• Journal of Industrial Convergence
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• v.21 no.6
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• pp.13-21
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• 2023

The purpose of this study is to investigate the relationship between the amount of media coverage related to drug crimes and the actual number of drug-related arrests. It aims to determine the implications of this relationship for the formulation of police policies in response to drug crimes. To achieve this, we collected and analyzed 50,355 domestic online media coverage data from 2001 to 2022 and drug crime arrest data compiled by the National Police Agency. The analysis confirmed a significant causal relationship between the amount of online media coverage of drug crimes and the actual number of drug crime arrests. This relationship was found to be influenced by the existence of dominant issues and the relevance of famous incidents. It was determined that media coverage was also influenced by public interest beyond drug crime arrest numbers. Based on these research findings, the police have proposed the need to monitor the amount of crime-related media coverage and enhance security capabilities for crimes that receive public attention in order to gain the trust of the citizens.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1101-1104
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• 2021

최근 COVID-19(코로나 바이러스 감염증) 확산에 따라 다양한 분야에서 힘든 상황이 이어지고 있다. 중앙 재난 안전 대책본부에 따르면 지난 2월 한 달간 코로나 안전신고는 약 2만 5천 건의 방역수칙 위반 신고가 들어온 것으로 집계됐다. 이에 따라 음식점 및 매장은 QR코드. 수기 작성을 통한 동선 체크, 온도 검사 등 코로나 확산을 방지하기 위한 방법을 시행하고 있지만 이는 단지 코로나 확산 방지를 위한 대책 일뿐 소상공인의 매장 운영이나 안정적인 영업 유지 등 직접적인 영향을 줄 수 없다. 이에 본 논문은 OpenCV를 활용한 고객 출입 관리 시스템을 제안한다. 본 시스템은 OpenCV 영상처리기술을 활용하여 매장을 방문하는 고객의 나이, 성별을 수집하여 주요 고객층 분석, 출입 현황 및 이용 시간을 파악한다. 본 시스템은 코로나 확진자 동선 파악을 위한 역학조사와 소상공인의 효율적인 매장 운영 시간을 분석하여 '코로나 확산 방지', '소상공인 매출 증가'의 기대 효과를 얻을 수 있다. 향후, 제안하는 기법의 실질적인 검증을 위해 실제 매장 환경에서의 테스트가 필요하다.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.109-124
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• 2017

Through a wide variety of disciplines, practices on data access and re-use have been increased recently. In fact, there has been an emerging phenomenon that researchers tend to use the data sets produced by other researchers and give scholarly credit as citation. With respect to this practice, in 2012, Thomson Reuters launched Data Citation Index (DCI). With the DCI, citation to research data published by researchers are collected and analyzed in a similar way for citation to journal articles. The purpose of this study is to identify the characteristics and intellectual structure of sociology field based on research data, which is one of actively data-citing fields. To accomplish this purpose, two data sets were collected and analyzed. First, from DCI, a total of 8,365 data were collected in the field of sociology. Second, a total of 12,132 data were collected from Web of Science with a topic search with 'Sociology'. As a result of the co-word analysis of author provided-keywords for both data sets, the intellectual structure of research data-based sociology was composed of two areas and 15 clusters and that of article-based sociology was composed with three areas and 17 clusters. More importantly, medical science area was found to be actively studied in research data-based sociology and public health and psychology are identified to be central areas from data citation.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.59-71
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• 2014

At present, Korean one-person households have been continuously increased in spite of the reduction of total population. The increasement of one-person household has become a social and institutional issue. It is necessary to response socially and economically to not only changes of housing demand but also the disadvantaged classes such as the socially weak and single elderly household from the national level. In this respect, this research examined the spatial distribution (such as the increasing area, high-density area, and majority area) of one-person household with census data in the city of Busan. The clusters of one-person households were selected by focusing on the spatial distributions by time series changes of 2000, 2005, and 2010 and considering their housing characteristics. In terms of policy efficiency, the clusters of one-person households to be supported by priority were derived by analyzing the census data from 6066 output areas in the city of Busan. As a result, lots of one-person households of juniors were distributed around the university town, office facility, and station service area. Lots of one-person households at middle-aged class were distributed in Busan's original downtown and mountain-side road. Generalizing these characteristics, cluster analysis was conducted. As a result, one-person household dense area in Busan could be classified into four types. This research should be utilized as a counterplan for increasing the housing demand of one-person household or basic data for supporting small housing supply policies in the future.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.589-599
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• 2021

Purpose: A reliable indicator of congested traffic speed is essential in providing the information of traffic flow states about motorway sections. The aim of this study is to propose an adaptive indicator of congested speed which is employed for deciding the traffic flow states for individual motorway sections using disaggregated section-based speed data. Method: Typically, the state of traffic flow is categorized into the three: uncongested, mixed, congested states. A method, presented in this study, was developed for identifying boundary speed values of road sections through categorizing the three traffic flow states with individual vehicular speed values. The boundary speed state of each road segment is determined using the speed distributions of mixed and congested traffic states. Result: Analysis results revealed that boundary speed values between mixed and congested states for road sections were similar to those of US and EU criteria (i.e., 48.28~66.0 kph). This indicates that boundary speed values could be different according to road sections. Conclusion: It is expected that the method and indicator, proposed in this study, could be efficaciously used for providing ad-hoc real-time traffic states and computing traffic congestion costs for motorway sections in the era of big data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.669-678
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• 2018

This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.217-233
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• 2020

This study evaluated the urban vitality of Daegu metropolitan city in 2018 using emerging geographic data such as spatial big data, Wi-Fi AP(access points) and nighttime light satellite image. The emerging geographic data were used in this research to quantify human activities in the city more directly at various spatial and temporal scales. Three spatial big data such as mobile phone data, credit card data and public transport smart card data were employed to reflect social, economic and mobility aspects of urban vitality while public Wi-Fi AP and nighttime light satellite image were included to consider virtual and physical aspects of the urban vitality. With PCA (Principal Component Analysis), five indicators were integrated and transformed to the urban vitality index at census output area by temporal slots. Results show that five clusters with high urban vitality were identified around downtown Daegu, Daegu bank intersection and Beomeo intersection, Seongseo, Dongdaegu station and Chilgok 3 district. Further, the results unveil that the urban vitality index was varied over the same urban space by temporal slots. This study provides the possibility for the integrated use of spatial big data, Wi-Fi AP and nighttime light satellite image as proxy for measuring urban vitality.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1811-1825
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• 2011

This study analyzed the transit services with respect to transit service measures such as the load factor representing number of passengers between stops, dwelling time, and operational speed based on transit smart card data recorded in 2009. A case study on the local bus line 7024 connecting Seoul railway station to evaluate bus services at passenger perspectives was accomplished. From the results, we found that the dwelling time was not affected by the number of passengers which is because the tagging patterns are different among passengers. The operational speed was analyzed by calculating the average speed of the bus route and the speed of each bus stops based on dwelling time. Interestingly, calculating operation speed based on the transit smart card data is the first time effort ever made and this means that it is not necessary to observe travel speed of bus and railway at a field level any more. we hope that this study will be a basis of evaluation of transit services purely based on the transit smart card data and help to make better transit services for passengers and operators as well.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.157-163
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• 2018

People who use SNS publish their diverse ideas on SNS every day. The data posted on the SNS contains many people's thoughts and opinions. In particular, popular keywords served on Twitter compile the number of frequently appearing words in user posts and rank them. However, this method is sensitive to unnecessary data simply by listing duplicate words. The proposed method determines the ranking based on the topic of the word using the relationship diagram between words, so that the influence of unnecessary data is less and the main word can be stably extracted. For the performance comparison in terms of the descending keyword rank and the ratios of meaningless keywords among high rank 20 keywords, we make a comparison between the proposed scheme which is based on morphological analysis and PageRank, and the existing scheme which is based on the number of appearances. As a result, the proposed scheme and the existing scheme have included 55% and 70% of meaningless keywords among high rank 20 keywords, respectively, where the proposed scheme is improved about 15% compared with the existing scheme.