• Title/Summary/Keyword: Spatio-temporal Cluster Analysis

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Spatio-Temporal Clustering Analysis of HPAI Outbreaks in South Korea, 2014 (2014년 국내 발생 HPAI(고병원성 조류인플루엔자)의 시·공간 군집 분석)

  • MOON, Oun-Kyong;CHO, Seong-Beom;BAE, Sun-Hak
    • Journal of the Korean Association of Geographic Information Studies
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    • v.18 no.3
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    • pp.89-101
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    • 2015
  • Outbreaks of highly pathogenic avian influenza(HPAI) subtype H5N8 have occurred in Korea, January 2014 and it continued more than a year until 2015. And more than 5 million heads of poultry hads been damaged in 196 farms until May 2014. So, we studied the spatial, temporal and spatio-temporal patterns of the HPAI epidemics for understanding the propagation and diffusion characteristics of the 2014 HPAI. The results are expressed using GIS. Throughout the study period three epidemic waves occurred over the time. And outbreaks made three clusters in space. First spatial cluster is adjacent areas of province of Chungcheongbuk-do, Chungcheongnam-do and Gyeonggi -do. Second is Jeonlabuk-do Gomso Bay area. And the last is Naju and Yeongam in Jeollanam-do. Also, most of spatio-temporal clusters were formed in spatially high clustered areas. Especially, in Gomso Bay area space density and spatio-temporal density were concurrent. It means that the effective prevention activity for HPAI was carried out. But there are some exceptional areas such as Chungcheongbuk-do, Chungcheongnam-do, Gyeonggi-do adjacent area. In these areas the outbreak density was high in space but the spatio-temporal cluster was not formed. It means that the HPAI virus was continuing inflow over a long period.

Analyzing the Spatio-temporal Trend in TMDL Water Quality for Gyeongnam Using Emerging Hot Spot Analysis (수질오염총량제 대응을 위한 경남 하천 수질의 시공간 경향성 분석)

  • Sun, Danbee;Kim, Jiho;Kim, Sangmin;Jang, Min-Won
    • Journal of Korean Society of Rural Planning
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    • v.26 no.4
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    • pp.53-65
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    • 2020
  • This study aimed to provide a basic information for managing the water quality of national and regional 1st rivers in Gyeongnam by analyzing the emerging hot spot patterns in BOD, T-P, and TOC, and by grouping the changing trends into clusters. The emerging hot spot analysis for each water quality item was implemented in ArcGIS Desktop with monthly water quality data from 96 water environmental monitoring stations in Gyeongnam, and then four patterns of water quality change were classified by the K-mean cluster analysis. As for BOD, persistent cold spot pattern covered about 42.9% of target rivers, and T-P concentration tended to be low or be getting lower at over 70% of target rivers. While, for TOC, about 70% of target rivers resulted in oscillating hot spots. In addition, the cluster analysis showed that the downstream of Nakdong river had the top priority in terms of water quality management because of the increasing concentration for all the three water quality.

Base Location Prediction Algorithm of Serial Crimes based on the Spatio-Temporal Analysis (시공간 분석 기반 연쇄 범죄 거점 위치 예측 알고리즘)

  • Hong, Dong-Suk;Kim, Joung-Joon;Kang, Hong-Koo;Lee, Ki-Young;Seo, Jong-Soo;Han, Ki-Joon
    • Journal of Korea Spatial Information System Society
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    • v.10 no.2
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    • pp.63-79
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    • 2008
  • With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

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Hotspot Analysis of Urban Crime Using Space-Time Scan Statistics (시공간검정통계량을 이용한 도시범죄의 핫스팟분석)

  • Jeong, Kyeong-Seok;Moon, Tae-Heon;Jeong, Jae-Hee
    • 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.

Spatio-temporal Analysis using Real-Time Data Processing for Wireless Sensor Networks (무선 센서 네트워크에서 실시간 데이터 처리를 이용한 시공간 분석)

  • Baek, Jeong-Ho;Mun, Young-Chae;Lee, Hong-Ro
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.6
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    • pp.688-692
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    • 2010
  • Wireless sensor network system collects and analyzes real-time data that have been requested by the many application nodes. This paper has constructed a sensor network cluster with various elements in the Gunsan City area of Jeollabuk-do, S.korea. The purpose of this paper is to utilize the constructed system in order to illustrate the real-time data in a diagram and analyze it to deduce the change ratio. The resulting analysis contents allow simple data interpretation by illustrating the data in change ratio by time, space, and motional directions. This analytical method will offer great benefit to those users using the wireless sensor network.

Cluster exploration of water pipe leak and complaints surveillance using a spatio-temporal statistical analysis (스캔통계량 분석을 통한 상수도 누수 및 수질 민원 발생 클러스터 탐색)

  • Juwon Lee;Eunju Kim;Sookhyun Nam;Tae-Mun Hwang
    • 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.

Spatio-temporal Distribution of Macrobenthic Communities in Jinhae Bay, Korea (진해만 특별관리해역의 대형저서동물의 시공간 분포)

  • Seo, Jin-Young;Lim, Hyun-Sig;Choi, Jin-Woo
    • Ocean and Polar Research
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    • v.37 no.4
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    • pp.295-315
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    • 2015
  • In order to determine the spatio-temporal distribution of macrobenthic faunal communities in Jinhae Bay, quantitative faunal samples were collected seasonally at 23 sites in Jinhae Bay from February, 2011 to November, 2012. Sediment facies were found to be mud except for those at Chilcheon-do near Geoje Island. Mean values of TOC (%) ranged between 1.3 and 3.6%, and these are the highest values recorded excluding special management areas in Korea. Hypoxia occurred every summer in the whole areas of Jinhae Bay except around Geoje Island in the bay mouth. Due to the summer hypoxia, species richness, density and biomass also declined during the summer in Jinhae Bay. Opportunistic species such as Paraprionospio patiens, Sigambra bassi, Nectoneanthes oxypoda and Theora fragilis occurred as the dominant species before and after the hypoxia. However, Capitella capitata appeared as a dominant species only during the winter-spring season every year. From cluster analysis, Jinhae Bay could be divided into two sites groups: one group occupied the normoxic zone and the other one located in the hypoxic zone.

Analysis of W-CDMA System with Smart Antenna for Different Bandwidths in Wideband Multipath Channel (광대역 다중경로 채널에서 스마트 안테나를 적용한 W-CDMA 시스템의 대역폭에 따른 성능분석)

  • 전준수;이주석 ;김철성
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.40 no.2
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    • pp.47-55
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    • 2003
  • In this paper, the performance of DS-CDMA system with smart antenna is analyzed for different bandwidths (1.25MHz,5MHz) and different channel environments (rural, urban) in wideband multipath channel. For the analysis of smart antenna system, the vector channel having the spatio-temporal correlation is modeled as a time-variant linear filter in time, and each multipath is assumed as a reflective wave from only one direction (only one cluster) in space. Several multipath is within one chip are distingushed into each one and the strongest signal is selected, DS-CDMA system with smart antenna using wider bandwidth present better performance than that using narrow bandwidth. It is shown that the smart antenna is more effective in urban area when using 2D-RAKE receiver.

Deep Learning-Based Spatio-Temporal Earthquake Prediction (딥러닝 기반의 시공간 지진 예측)

  • Kounghoon Nam;Jong-Tae Kim;Seong-Cheol Park;Chang Ju Lee;Soo-Jin Kim;Chang Oh Choo;Gyo-Cheol Jeong
    • The Journal of Engineering Geology
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    • v.33 no.1
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    • pp.1-13
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    • 2023
  • Predicting earthquakes is difficult due to the complexity of the systems underlying tectonic phenomena and incomplete understanding of the interactions among tectonic settings, tectonic stress, and crustal components. The Korean Peninsula is located in a stable intraplate region with a low average seismicity of M 2.3. As public interest in the earthquake grows, we analyzed earthquakes on the Korean Peninsula by attempting to predict spatio-temporal earthquake patterns and magnitudes using Facebook's Prophet model based on deep learning, and here we discuss seismic distribution zones using DBSCAN, a cluster analysis method. The Prophet model predicts future earthquakes in Chungcheongbuk-do, Gyeonggi-do, Seoul, and Gyeongsangbuk-do.

Cluster analysis by month for meteorological stations using a gridded data of numerical model with temperatures and precipitation (기온과 강수량의 수치모델 격자자료를 이용한 기상관측지점의 월별 군집화)

  • Kim, Hee-Kyung;Kim, Kwang-Sub;Lee, Jae-Won;Lee, Yung-Seop
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1133-1144
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    • 2017
  • Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of $5km{\times}5km$ gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.