• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.125-143
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• 1993

The primary objective of the paper is to review the development of approximations of the null distribution of a scan statistic and to show how these approximations were improved. Let $X_1, \cdots, X_N$ be a sequence of independent uniform random variables on an interval (0, t]. A can statistic is defined to be the maximum number of observations in a subinterval of length t $\leq$ T, when we continuously (or discretely) move the subinterval from 0 to T. A scan statistic is used to test whether certain events occur in a cluster aganist a null hypothesis of the uniformity. It is difficult to calculate the exact null distribution of a scan statistic. Several authors have suggested approximations of the null distribution of a scan statistic since Naus(1966). We conceive that a scan statistic can be used for detecting a "hot region" is defined to be a region at which the frequencies of mutations are relatively high. A "hot region" may be regarded as a generalized version of a hot spot. We leave it for a further study the concrete formulation of deteciton a "hot region" in a mutational spectrum.uot; in a mutational spectrum.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1193-1201
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• 2016

In epidemiology or etiology, we are often interested in identifying areas of elevated risk, so called, hot spot or cluster. Many existing clustering methods only tend to a result if there exists any clustering pattern in study area. Recently, however, lots of newly introduced clustering methods can identify the location, size, and shape of clusters and test if the clusters are statistically significant as well. In this paper, one of most commonly used clustering methods, scan statistic, and its implementation SaTScan software, which is freely available, will be introduced. To exemplify the usage of SaTScan software, we used cancer data from the SEER program of National Cancer Institute of U.S.A.We aimed to help researchers and practitioners, who are interested in spatial cluster detection, using female lung cancer mortality data of the SEER program.

• Parasites, Hosts and Diseases
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• v.58 no.6
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• pp.603-608
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• 2020

This study was performed to find out the clusters with high parasite infection risk to discuss the geographical pattern. Clusters were detected using SatScan software, which is a statistical spatial scan program using Kulldorff's scan statistic. Information on the parasitic infection cases in Korea 2011-2019 were collected from the Korea Centers for Disease Control and Prevention. Clusters of Ascaris lumbricoides infection were detected in Jeollabuk-do, and T. trichiura in Ulsan, Busan, and Gyeongsangnam-do. C. sinensis clusters were detected in Ulsan, Daegu, Busan, Gyeongsangnamdo, and Gyeongsangbuk-do. Clusters of intestinal trematodes were detected in Ulsan, Busan, and Gyeongsangnam-do. P. westermani cluster was found in Jeollabuk-do. E. vermicularis clusters were distributed in Gangwon-do, Jeju-do, Daegu, Daejeon, and Gwangju. This clustering information can be referred for surveillance and control on the parasitic infection outbreak in the infection-prone areas.

• 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 Korean Neurosurgical Society
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• v.47 no.2
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• pp.89-94
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• 2010

Objective : To evaluate the role of cranial sonography and computed tomography in the diagnosis of neonatal intracranial hemorrhage and hypoxic-ischemic injury in an Indian set-up. Methods : The study included 100 neonates who underwent cranial sonography and computed tomography (CT) in the first month of life for suspected intracranial ischemia and hemorrhage. Two observers rated the images for possible intracranial lesions and a kappa statistic for interobserver agreement was calculated. Results : There was no significant difference in the kappa values of CT and ultrasonography (USG) for the diagnosis of germinal matrix hemorrhage/intraventricular hemorrhage (GMH/IVH) and periventricular leucomalacia (PVL) and both showed good interobserver agreement. USG, however detected more cases of GMH/IVH (24 cases) and PVL (19) cases than CT (22 cases and 16 cases of IVH and PVL, respectively). CT had significantly better interobserver agreement for the diagnosis of hypoxic ischemic injury (HII) in term infants and also detected more cases (33) as compared to USG (18). CT also detected 6 cases of extraaxial hemorrhages as compared to 1 detected by USG. Conclusion : USG is better modality for imaging preterm neonates with suspected IVH or PVL. However, USG is unreliable in the imaging of term newborns with suspected HII where CT or magnetic resonance image scan is a better modality.

• Parasites, Hosts and Diseases
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• v.58 no.5
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• pp.493-497
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• 2020

The purpose of this study is to find out the clusters with high toxoplasmosis risk to discuss the geographical pattern in 2 islands of Gyodong-myeon and Samsan-myeon in Ganghwa-gun, Korea. Seroepidemiological data of toxoplasmosis surveyed using rapid diagnostic tests for the residents in 2 islands from 2010 to 2013 were analyzed to detect clusters of the infection. The cluster was investigated using the SatScan program which is based on Kulldorff's scan statistic. The clusters were found with P-values in each region analyzed in the program, and the risk and patient incidence of specific areas can be examined by the values such as relative risk and log likelyhood ratio. Jiseok-ri was found to be a cluster in Gyodong-myeon and Ha-ri was the cluster in Samsan-myeon. This findings can be used to monitor and prevent toxoplasmosis infections occurring in vulnerable areas.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1141-1150
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• 2006

In this study, we performed spatial cluster analysis which considered spatial information using earthquake data for Korean peninsula occurred on 1978 year to 2005 year. Also, we look into how to be clustered for regions using earthquake magnitude and frequency based on spatial scan statistic. And, on the basis of the results, we constructed earthquake map by earthquake outbreak risk and gave a possible explanation for the results of spatial cluster analysis.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.5
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• pp.261-269
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• 2023

In light of recent social concerns related to issues such as water supply pipe deterioration leading to problems like leaks and degraded water quality, the significance of maintenance efforts to enhance water source quality and ensure a stable water supply has grown substantially. In this study, scan statistic was applied to analyze water quality complaints and water leakage accidents from 2015 to 2021 to present a reasonable method to identify areas requiring improvement in water management. SaTScan, a spatio-temporal statistical analysis program, and ArcGIS were used for spatial information analysis, and clusters with high relative risk (RR) were determined using the maximum log-likelihood ratio, relative risk, and Monte Carlo hypothesis test for I city, the target area. Specifically, in the case of water quality complaints, the analysis results were compared by distinguishing cases occurring before and after the onset of "red water." The period between 2015 and 2019 revealed that preceding the occurrence of red water, the leak cluster at location L2 posed a significantly higher risk (RR: 2.45) than other regions. As for water quality complaints, cluster C2 exhibited a notably elevated RR (RR: 2.21) and appeared concentrated in areas D and S, respectively. On the other hand, post-red water incidents of water quality complaints were predominantly concentrated in area S. The analysis found that the locations of complaint clusters were similar to those of red water incidents. Of these, cluster C7 exhibited a substantial RR of 4.58, signifying more than a twofold increase compared to pre-incident levels. A kernel density map analysis was performed using GIS to identify priority areas for waterworks management based on the central location of clusters and complaint cluster RR data.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.76-80
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• 2009

Purpose: The uptake of $^{99m}Tc$-MIBI increases in the parathyroid adenoma, hyperplasia, and carcinoma. The purpose of this study is to evaluate and compare the results of the biopsy with quantitative analysis results in the localized parathyroid lesions (adenoma, hyperplasia, and carcinoma). Materials and Methods: From April 2002 to January 2009, double-phase $^{99m}Tc$-MIBI parathyroid scan (early 15 min, delayed 2 hrs) was performed after injection of 750 MBq of $^{99m}Tc$-MIBI in 27 diagnosed parathyroid patients (adenoma:15, hyperplasia:4, carcinoma:8). For quantitative analysis, early, delayed lesion to non-lesion ratios (L/NLs), washout rate (%, WR) and vertical axis were calculated. Results: In early images, lesion to non-lesion ratios were $1.53{\pm}0.41$ (adenoma), $1.38{\pm}0.27$ (hyperplasia) and $1.45{\pm}0.64$ (carcinoma). In delayed images, lesion to non-lesion ratios were $1.56{\pm}0.43$ (adenoma), $1.33{\pm}0.10$ (hyperplasia), $1.83{\pm}0.79$ (carcinoma). In vertical axis, the sizes were $2.11{\pm}0.67$ (adenoma), $2.23{\pm}0.75$ (hyperplasia) and $2.20{\pm}0.97$ (carcinoma). There was no statistical difference between lesion to non-lesion ratios and the size of vertical axis (p>0.05). However, washout rates were $31.59{\pm}13.97$ (adenoma), $37.8{\pm}5.69$ (hyperplasia), $17.73{\pm}11.02$ (carcinoma). As a result, there was a significant difference statistically between and that of carcinoma (p<0.05, p=0.028 by Kruskal-willis statistic, Dunn's Multiple comparison test SPSS Ver 12.0). Conclusions: There was no statistical difference between the lesion to non-lesion ratios and the size of vertical axis. However, there was a significant difference statistically between WR of hyperplasia and that of carcinoma.