• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.63-70
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• 2016

A housing market in an urban area is not just a single market but a combination of regionally different submarkets. This study begins with a critical mind that previous researches did not consider the spatial autocorrelation of each area where the housings are located. The clustering analysis of housing submarket which considers spatial autocorrelation is performed as it follows. First, 4 housing market attribute variables are reducted to 1 variable by principle component analysis. Then, after calculating $Gi^*max$ by AMOEBA, 7 housing submarkets which have similar characteristics based on $Gi^*max$ are classified. The characteristics of each submarket are investigated, then political implication is deduced as the following. Different level of housing policy should be made to each cluster because each cluster has different level of spatial autocorrelation.

• Journal of Environmental Impact Assessment
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• v.22 no.5
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• pp.467-479
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• 2013

This study was conducted to find out characterization of spatial distribution of birds observed in river areas. Our bird survey was carried out 4 times at 31 sites from January to September in 2011. A total of 1,609 accumulated individuals belonging to 59 species, 28 families and 11 orders were observed. In the result of spatial autocorrelation analysis using the richness index of the maximum counts of each sites, we confirmed that the distribution of birds in Namdaecheon river was clustered and the tendency of spatial autocorrelation was apparent. The area of each sites within a 200m radius was classified in four biotope categories such as agricultural land, forest, residential area and water area, and the spatial autocorrelation was analysed about four types. In the result of spatial autocorrelation analysis for four biotope categories, all types were showed the positive spatial autocorrelation, but the type of water area was higher than other types. The positive correlation was found between the water area and water birds in statistical significance. However, the forest birds had non-significance values. Therefore, it is appropriate to focus on water birds except for forest birds, when researches of bird distribution in river ecosystem is conducted. The number of bird species and individuals increased as the riverside of water area was to widen. Thus, if the areas of riverside offering the feeding and roosting area increase, it will be accommodated many birds. Also, the areas of riverside should be maintained naturally because it is an important habitats of birds. Our study area is on the outskirts the city of higher rates of forest and agricultural land, it may be unreasonable to apply our results to the whole rivers. If the research about the river flowing around the city will be conducted, it is expected to be useful to the relation study area such as ecological river's restoration.

• Journal of Korean Society of Rural Planning
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• v.19 no.2
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• pp.107-120
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• 2013

A lot of rural development projects have been planned and implemented for revitalizing rural areas in South Korea. However, it is not easy to properly evaluate and quantitatively analyze project outcomes. For this reason only selected regions have been evaluated for rural projects by government agencies. In this study, we analyzed the purpose and the contents of the Rural Village Development Project (RVDP) and Green Tourism Village Project (GTVP) to find indicators for evaluating results of rural projects using logistic regression analysis. Outputs of this study show that RVDPs increase regional population and GTVPs positively affect the sales of agricultural products. We also estimated the spatial distribution of project effects through spatial autocorrelation analysis and local-spatial autocorrelation analysis. Results show that the Moran's I values for the proportion of farmers with avocational jobs, product sales changes, and population growth in Jeol-La province are positive and the biggest one is population growth. Especially, key areas of agricultural product sales are widely distributed.

• Geomechanics and Engineering
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• v.22 no.4
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• pp.339-348
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• 2020

The uncertain geotechnical properties of frozen soil are important evidence for the design, operation and maintenance of the frozen ground. The complex geological, environmental and physical effects can lead to the spatial variations of the frozen soil, and the uncertain mechanical properties are the key factors for the uncertain analysis of frozen soil engineering. In this study, the elastic modulus, strength and Poisson ratio of warm frozen soil were measured, and the statistical characteristics under different temperature conditions are obtained. The autocorrelation distance (ACD) and autocorrelation function (ACF) of uncertain mechanical properties are estimated by random field (RF) method. The results show that the mean elastic modulus and mean strength decrease with the increase of temperature while the mean Poisson ratio increases with the increase of temperature. The average values of the ACD for the elastic modulus, strength and Poisson ratio are 0.64m, 0.53m and 0.48m, respectively. The standard deviation of the ACD for the elastic modulus, strength and Poisson ratio are 0.03m, 0.07m and 0.03m, respectively. The ACFs of elastic modulus, strength and Poisson ratio decrease with the increase of ratio of local average distance and scale of fluctuation. The ACF of uncertain mechanical properties is different when the temperature is different. This study can improve our understanding of the spatial autocorrelation variations of uncertain geotechnical properties and provide a basis and reference for the uncertain settlement analysis of frozen soil foundation.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1799-1802
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• 2002

This paper proposes an improved method for formant frequencies estimation based on the complex autocorrelation function of the speech signal. Instead of using the incoming signal as an input fur the LPC analysis, the analytic signal of the autocorrelation function of the speech signal is computed and itself used as an input for the LPC analysis. Due to the properties of the analytic signal, which occupies half of the bandwidth of the original signal, the required model order for the LPC analysis is halved. The accuracy of the proposed method in noisy environments is examined on five natural vowels. The effectiveness of the proposed method is shown by the estimated spectral shapes and the estimation errors of the formant frequencies.

• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.71-78
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• 2016

PPG signal measured by pulse oximeter can measure pulse and the oxygen saturation of arterial blood. But the PPG signal is distorted by finger movement or other movement in the body. To detect pulse from the PPG signal with motion artifact, we use band pass filter(BPF), Independent component analysis(ICA) and nonlinear autocorrelation(NAC). BPF is used to remove DC component and high frequency noise in the PPG signal with motion artifacts. ICA is used to separate pulse signal and motion artifact. However, pulse signal separated by ICA have no choice but to accompany signal distortion because pulse signal and motion artifact are not completely independent. So, we use nonlinear autocorrelation to emphasize the pure pulse signal from the distorted signal.

• The Korean Journal of Ecology
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• v.22 no.6
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• pp.349-354
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• 1999

The spatial distributions of allelic frequencies and ecological traits by randomization were studied in the natural population of two species (Pinus rigida and Pyrola japonica). Both species showed significant positive spatial autocorrelation as measured by Moran's I. In P. rigida, the genetic similarity was shown in individuals within up to a scale of 18 m distance and this is partly due to combination of pollen and seed dispersal by wind or men. In P. japonica, significant spatial autocorrelation was consisted of a scale of 8 m intervals. These population structure in the distribution of allelic frequencies is related to mating systems such as outcrossing and vegetative spread. The results also indicate that positive species associations between P. rigida and P. japonica can occur when both species select the same habitat or require the same environmental conditions.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.455-458
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• 2004

This study uses time series analyses to evaluate fluctuation of water levels in a geothermal water well due to pumping, in relation to rainfall at Dongrae hot-spring site on the southeastern coast of tile Korean peninsula. The volume of water pumped from the public study wells ranges from 542 to 993 m$^3$/month, and the minimum water level ranged from 35 to 144.7 m during the measured period. Autocorrelation analysis was conducted for the withdrawal rate at the public wells, water levels and rainfall. The autocorrelation of the withdrawal rate shows distinct periodicity with 3 months of lag time, the autocorrelation of rainfall shows weak linearity and short memory with 1 months of lag time, and the autocorrelation of water levels shows weak linearity and short memory with 2 months of lag time. The cross-correlation between the pumping volume and the minimum water level shows a maximum value 1 at a delayed time of 34 months. The cross-correlation between rainfall and the minimum water level shows a maximum value of 0.39 at a delayed time of 32 months.