streptomycetaceae, and also that different mechanisms might have resulted in the evolution of two groups, Streptacidiphilus (strict acidophiles) and neutrotolerant acidophilic Streptomyces. The effect of geographic separation was clearly seen among the Streptacidiphilus isolates, which may be a key factor in speciation of the genus.