• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.731-740
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• 1999

Autocorrelation function is widely used as a tool measuring linear dependence of hydrologic time series. However, it may not be appropriate for choosing decorrelation time or delay time ${\tau}_d$ which is essential in nonlinear dynamics domain and the mutual information have recommended for measuring nonlinear dependence of time series. Furthermore, some researchers have suggested that one should not choose a fixed delay time ${\tau}_d$ but, rather, one should choose an appropriate value for the delay time window ${\tau}_d={\tau}(m-1)$, which is the total time spanned by the components of each embedded point for the analysis of chaotic dynamics. Unfortunately, the delay time window cannot be estimated using the autocorrelation function or the mutual information. Basically, the delay time window is the optimal time for independence of time series and the delay time is the first locally optimal time. In this study, we estimate general dependence of hydrologic time series using the C-C method which can estimate both the delay time and the delay time window and the results may give us whether hydrologic time series depends on its linear or nonlinear characteristics which are very important for modeling and forecasting of underlying system.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.73-85
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• 2016

Systematic and efficient forestry management is required because of the long-term low profitability of timber production and forest products. In this situation, non-timber forest products can be the solution to secure stable sources of income for workers in the forestry field. However, the existing studies for non-timber forest products focus on effective production and economic analysis and provide plans for increasing the income and improving the marketing system. Therefore, this research intends to analyze the spatial distribution as well as quantitative concentration of non-timber forest production. To achieve this goal, this study examined the regional concentration and dispersion of non-timber forest production in 2001, 2007, and 2014 using the coefficient of localization(CL) and location quotient(LQ) and investigated the change in spatial distribution using spatial statistics. The production of chestnuts generally showed a concentrated pattern in 2014 based on the outputs of the CL and LQ, but the result of spatial autocorrelation indicated a decrease in the spatial concentration. In addition, astringent persimmon showed more concentration from the output of CL than oak mushroom, but Moran's I suggests the opposite. Therefore, it is necessary to examine the spatial distribution to understand and improve the marketing system and intensify the production of forest products.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.507-517
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• 2012

The main purpose of this study is to address flooding resilient land use management strategy based on the distributional characteristics of storm damage areas in Gyeonggi-do. The employed methods are 1) Exploratory Spatial Data Analysis (ESDA) to understand the spatial patterns of storm damage areas occurred from 2005 to 2009, 2) Local Indicator of Spatial Association (LISA) to examine spatial autocorrelation existed in storm damage areas for the year of 2009. The results show that 1) crop land damage is very sensitive to heavy precipitation, 2) damaged buildings are found in all over the Gyeonggi areas, but relatively more damages are in the regions closed to the City of Seoul, 3) damaged roads-bridges, streams, and reaches are found in mostly rural areas, 4) building and crop land damage occurs mostly in lowlands with different spatial patterns. These findings imply that 1) it will be useful to consider the average distances and slopes of damaged building and crop lands from streams for the decision making of land use management strategy, 2) further management efforts are required in the north, east, and south regions of Gyeonggi areas to prevent roads-bridge, stream, and reach damages, 3) the present land use pattern needs to be carefully investigated by considering the damage clustered areas for the year of 2009 based on watershed and municipality boundaries.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.456-456
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• 2012

한강유역에 위치한 247개의 강우계에서 관측된 강우 자료를 분석하여 Modified Bartlett-Lewis Retangular Pulse Model (MBLRPM)의 매개변수들을 산정하고, 이들의 지도를 작성한 후, 이들의 정확도 및 매개변수들의 시/공간적 변화 유형을 분석하였다. 이를 위한 첫번째 과정으로, 각 강우 게이지에 대해 MBLRPM의 매개변수에 사용되는 통계치 (각 달에 대한 1, 3, 12, 24시간 누적 수준에서의 평균, 분산, 자기 상관계수, 무강우 확률)들을 계산한다. 이 후, 격자화된 한강유역의 각 셀에 대하여 앞서 계산된 강우 통계치를 Ordinary Kriging 공간 보간법을 통하여 할당한다. 이 후, 각 셀에 할당된 강우 통계치를 사용하여 MBLRPM의 매개변수들을 산정하여 각 매개변수들의 지도를 각 달에 대하여 얻는다. 매개변수 지도를 사용하여 MBLRPM에 의해 생성된 강우 데이터들은 관측치의 통계치를 정확성있게 재현하였으며, 시/공간적 경향성을 분석한 결과, 강우세포의 지속기간과 관련된 매개 변수를 제외한 나머지 5개의 매개변수들은 확연한 공간적 경향성을 보인 한 편, 시간적 경향성은 잘 나타나지 않았다. 본 연구 결과는 매개변수 산정이 힘든 MBLRPM의 특성을 극복하게 해주어 가상 강우 생성을 용이하게 함으로써 강우에 영향을 받는 여러 종류의 연구 주제에 대해 불확실성 분석을 할 수 있게 한다는 점에서 의미를 가질 수 있다.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.121-131
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• 2009

In recent years, microtremor array observations have been used for estimation of shear-wave velocity structures. One of the methods is the conventional spatial autocorrelation (SPAC) method, which requires simultaneous recording at least with three or four sensors. Modified SPAC methods such as 2sSPAC, and linear array methods, allow estimating shear-wave structures by using only two sensors, but suffer from instability of the spatial autocorrelation coefficient for frequency ranges higher than 1.0 Hz. Based on microtremor measurements from four different size triangular arrays and four same-size triangular and linear arrays, we have demonstrated the stability of SPAC coefficient for the frequency range from 2 to 4 or 5 Hz. The phase velocities, obtained by fitting the SPAC coefficients to the Bessel function, are also consistent up to the frequency 5 Hz. All data were processed by the SPAC method, with the exception of the spatial averaging for the linear array cases. The arrays were deployed sequentially at different times, near a site having existing Parallel Seismic (PS) borehole logging data. We also used the imaginary part of the SPAC coefficients as a data-quality indicator. Based on perturbations of the autocorrelation spectrum (and in some cases on visual examination of the record waveforms) we divided data into so-called 'reliable' and 'unreliable' categories. We then calculated the imaginary part of the SPAC spectrum for 'reliable', 'unreliable', and complete (i.e. 'reliable' and 'unreliable' datasets combined) datasets for each array, and compared the results. In the case of insufficient azimuthal distribution of the stations (the linear array) the imaginary curve shows some instability and can therefore be regarded as an indicator of insufficient spatial averaging. However, in the case of low coherency of the wavefield the imaginary curve does not show any significant instability.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.610-622
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• 2010

The proportion of overweight and obese individuals in the United States has been continuously increasing up to recently. Many studies related to obesity have concentrated on jurisdictional levels of aggregation, making it very difficult to dearly illustrate at risk regions. In other words, little research has been conducted in relation to spatial patterns considering spatial dependency and heterogeneity by spatial autocorrelation models over space. In response, this research analyzes spatial patterns between overweight/obesity and risk factors, such as high blood pressure and diabetes, over space. Specifically, the Moran''s I and Geary''s C will be conducted for global and local measures. What is more, the Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression methods will be applied to identify spatial dependency and spatial heterogeneity. Data provided by the Behavioral Risk Factor Surveillance System (BRFSS) have Body-Mass Index (BMI) rates, containing 4 rates of under, healthy, overweight, and obesity. In addition, high blood pressure and diabetes rates in the United States will be used as independent variables. Lastly, we are confident that this research will be beneficial for a decision maker to make a prevention plan for obesity.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.77-84
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• 2015

The key objective of this research is to review the effectiveness of spatial regression to identify the influencing factors of spatial distribution patterns of floating population. To this end, global and local spatial autocorrelation test were performed using seoul floating population survey(2014) data. The result of Moran's I and Getis-Ord $Gi^*$ as used in the analysis derived spatial heterogeneity and spatial similarities of floating population patterns in a statistically significant range. Accordingly, Geographically Weighted Regression was applied to identify the relationship between land use attributes and population floating. Urbanization area, green tract of land of micro land cover data were aggregated in to $400m{\times}400m$ grid boundary of Seoul. Additionally public transportation variables such as intersection density transit accessibility, road density and pedestrian passage density were adopted as transit environmental factors. As a result, the GWR model derived more improved results than Ordinary Least Square(OLS) regression model. Furthermore, the spatial variation of applied local effect of independent variables for the floating population distributions.