• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.153-169
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• 2022

With the extension of the average life span and the rapid aging of the population, defining elderly population as a single group is difficult as the physical, economic and social conditions of individual have become different. Therefore, policies that take into account the characteristics of each group are required. The purpose of this study is to classify individual travel types and to analyze the characteristics of each travel type, based on individual public transportation trajectory data as known as smart card data. Among the four classified types, the long-distance low-frequency stay type and the short-range medium-frequency mobile type show external activity traffic characteristics for retirement leisure, while the long-distance high-frequency stay type and the long-distance high-frequency mobile group include regular commuting. Traffic variability and residence areas of stay were identified in terms of each classified travel type. The results of this study provide the important suggestions for establishing a transportation policy that takes into account the characteristics of each type of elderly population in Seoul.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.113-126
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• 2017

This study aimed to quantitatively analyze the trends in environmental research that utilize environmental geospatial information through text mining, one of the big data analysis technologies. The analysis was conducted on a total of 869 papers published in the Republic of Korea, which were collected from the National Digital Science Library (NDSL). On the basis of the classification scheme, the keywords extracted from the papers were recategorized into 10 environmental fields including "general environment", "climate", "air quality", and 20 environmental geospatial information fields including "satellite image", "numerical map", and "disaster". With the recategorized keywords, their frequency levels and time series changes in the collected papers were analyzed, as well as the association rules between keywords. First, the results of frequency analysis showed that "general environment"(40.85%) and "satellite image"(24.87%) had the highest frequency levels among environmental fields and environmental geospatial information fields, respectively. Second, the results of the time series analysis on environmental fields showed that the share of "climate" between 1996 and 2000 was high, but since 2001, that of "general environment" has increased. In terms of environmental geospatial information fields, the demand for "satellite image" was highest throughout the period analyzed, and its utilization share has also gradually increased. Third, a total of 80 correlation rules were generated for environmental fields and environmental geospatial information fields. Among environmental fields, "general environment" generated the highest number of correlation rules (17) with environmental geospatial information fields such as "satellite image" and "digital map".

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.77-100
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• 2022

In the era of low growth, many provincial cities are experiencing population decline and aging. Population decline phenomena such as reduction of productive manpower, reduction of finances, deterioration of quality of life, and collapse of the community base are occurring in a chain and are being pushed to the brink of extinction of the cities. This study aims to propose a methodology to objectively estimate the employment creation centers and setting the basic unit of industrial-centered zoning by applying spatial statistical techniques and GIS for the application of the compact city plan as an efficient spatial management policy in a city with a declining population. In details, based on reviewing previous studies on compact city, 'employment complex index(ECI)' were defined considering the number of workers, the number of settlers, and the area of development land, the employment creation center was estimated by applying the 'Local Moran's I' and 'Getis-Ord's Hot-Spot Analysis'. As a case study, changes in the four years of 2013, 2015, 2017, and 2019 were compared and analyzed for Changwon City. As a result, it was confirmed that the employment creation center is becoming compacted and polycentric, which is a significant result that reflects the actual situation well. This results provide the basic data for functional and institutional territorial governance for the regional revitalization platform, and provide meaningful information necessary for spatial policy decision-making, such as population reduction, regional gross domestic product, and public facility arrangement that can respond to energy savings, transportation plans, and medical and health plans.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.279-284
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• 2022

As a study of the blended learning method on design education through the blended learning method, I would like to propose that more advanced learner-led customized design education is possible. Understanding in face-to-face classes and advantages in non-face-to-face classes can be supplemented in an appropriate way in remote classes. Advanced artificial intelligence and big data technology can provide personalized and subdivided learning materials and effective learning methods tailored to learners' levels and interests based on quantified data in design classes. In this paper, it was proposed to maximize the efficiency of the class by applying a method that exceeds the limitations of time and space through the proposal of the A La Carte model (A La Carte). It is a remote class that can be heard anytime, anywhere, and it is also possible to bridge the educational quality and educational gap provided to students living in underprivileged areas. As the goal of fostering creative convergence-type future talents, it is changing with a rapid technological development speed. It is necessary to adapt to the change in learning methods in line with this. An analysis of the infographic virtual space design and construction process through the A La Carte model (A La Carte) proposal was presented. Rather than simply acquiring knowledge, it is expected that knowledge can be sorted, distinguished, learned, and easily reborn with its own knowledge.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.79-98
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• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Land and Housing Review
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• v.13 no.4
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• pp.55-71
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• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1515-1527
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• 2016

With the growth of big data, machine learning, and cloud computing, the importance of storage that can store large amounts of unstructured data is growing recently. So the commodity hardware based distributed file systems such as MAHA-FS, GlusterFS, and Ceph file system have received a lot of attention because of their scale-out and low-cost property. For the data fault tolerance, most of these file systems uses replication in the beginning. But as storage size is growing to tens or hundreds of petabytes, the low space efficiency of the replication has been considered as a problem. This paper applied erasure coding data fault tolerance policy to MAHA-FS for high space efficiency and introduces VDelta technique to solve data consistency problem. In this paper, we compares the performance of two file systems, MAHA-FS and GlusterFS. They have different IO processing architecture, the former is server centric and the latter is client centric architecture. We found the erasure coding performance of MAHA-FS is better than GlusterFS.