• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.367-374
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• 2010

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.247-260
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• 2000

This paper is concerned with a modification of the k-spatial medians clustering. To find a suitable number of clusters, the number k of clusters is incorporated into the k-spatial medians clustering criterion through a weight function. Proposed method for the choice of the weight function offers a reasonable number of clusters. Some theoretical properties of the method are investigated along with some examples.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• 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.

• IE interfaces
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• v.24 no.4
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• pp.447-456
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• 2011

Identifying the major moving patterns of shoppers' movements in the selling floor has been a longstanding issue in the retailing industry. With the advent of RFID technology, it has been easier to collect the moving data for a individual shopper's movement. Most of the previous studies used the traditional clustering technique to identify the major moving pattern of customers. However, in using clustering technique, due to the spatial constraint (aisle layout or other physical obstructions in the store), standard clustering methods are not feasible for moving data like shopping path should be adjusted for the analysis in advance, which is time-consuming and causes data distortion. To alleviate this problems, we propose a new approach to spatial pattern clustering based on longest common subsequence (LCSS). Experimental results using the real data obtained from a grocery in Seoul show that the proposed method performs well in finding the hot spot and dead spot as well as in finding the major path patterns of customer movements.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.259-266
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• 2010

Forest fire occurrence in Korea is highly related to human activities and its spatial distribution shows a strong spatial dependency with cluster pattern. In this study, we analyzed spatial distribution pattern of forest fire with point pattern analysis considering spatial dependency. Distributional pattern was derived from Ripley's K-function according to causes and distances. Spatially clustered intensity was found out using Kernel intensity estimation. As a result, forest fires in Korea show clustered pattern, although the degrees of clustering for each cause are different. Furthermore, spatial clustering pattern can be classified into two groups in terms of degrees of clustering and distance. The first group shows the national-wide cluster pattern related to the human activity near forests, such as human-induced accidental fire in mountain and field incineration. Another group shows localized cluster pattern which is clustered within a short distance. It is associated with the smoker fire, arson, accidental by children. The range of localized clustering was 30 km. Beyond of this range, the patterns of forest fire became random distribution gradually. Kernel intensity analysis showed that the latter group, which have localized cluster pattern, was occurred in near Seoul with high densed population.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.325-336
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• 2023

In August 2022, even though flood damage occurred in the metropolitan area due to heavy rain, drought warnings were issued in Jeolla province, which indicates that the regional drought is intensified recent years. To cope with regarding intensified regional droughts, many studies have been conducted to identify spatial patterns of the occurrence of meteorological drought, however, case studies of spatial clustering for water shortage are not sufficient. In this study, using the estimations of water shortage in the Han River Basin in 2030 of the Master Plans for National Water Management, the spatial characteristics of water shortage were analyzed to identify the hotspot areas based on the Local Moran's I and Getis-Ord Gi^*, which are representative indicators of spatial clustering analysis. The spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The overall results of for three anayisis periods (S0(1967-1983), S1(1984-2000), S2(2001-2018)) indicated that the lower Imjin River (#1023) was the hotspot for water shortage, and there are moving patterns of water shortage from the east of lower Imjin River (#1023) to the west during S2 compared to S0 and S1. In addition, the Yangyang-namdaecheon (#1301) was the HL area that is adjacent to a high water shortage area and a low water shortage area, and had water shortage pattern in S2 compared to S0 and S1.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.296-299
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• 2005

Time series of spring discharge data in Jeju island can provide abundant information on the spatial groundwater system. In this study, the classification based on time series of spring discharge was performed with clustering analysis: discharge rate and EC. Peak discharges are mainly observed in august or september. However, double peaks and late peaks of discharge are also observed at a plenty of springs. Based on results of clustering analysis, it can be deduced that GH model is not appropriate for the conceptual model of Groundwater system in Jeju island. EC distributions in dry season are also support the conclusion.