• Journal of the Korean Data and Information Science Society
• /
• 제26권3호
• /
• pp.561-568
• /
• 2015

이 연구에서는 공간회귀모형 중 공간시차모형과 공간오차모형을 이용하여 대구 경북 지역 단위면적당 아파트 매매가격을 예측하였다. k-최근접이웃 (k-nearest neighbours)을 이용하여 공간가중행렬을 구축하였으며, 이를 이용해 2012년 3월의 단위면적당 아파트 매매가격에 대한 모형을 적합시켰다. 적합시킨 공간시차모형, 공간오차모형을 이용하여 2013년 3월의 단위면적당 아파트 매매가격을 예측하였으며 RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error)를 통해 두 모형의 성능을 비교하였다.

• 대한지리학회지
• /
• 제48권6호
• /
• pp.978-993
• /
• 2013

공간회귀모형은 공간가중행렬을 통해 공간관계를 명시적으로 정량화한다는 점에서 타 모형과 뚜렷하게 구별되는 강점이 있는 동시에, 공간가중행렬 구성에 자의성이 개입된다는 약점을 가지고 있기도 하다. 본 연구에서는 공간가중행렬의 구성에 따라 모형 적합도가 어떻게 변화하는지 인천시를 사례로 실증적으로 검토하였다. 또한 인근지역 범위 설정에 따라 공간시차모형(spatial lag model) 또는 공간오차모형(spatial error model) 중 어떠한 모형이 보다 우수하게 나타는지 검토하였다. 분석 결과, 토지가격 추정에 있어 인근지역 범위를 좁게 파악하는 공간가중행렬을 구성할수록 모형 적합도가 전반적으로 개선되는 것이 확인되었다. 또한, 공간적 이질성이 심한 지역은 공간오차모형의 적합도가 보다 우수한 것으로 파악되었다. 공간적 이질성이 심한 지역은 동질적 성격을 갖는 하부 인근지역으로 세분함으로써 그러한 이질성을 완화시킬 수 있었고, 그 결과 공간오차모형보다 공간시차모형의 적합도가 우수하게 나타날 수 있음을 밝혔다.

• Computers and Concrete
• /
• 제10권5호
• /
• pp.457-467
• /
• 2012

Using appropriate raw materials for cement is crucial for providing the required products. Monitoring relationships and analyzing distributions in a cement material quarry are important stages in the process. CaO, one of the substantial chemical components, is included in some raw materials such as limestone and marl; furthermore, appraising spatial assessment of this chemical component is also very critical. In this study, spatial evaluation and monitoring of CaO concentrations in a cement site are considered. For this purpose, two effective regression-based models were applied to a cement quarry located in Turkey. For the assessment, some spatial models were developed and performance comparisons were carried out. The results show that the regression-based spatial modelling is an efficient methodology and it can be employed to evaluate spatially varying relationships in a cement quarry.

• 대한원격탐사학회지
• /
• 제33권4호
• /
• pp.445-454
• /
• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• 대한원격탐사학회지
• /
• 제35권4호
• /
• pp.589-607
• /
• 2019

This paper presents a comprehensive overview of recent model developments and practical issues in spatial downscaling of coarse resolution satellite-derived products. First, theoretical aspects of spatial downscaling models that have been applied when auxiliary variables are available at a finer spatial resolution are outlined and discussed. Based on a thorough literature survey, the spatial downscaling models are classified into two categories, including regression-based and component decomposition-based approaches, and their characteristics and limitations are then discussed. Second, open issues that have not been fully taken into account and future research directions, including quantification of uncertainty, trend component estimation across spatial scales, and an extension to a spatiotemporal downscaling framework, are discussed. If methodological developments pertaining to these issues are done in the near future, spatial downscaling is expected to play an important role in providing rich thematic information at the target spatial resolution.

• Journal of the Korean Data and Information Science Society
• /
• 제27권5호
• /
• pp.1215-1224
• /
• 2016

고해상도 격자 단위 기후정보는 농업, 관광학, 생태학, 질병학 등 다양한 분야의 현상을 설명하는 중요 요인이다. 고해상도 기후정보는 동적 모형과 통계적 모형을 통해 얻을 수 있다. 통계적 모형은 동적 모형에 비해 계산 시간이 저렴하여 시공간 해상도가 높은 기후자료 생성에 주로 이용한다. 본 연구에서는 2003년부터 2012년까지 1월에 관측된 일 평균기온자료를 토대로 통계적 모형의 일 평균 기온을 생성하였다. 통계적 모형으로 선형모형을 기반으로한 일반선형모형, 일반화가법모형, 공간선형모형, 베이지안공간선형모형을 고려하였다. 예측성능평가를 위해 60개소의 지상관측소에서 관측된 일 평균기온을 모형적합 자료로 사용하여 352개소의 자동기상관측의 일 평균기온을 검증하였다. 평균제곱오차와 상관계수를 보면 베이지안공간모형의 예측성능이 다른 모형에 비해 상대적으로 우수하였다. 최종적으로 $1km{\times}1km$ 격자 단위 일 평균기온 지도를 생성하였다.

• 한국공간구조학회논문집
• /
• 제20권4호
• /
• pp.111-121
• /
• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• 한국측량학회지
• /
• 제32권3호
• /
• pp.225-231
• /
• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• 대한원격탐사학회지
• /
• 제33권1호
• /
• pp.25-35
• /
• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
• /
• pp.622-625
• /
• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.