• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10335-10337
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• 2015

Background: Gastric cancer is considered as the second most prevalent cancer in Iran. The present research sought to identify high risk clusters of gastric cancer with mapping using space-time scan statistics. Materials and Methods: The present research is of descriptive type. The required data were gathered from the registered cancer reports of Cancer Control Office in the Center for Non Communicable Disease of the Ministry of Health (MOH). The data were extracted at province level in the time span of 2004-9. Sat-Scan software was used to analyse the data and to identify high risk clusters. ArcGIS10 was utilized to map the distribution of gastric cancer and to demonstrate high risk clusters. Results: The most likely clusters were found in Ardabil, Gilan, Zanjan, East-Azerbaijan, Qazvin, West-Azerbaijan, Kurdistan, Hamadan, Tehran and Mazandaran between 2007 and 2009. It was statistically significant at the p-value below 0.05. Conclusions: High risk regions included Northern, West-North and central provinces, particularly Ardabil, Kurdistan, Mazandaran and Gilan. More screening tests are suggested to be conducted in high risk regions along with more frequent epidemiological studies to enact gastric cancer prevention programs.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.14-28
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• 2010

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.

• 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.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1181-1190
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• 2006

Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. Until now, the echelon analysis has been applied only to univariate spatial data. As a result, it is impossible to detect the hotspots on the multivariate spatial data In this paper, we expand the spatial data to time series structure. And then we analyze them on the time space and detect the hotspots. Echelon dendrogram has been made by piling up each multivariate spatial data to bring time spatial data. We perform the structural analysis of temporal spatial data.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.492-511
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• 2013

According to IASB(International Accounting Standards Board), R&D(Research and Development) is defined as a tertiary sector industry combining research and development. Many studies investigated R&D industry clusters in the form of high-tech cluster(Coe et al., 2007). However, these studies only generalized various spatial cluster of R&D industries. In particular, the studies could not considers cluster formation process over time lacking statistical significance in space-time perspectives. This study, therefore, indicates the limitation of recent R&D cluster literature which only considers either time or space. In addition, this study explores space-time clusters in R&D industry together with textile and cloth industry for comparison. Discovering the existence and location of clusters, this study utilized space-time K function and space-time scan statistics. The result shows that R&D industry presents significant clusters only in spatial dimension. No significant clusters were found in space-time dimension. However, textile and clothing industry presents significant clusters in both spatial and space-time dimensions.

• Korean Journal of Veterinary Research
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• v.53 no.1
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• pp.49-54
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• 2013

To investigate the transmission pattern of geographical area and temporal trends of the 2010~2011 foot-and-mouth disease (FMD) outbreaks in Korea, and to explore temporal intervals at which spatial clustering of FMD cases space-time analysis based on georeferenced database of 3,575 burial sites, from 30 November 2010 to 23 February 2011, was performed. The cases represent approximately 98.1% of all infected farms (n = 3,644) during the same period. Descriptive maps of spatial patterns of the outbreaks were generated by ArcGIS. Spatial Scan Statistics, using SaTScan software, was applied to investigate geographical clusters of FMD cases across the country. Overall, spatial heterogeneity was identified, and the transmission pattern was different by province. Cattle have more clusters in number but smaller in size, as compared to the swine population. In addition, spatiotemporal analysis and the comparison of clustering patterns between the first 7 days and days 8 to 14 of the outbreak revealed that the strongest spatial clustering was identified at the 7-day interval, although clustering over longer intervals (8~14 days) was also observed. We further discussed the importance of time period elapsed between FMD-suspected notice and the date of confirmation, and emphasized the necessity of region-specific and species-specific control measures.