• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.95-95
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• 2020

Urban flooding occurs in the form of internal-water inundation on roads and lowlands due to heavy rainfall. Unlike in the case of rivers, inundation in urban areas there is lacking in research on predicting and warning through measurement data. In order to analyze urban flood patterns and prevent damage, it is necessary to analyze flooding measurement data for various rainfalls. In this study, the pattern of urban flooding caused by rainfall was analyzed by utilizing the urban flooding measuring sensor, which is being test-run in the flood prone zone for urban flooding management. For analysis, 2019 rainfall data, surface water depth data, and water level data of a street inlet (storm water pipeline) were used. The analysis showed that the amount of rainfall that causes flooding in the target area was identified, and the timing of inundation varies depending on the rainfall pattern. The results of the analysis can be used as verification data for the urban inundation limit rainfall under development. In addition, by using rainfall intensity and rainfall patterns that affect the flooding, it can be used as data for establishing rainfall criteria of urban flooding and predicting that may occur in the future.

• Journal of KIBIM
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• v.13 no.4
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.645-657
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• 2021

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.65-76
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• 2023

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban characteristics that affect them by utilizing the big data of the S-Dot sensors in Seoul, which has recently become a hot topic. In other words, it proposes a big data based urban computing research methodology and research direction to confirm the relationship between urban characteristics and living environments that directly affect citizens. The temporal range is from 2020 to 2021, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500mX500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning factors of each cluster, basic statistical analysis such as ANOVA, OLS and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of environment elements(PM^2.5, PM¹⁰, Noise) and urban factors that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to urban computing field, especially related to environmental data in the future.

• Journal of KIBIM
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• v.7 no.2
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• pp.36-43
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• 2017

Population growth and rapid urbanization has been converting large amounts of rural vegetation into urbanized areas. This human induced change has increased temperature in urban areas in comparison to adjacent rural regions. Various studies regarding to urban heat island have been conducted in different disciplines in order to analyze the environmental issue. Especially, different types of thermal infrared remote sensing data are applied to urban heat island research. This article reviews research focusing on thermal infrared remote sensing for urban heat island and urban planning studies. Seven studies of analyses for the relationships between urban heat island and other dependent indicators in urban planning discipline are reviewed. Despite of different types of thermal infrared remote sensing data, units of analysis, land use and land cover, and other dependent variable, each study results in meaningful outputs which can be implemented in urban planning strategies. As the application of thermal infrared remote sensing data is critical to measure urban heat island, it is important to understand its advantages and disadvantages for better analyses of urban heat island based on this review. Despite of its limitations - spatial resolution, overpass time, and revisiting cycle, it is meaningful to conduct future research on urban heat island with thermal infrared remote sensing data as well as its application to urban planning disciplines. Based on the results from this review, future research with remotely sensed data of urban heat island and urban planning could be modified and better results and mitigation strategies could be developed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.125-135
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• 2020

The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.100-110
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• 2020

Recently, the frequency of typhoon and torrential rain due to climate change is increasing. In addition, the upsurge in the complexity of urban sewer network and impervious surfaces area aggravates the inland flooding damage. In response to these worsening situations, the central and local governments are conducting R&D tasks related to predict and mitigate the flood risk. Researches on the analysis of inundation in urban areas have been implemented through various ways, and the common features were to evaluate the accuracy and justification of the model by comparing the model results with the actual inundation data. However, the evaluation procesure using available urban flooding data are not consistent, and if there are no quantitative urban inundation data, verification has to be performed by using press releases, public complaints, or photos of inundation occurring through 'CCTV'. Because theses materials are not quantitative, there is a problem of low reliability. Therefore, this study intends to develop a comparative analysis procedure on the quantitative degree and applicability of the verifiable inundation data, and a systematic framework for the performance assessment of urban flood analysis model was proposed. This would contribute to the standardization of the evaluation and verification procedure for urban flooding modelling.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.499-505
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• 2007

A method of improving the correctness and confidence in land use classification as well as urban spatial structure analysis of local governments using GIS and satellite imagery is suggested. This study also compares and analyzes LSAS (Land Suitability Assessment System) results using two approaches-LSAS with priority classification, and LSAS using standard estimation factors without priority classification. The conclusions that can be drawn from this study are as follows. First, a method of maintaining up-to-date local government data by updating the LSAS database using high-resolution satellite imagery is suggested. Second, to formulate a scientific and reasonable land use plan from the viewpoint of territory development and urban management, a method of simultaneously processing the two described approaches is suggested. Finally, LSAS was constructed by using varieties of land information such as the cadastral map, the digital topographic map, varieties of thematic maps, and official land price data, and expects to utilize urban management plan establishment widely and effectively through regular data updating and problem resolution of data accuracy.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.509-517
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• 2020

Geographic information system (GIS) sewer network data are a fundamental input material for urban inundation modeling, which is important to reduce the increasing damages from urban inundation due to climate change. However, the essential attributes of the data built by a local government are often missing because the purpose of building the data is the maintenance of the sewer system. Inconsistent simplification and supplementation of the sewer network data made by individual researchers may increase the uncertainty of flood simulations and influence the inundation analysis results. Therefore, it is necessary to develop a basic algorithm to convert the GIS-based sewage network data into input data that can be used for inundation simulations in consistent way. In this study, the format of GIS-based sewer network data for a watershed near the Sadang Station in Seoul and the Oncheon River Basin in Busan was investigated, and a missing data supplementing algorithm was developed. The missing data such as diameter, location, elevation of pipes and manholes were assumed following a consistent rule, which was developed referring to government documents, previous studies, and average data. The developed algorithm will contribute to minimizing the uncertainty of sewer network data in an urban inundation analysis by excluding the subjective judgment of individual researchers.

• Architectural research
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• v.14 no.2
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• pp.45-56
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• 2012

The high rate of urban crime is a main issue that needs to be dealt with in this high-tech society. With the rapid increase of urban crime, research has mainly focused on topics either on a global or a local scale, such as cities or communities and houses or buildings, without reliable observational data. This study makes the best use of the nationwide surveys carried out by Korean government agencies for the analysis of urban crime patterns and factors in major Korean cities. The aims of this research are threefold: understanding the relationship between urban crime patterns and socio-economic differences in cities, determining the effect of residence types on the urban crime patterns; and uncovering potential influential factors of a crime victim's individual characteristics. The statistical methods used for the analysis of social statistical data are as follows: simple regression, logistic regression, one-way ANOVA and post-hoc test. This research found that the patterns of urban crime rate in cities have a certain tendency toward the cities' socio-economic and geographical differences. The residence type is an influential factor showing a close relation to the crime rate. Personal issues, such as the types of occupation, education, marriage, etc., are directly relevant to victims of crime.