• Title/Summary/Keyword: Spatial big data

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Development of Facility Management System for Indoor Space Based on ICBM Technology (ICBM기반 실내 공간 유지관리 시스템 개발)

  • Jung, Yoo-Seok;Kang, Tae-Wook
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.20 no.3
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    • pp.49-55
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    • 2019
  • An open office or a shared office is emerging as the emphasis on the collaborative and communicative work environments is increasing. In the past, the user maintained the space, but the maintenance of indoor space became difficult because there is no fixed user. Indoor space information can be collected using the ICBM framework system. The facility management can achieve this with data. Therefore, this study proposed a framework based on ICBM (Internet of Things, Cloud, Big Data, and Mobile) for verifying the possibility of a smart facility management system for indoor space. IoT (Internet of Things) technology was used to measure the indoor temperature, humidity, occupancy, and brightness continuously, and provided the data to Web API via WiFi. Data acquired automatically via IoT, existing maintenance data, and spatial information were integrated through the Cloud. Big data collected by sensors were processed as meaningful spatial information for maintenance. Indoor space information and maintenance information can be delivered to the manager through the mobile. Based on the collected data, room occupancy recognition is limited due to a range of ultrasonic wave sensors. On the other hand, brightness represents the space conditions. The difference between lighting on/off, weekday and weekend can be shown. The temperature data and the relative humidity data were collected steadily to evaluate the comfort.

Production of Agrometeorological Information in Onion Fields using Geostatistical Models (지구 통계 모형을 이용한 양파 재배지 농업기상정보 생성 방법)

  • Im, Jieun;Yoon, Sanghoo
    • Journal of Environmental Science International
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    • v.27 no.7
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    • pp.509-518
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    • 2018
  • Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

A Missing Value Replacement Method for Agricultural Meteorological Data Using Bayesian Spatio-Temporal Model (농업기상 결측치 보정을 위한 통계적 시공간모형)

  • Park, Dain;Yoon, Sanghoo
    • Journal of Environmental Science International
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    • v.27 no.7
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    • pp.499-507
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    • 2018
  • Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

Crime Incident Prediction Model based on Bayesian Probability (베이지안 확률 기반 범죄위험지역 예측 모델 개발)

  • HEO, Sun-Young;KIM, Ju-Young;MOON, Tae-Heon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.20 no.4
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    • pp.89-101
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    • 2017
  • Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

A Gap Analysis Using Spatial Data and Social Media Big Data Analysis Results of Island Tourism Resources for Sustainable Resource Management (지속가능한 자원관리를 위한 섬 지역 관광자원의 공간정보와 소셜미디어 빅데이터 분석 결과를 활용한 격차분석)

  • Lee, Sung-Hee;Lee, Ju-Kyung;Son, Yong-Hoon;Kim, Young-Jin
    • Journal of Korean Society of Rural Planning
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    • v.30 no.2
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    • pp.13-24
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    • 2024
  • This study conducts an analysis of social media big data pertaining to island tourism resources, aiming to discern the diverse forms and categories of island tourism favored by consumers, ascertain predominant resources, and facilitate objective decision-making grounded in scientific methodologies. To achieve this objective, an examination of blog posts published on Naver from 2022 to 2023 was undertaken, utilizing keywords such as 'Island tourism', 'Island travel', and 'Island backpacking' as focal points for analysis. Text mining techniques were applied to sift through the data. Among the resources identified, the port emerged as a significant asset, serving as a pivotal conduit linking the island and mainland and holding substantial importance as a focal point and resource for tourist access to the island. Furthermore, an analysis of the disparity between existing island tourism resources and those acknowledged by tourists who actively engage with and appreciate island destinations led to the identification of 186 newly emerging resources. These nascent resources predominantly clustered within five regions: Incheon Metropolitan City, Tongyeong/Geoje City, Jeju Island, Ulleung-gun, and Shinan-gun. A scrutiny of these resources, categorized according to the tourism resource classification system, revealed a notable presence of new resources, chiefly in the domains of 'rural landscape', 'tourist resort/training facility', 'transportation facility', and 'natural resource'. Notably, many of these emerging resources were previously overlooked in official management targets or resource inventories pertaining to existing island tourism resources. Noteworthy examples include ports, beaches, and mountains, which, despite constituting a substantial proportion of the newly identified tourist resources, were not accorded prominence in spatial information datasets. This study holds significance in its ability to unearth novel tourism resources recognized by island tourism consumers through a gap analysis approach that juxtaposes the existing status of island tourism resource data with techniques utilizing social media big data. Furthermore, the methodology delineated in this research offers a valuable framework for domestic local governments to gauge local tourism demand and embark on initiatives for tourism development or regional revitalization.

A Study on Big Data Information System based on Artificial Intelligence -Filmmaker and Focusing on Movie case analysis of 10 million Viewers- (인공지능 기반형 빅데이터 정보시스템에 관한 연구 -영화제작자와 천만 영화 사례분석 중심으로-)

  • Lee, Sang-Yun;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.2
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    • pp.377-388
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    • 2019
  • The system proposed in this paper was suggested as a big data system that works in the age of artificial intelligence of the 4th Industrial Revolution. The proposed system can be a good example in terms of government 's development of new intelligent big data information system. For example, the proposed system may be introduced into the system of a department as a function of the integration of existing cinema ticket integration network or its networking. For this purpose, the proposed system transmits the user's profile to the film producer or other company, where it is provided as comparison data. Soon, the information is sent to the user-specific characteristic data and then the film-maker will be able to gauge the success of the three elements of the movie's performance, cinematic quality, and break-even point in real time, which are revealed through the movie review that the actual user feels, including the so-called 'new reinterpretation.

Priority Area Prediction Service for Local Road Packaging Maintenance Using Spatial Big Data (공간 빅데이터를 활용한 지방도 포장보수 우선지역 예측 서비스)

  • Minyoung Lee;Jiwoo Choi;Inyoung Kim;Sujin Son;Inho Choi
    • Journal of Intelligence and Information Systems
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    • v.29 no.3
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    • pp.79-101
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    • 2023
  • The current status of local road pavement management in Jeollabuk-do only relies on the accomplishments of the site construction company's pavement repair and is only managed through Microsoft Excel and word documents. Furthermore, the budget is irregular each year. Accordingly, a systematic maintenance plan for local roads is necessary. In this paper, data related to road damage and road environment were collected and processed to derive possible areas which could suffer from road damage. The effectiveness of the methodology was reviewed through the on-site inspection of the area. According to the Ministry of Land, Infrastructure and Transport, in 2018, the number of damages on general national roads were about 47,000. In 2019, it reached around 38,000. Furthermore, the number of lawsuits regarding the road damages were about 93 in 2018 and it increased to 119 in 2019. In the case of national roads, the number of damages decreased compared to 2018 due to pavement repairs. To measure the priorities in maintenance of local roads at Jeollabuk-do, data on maintenance history, local port hole occurrence site, overlapping business section, and emergency maintenance section were transformed into data. Eventually, it led to improvements in maintenance of local roads. Furthermore, spatial data were constructed using various current status data related to roads, and finally the data was processed into a new form that could be utilized in machine learning and predictions. Using the spatial data, areas requiring maintenance on pavement were predicted and the results were used to establish new budgets and policies on road management.

Estimating Visitors on Water-friendly Space in the River Using Mobile Big Data and UAV (통신 빅데이터와 무인기 영상을 활용한 하천 친수지구 이용객 추정)

  • Kim, Seo Jun;Kim, Chang Sung;Kim, Ji Sung
    • Ecology and Resilient Infrastructure
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    • v.6 no.4
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    • pp.250-257
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    • 2019
  • Recently, 357 water-friendly space were established near the main streams of the country through the Four Major Rivers Project, which was used as a resting and leisure space for the citizens, and the river environment and ecological health were improved. We are working hard to reduce the number of points and plan and manage the water-friendly space. In particular, attempts are being made to utilize mobile big data to make more scientific and systematic research on the number of users. However, when using mobile big data compared to the existing method of conducting field surveys, it is possible to easily identify spatial user movement patterns, but it is different from the actual amount of use, so various verifications are required to solve this problem. Therefore, this study evaluated the accuracy of estimating the number of users using mobile big data by comparing the number of visitors using mobile big data and the number of visitors using drone for Samrak ecological park located in the mouth of Nakdong River. As a result, in the river hydrophilic district, it was difficult to accurately estimating the usage pattern of each facility due to the low precision of pCELL, and it was confirmed that the usage patterns in the park could be distorted due to the signals stopped at roads and parking lots. Therefore, it is necessary to improve the number of pCELLs in the water-friendly space and to estimate the number of visitors excluding facilities such as roads and parking lots in future mobile big data processing.

A Study on the Regionally Customized Urban Regeneration and Maintenance of Small and Medium Cities Using Spatial Big-Data - Focused on the Residential Census Output Area - (공간 빅데이터를 활용한 중소도시 지역맞춤형 도시재생·유지관리 연구 - 주거지역 집계구를 중심으로 -)

  • Han, Da-Hyuck;Lee, Min-Seok
    • Journal of the Korean Institute of Rural Architecture
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    • v.23 no.2
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    • pp.9-16
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    • 2021
  • The purpose of this study is to maintain the existing characteristics of the city by utilizing the physical decline status and floating population in small and medium cities residential areas. In addition, it intends to present the direction of flexible urban regeneration and maintenance by reflecting regional characteristics and current status. A total of three data were used in this study. Building data, floating population data, and census output area data were used. Building data and floating population data were classified into five classes. The graded data were joined to the census output area data and analyzed by overlapping the two data. As a result of analysis of 17 residential areas in 5 small and medium cities in Jeollanam-do, 4 types, 2 management models, and 4 indicators could be presented by grade and regional characteristics. This study is meaningful in that it is possible to plan regionally customized urban regeneration/maintenance management plans and projects through the typology of the current status and characteristics of the region, which is an important step in the bottom-up form.

Application Verification of AI&Thermal Imaging-Based Concrete Crack Depth Evaluation Technique through Mock-up Test (Mock-up Test를 통한 AI 및 열화상 기반 콘크리트 균열 깊이 평가 기법의 적용성 검증)

  • Jeong, Sang-Gi;Jang, Arum;Park, Jinhan;Kang, Chang-hoon;Ju, Young K.
    • Journal of Korean Association for Spatial Structures
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    • v.23 no.3
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    • pp.95-103
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    • 2023
  • With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.