• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1317-1325
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• 2014

The study focused on analyzing spatial sampling by minimizing autocorrelation of spatial based on spatial and geographical data. The study concluded two different ways of minimizing autocorrelation. First, it was important to use suitable spatial sampling method to alienate spatial autocorrelation from spatial or geographical data. The shear distribution rate of public transportation in Seoul resulted in high rate of autocorrelation. However, the study showed samples eliminated autocorrelation when samples were extracted with reasonable distance(above 400m) apart. Without spatial sampling the distortion of spatial data leads to false results; therefore, spatial sampling is indispensable. Second, factors which fluctuates shear distribution of public transportation spatial sampling changed before and after spatial sampling. This was caused by incapable of controling inherent spatial autocorrelation of the data.

• 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 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.

• 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 the Economic Geographical Society of Korea
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• v.12 no.2
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• pp.179-194
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• 2009

The spatial distribution of crime incidences in urban neighborhoods is a reflection of their socio-economic environment and spatial inter-relations. Spatial interactions between offenders and victims lead to spatial autocorrelation of the crime incidences. The spatial autocorrelation among the incidences biases the interpretation of the ecological model in OLS framework. This research investigates residential crimes using residential burglaries and robberies occurred in the city of Columbus, Ohio, for 2000. In particular, the spatial distribution of incidence rates of residential crimes are accounted in OLS framework using eigenvectors, which reflect spatial dependence in crime patterns. Result presents that handling spatial autocorrelation enhanced model estimation, and both economic deprivation and crime opportunity are turned out significant in estimating residential crime rates.

• 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.

• Journal of Ecology and Environment
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• v.43 no.2
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• pp.191-201
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• 2019

Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

• Journal of Life Science
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• v.18 no.10
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• pp.1325-1330
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• 2008

Salix koriyanagi is a deciduous shrub and native to Korea. The spatial distribution of multilocus allelic frequencies and geographical distances of the natural population in upper swampy area at the Namgang River in Korea were studied. The species showed a significant positive and negative spatial autocorrelation according to geographical distances as measured by Moran's I. Genetic similarity of individuals was found among subpopulations at up to a scale of a 12 m distance, and this was partly due to a combination of allelic frequencies, and therefore, a significant spatial autocorrelation was composed of a scale of 12 m intervals. Within S. koriyanagi in swampy area at the Namgang River, a strong spatial structure was observed for allozyme markers, indicating a migration within subpopulations.

• Journal of Life Science
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• v.18 no.11
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• pp.1465-1470
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• 2008

Salix koriyanagi is a deciduous shrub and native to Korea. The spatial distribution of multilocus allelic frequencies and geographical distances of the natural population in upper swampy area at the Namgang River in Korea were studied. The species showed a significant positive and negative spatial autocorrelation according to geographical distances as measured by Moran's I. Genetic similarity of individuals was found among subpopulations at up to a scale of a 12 m distance, and this was partly due to a combination of allelic frequencies, and therefore, a significant spatial autocorrelation was composed of a scale of 12 m intervals. Within S. koriyanagi in swampy area at the Namgang River, a strong spatial structure was observed for allozyme markers, indicating a migration within subpopulations.

• Spatial Information Research
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• v.10 no.2
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• pp.233-246
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• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.