• Spatial Information Research
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• v.22 no.5
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• pp.1-8
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• 2014

Globally, mobile, cloud computing or big data are the recent marketable key terms. These trend technologies or paradigm in the ICT (Information Communication Technology) fields exert large influence on the most application fields including geo-spatial applications. Among them, cloud computing, though the early stage in Korea now, plays a important role as a platform for other trend technologies uses. Especially, mobile cloud, an integrated platform with mobile device and cloud computing can be considered as a good solution to overcome well known limitations of mobile applications and to provide more information processing functionalities to mobile users. This work is a case study to design and implement the mobile application system for geo-spatial image filtering processing operated on mobile cloud platform built using OpenStack and various open sources. Filtering processing is carried out using R environment, recently being recognized as one of big data analysis technologies. This approach is expected to be an element linking geo-spatial information for new service model development and the geo-spatial analysis service development using R.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.21-36
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• 2023

Entering the era of low growth due to changes in social and economic conditions, most cities across the country are actively promoting urban regeneration. Although urban regeneration is a project with huge national finances, a clear evaluation system has not yet been established. In order to ensure the sustainability of urban regeneration, it is necessary to secure the validity of urban regeneration policies and establish a reflux system to supplement the policies. The purpose of this study is to derive the limitations of the existing comprehensive performance evaluation and to develop an improved urban regeneration policy comprehensive performance evaluation technique based on spatial big data. The urban regeneration comprehensive performance evaluation technique differentiated the areas affected by the urban regeneration project and the surrounding areas based on the type of urban regeneration project and the presence or absence of large cities and middle cities. The effects of urban regeneration were quantitatively verified through relative comparison between the areas affected by urban regeneration projects and the surrounding areas of population, society, economy, industry, physical and environmental evaluation indicators.

• Spatial Information Research
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• v.23 no.2
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• pp.69-81
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• 2015

If one can infer the residential area of SNS users by analyzing the SNS big data, it can be an alternative by replacing the spatial big data researches which result from the location sparsity and ecological error. In this study, we developed the way of utilizing the daily life activity pattern, which can be found from timeline data of tweet users, to infer the residential areas of tweet users. We recognized the daily life activity pattern of tweet users from user's movement pattern and the regional cognition words that users text in tweet. The models based on user's movement and text are named as the daily movement pattern model and the daily activity field model, respectively. And then we selected the variables which are going to be utilized in each model. We defined the dependent variables as 0, if the residential areas that users tweet mainly are their home location(HL) and as 1, vice versa. According to our results, performed by the discriminant analysis, the hit ratio of the two models was 67.5%, 57.5% respectively. We tested both models by using the timeline data of the stress-related tweets. As a result, we inferred the residential areas of 5,301 users out of 48,235 users and could obtain 9,606 stress-related tweets with residential area. The results shows about 44 times increase by comparing to the geo-tagged tweets counts. We think that the methodology we have used in this study can be used not only to secure more location data in the study of SNS big data, but also to link the SNS big data with regional statistics in order to analyze the regional phenomenon.

• Information Systems Review
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• v.20 no.2
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• pp.63-85
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• 2018

Emerging hotspot and trendy areas are formed into alleys and blocks with the help of viral effects among social network services (SNS) users called "Golmogleo." These users search for every corner of the alleys to share and promote their own favorite places through SNS. An analysis of hot places is limited if it is only based on macroeconomic indicators such as commercial area data published by national organizations, large-scale visiting facilities, and commuter figures. Careful analyses based on consumers' actual activities are needed. This study develops a "social big data analysis methodology" using Instagram data, which is one of the most popular SNSs suitable to identify recent consumer trends. We build a spatial analysis model using Local Moran's I. Results show that our model identifies new trend zones on the basis of posting data in Instagram, which are not included in the commercial information prepared by national organizations. The proposed analysis methodology enables better identification of the latest trend areas formulated by SNS user activities. It also provides practical information for start-ups, small business owners, and alley merchants for marketing purposes. This analytical methodology can be applied to future studies on social big data analysis.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.17-30
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• 2015

Local governments' spatial information policies are very important in that it can increase the relatedness to upper policy regarding the share, openness and converged utilization of spatial information and contribute to voluntary participation and creative uses linked to big data. However, local governments' spatial information policies require enhancement since it need to update framework spatial data, to derive spatial information service and to share the data. In this research, we compared the spatial information policies and related systems of central and local governments, and analyzed the local governments' spatial information policy enforcement plans and the Seoul metropolitan government's utilization survey on 32 spatial information systems. In the result, for the improvement of local governments' spatial policies, on-demand updating of base map using the as built drawings linked to field work departments, securing up-to-date public domain spatial information through the NSDI system, sharing of spatial information based on the spatial information platform and benchmarking of best practices related to the spatial information based policy participation are suggested.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.581-590
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• 2017

This study analyzes the effect of floating population, locational characteristics and spatial autocorrelation on foodservice sales using big data provided by the Seoul Institute. Although big data provided by public sector is growing recently, research difficulties are occurred due to the difference of aggregation units of data. In this study, the aggregation unit of a dependent variable, sales of foodservice is SKT unit but those of independent variables are various, which are provided as the aggregation unit of Korea National Statistical Office, administration dong unit and point. To overcome this problem, we convert all data to the SKT aggregation unit. The spatial error model, SEM is used for analysing spatial autocorrelation. Floating population, the number of nearby workers, and the area of aggregation unit effect positively on foodservice sales. In addition, the sales of Jung-gu, Yeongdeungpo-gu and Songpa-gu are less than that of Gangnam-gu. This study provides implications for further study by showing the usefulness and limitations of converting aggregation units of heterogeneous spatial data.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.273-285
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• 2003

Generally, vector spatial data, with richer information than raster spatial data enabledata, enables a mere flexible and effective manipulation of the data sets. However, one of challenges against the publication of vector spatial information on the Internet is the efficient transmission of the big and complex vector spatial datadata, which is both large and complex, across the narrow-bandwidth of the Internet. This paper proposes a new transmission method, namely, the Scale-Dependent Transmission method, with the purpose of improving the efficiency of vector spatial data transmission on the narrow-bandwidthacross the Internet. Simply put, its nam idea is “Transmit what can be seen””. Scale is regarded as a factor naturally associated with spatial features so that not all features are visible to users at a certain scale. With the aid of the Wavelet-Wavelet-based Map Generalization Algorithm, the proposed method filters out invisible features from spatial objects according to the display scale and then to transmit onlytransmits only the visible features as athe final answer for an individual operation. Experiments show that the response times ofan individual operation has been reducedoperations were substantially by the usage of reduced when using the proposed method.

• Journal of KIBIM
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• v.13 no.3
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• pp.39-47
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• 2023

This study aims to introduce a model for enhancing community well-being through the utilization of public open data. To objectively assess abstract notions of residential satisfaction, text data from complaints is analyzed. By leveraging accessible public data, costs related to data collection are minimized. Initially, relevant text data containing civic complaints is collected and refined by removing extraneous information. This processed data is then combined with meaningful datasets and subjected to topic modeling, a text mining technique. The insights derived are visualized using Geographic Information System (GIS) and Application Programming Interface (API) data. The efficacy of this analytical model was demonstrated in the Godeok/Gangil area. The proposed methodology allows for comprehensive analysis across time, space, and categories. This flexible approach involves incorporating specific public open data as needed, all within the overarching framework.

• Informatization Policy
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• v.24 no.4
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• pp.79-92
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• 2017

As city functions develop more complex and advanced, interests in smart cities are also increasing. Smart cities refer to the cities effectively solving urban problems such as traffic, safety, welfare, and living issues by utilizing ICT. Recently, many countries are attempting to introduce big data, Internet of Things, and artificial intelligence into smart cities, but they have not yet developed into comprehensive urban services. In this paper, we review the current status of domestic and overseas smart cities and suggest ways to solve issues of data sharing and service compatibility. To this end, we propose a "Deep Learning City Framework" that incorporates the deep learning technology into smart city services, and propose a new smart city strategy that safely shares spatial and temporal data in cities and converges learning data of various cities.