• Journal of Ecology and Environment
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• v.37 no.2
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• pp.53-60
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• 2014

The arctic tundra is an important ecosystem in terms of the organic carbon cycle and climate change, and therefore, detailed analysis of vegetation distribution patterns is required to determine their association. We used grid-sampling method and applied geostatistics to analyze spatial variability and patterns of vegetation within a two-dimensional space, and calculated the Moran's I statistics and semivariance to assess the spatial autocorrelation of vegetation. Spatially autocorrelated vegetation consisted of moss, Eriophorum vaginatum, Betula nana, and Rubus chamaemorus. Interpolation maps and cross-correlograms revealed spatial specificity of Carex aquatilis and a strong negative spatial correlation between E. vaginatum and C. aquatilis. These results suggest differences between the species in water requirements for survival in the arctic tundra. Geostatistical methods could offer valuable information for identifying the vegetation spatial distribution.

• Journal of Ecology and Environment
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• v.41 no.8
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• pp.223-234
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• 2017

Background: Spatial structure of plants in a population reflects complex interactions of ecological and evolutionary processes. For dioecious plants, differences in reproduction cost between sexes and sizes might affect their spatial distribution. Abiotic heterogeneity may also affect adaptation activities, and result in a unique spatial structure of the population. Thus, we examined sex- and size-related spatial distributions of old-growth forest of dioecious tree Torreya nucifera in extremely heterogeneous Gotjawal terrain of Jeju Island, South Korea. Methods: We generated a database of location, sex, and size (DBH) of T. nucifera trees for each quadrat ($160{\times}300m$) in each of the three sites previously defined (quadrat A, B, C in Site I, II, and III, respectively). T. nucifera trees were categorized into eight groups based on sex (males vs. females), size (small vs. large trees), and sex by size (small vs. large males, and small vs. large females) for spatial point pattern analysis. Univariate and bivariate spatial analyses were conducted. Results: Univariate spatial analysis showed that spatial patterns of T. nucifera trees differed among the three quadrats. In quadrat A, individual trees showed random distribution at all scales regardless of sex and size groups. When assessing univariate patterns for sex by size groups in quadrat B, small males and small females were distributed randomly at all scales whereas large males and large females were clumped. All groups in quadrat C were clustered at short distances but the pattern changed as distance was increased. Bivariate spatial analyses testing the association between sex and size groups showed that spatial segregation occurred only in quadrat C. Males and females were spatially independent at all scales. However, after controlling for size, males and females were spatially separated. Conclusions: Diverse spatial patterns of T. nucifera trees across the three sites within the Torreya Forest imply that adaptive explanations are not sufficient for understanding spatial structure in this old-growth forest. If so, the role of Gotjawal terrain in terms of creating extremely diverse microhabitats and subsequently stochastic processes of survival and mortality of trees, both of which ultimately determine spatial patterns, needs to be further examined.

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.299-311
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• 2011

The objective of this paper is to present a methodology for the classification of spatial patterns considering the parameters of urban form which play a significant role in the formation of the urban climate. The urban morphological parameters, i.e. building coverage, impervious pavement, vegetation, water, farmland and landuse types were used to classify the spatial patterns by a K-means cluster analysis. And the presented methodology was applied on Changwon city, South Korea. According to the results of cluster analysis, the total spatial patterns were classified as 24 patterns. First of all, The spatial patterns(A-1, A-2, A-3, B-1, B-2, B-3, C-1, C-2, C-3, D-1, D-2, D-3, E-1, E-2, E-3, F-1, F-2, F-3, G-1, G-2, G-3), which distributed in the rural area and the suburban area, can have the positive impacts of cold air generation and wind corridor on an urban climate environment, were distributed in the rural area. On the other hand, the spatial patterns of the downtown area including A-4, B-4, C-4 and D-4 are expected to have the negative impacts on urban climate owing to the of artificial heat emission or the wind flow obstruction. Finally, it will require the future research to analysis the climatic properties according to the same spatial patterns by the field survey.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.987-990
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• 1998

An optical adder for a modified signed-digit(MSD) number system using joint spatial encoding method is proposed. In order to minimize the numbers of symbolic substitution rules, nine input patterns were divided into five groups of the same addition results. For recognizing the input reference patterns, masks and reference patterns without any other spatial operations are used. This adder is implemented by smaller system in size than a conventional adder.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.104-116
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• 2008

Overheated speculation areas which have high potential of becoming speculative are the target of many real estate policies. This paper proposes a model for spatial patterns of house price volatility and suggests a spatial pattern of overheated speculation areas. House prices are determined by economic behaviors of sellers and buyers who have rational or adaptive expectations. Spatial patterns of house price volatility are formed by tendencies of their economic behavior. If there is a majority of adaptive sellers and buyers in an area, it may appear as a "hotspot" by showing high volatility of house prices and simultaneous price increases. Overheated speculation areas are formed by adaptive sellers and buyers who want to realize maximum expectation profit, therefore these areas patterns are defined as hotspot patterns of price volatility.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.632-647
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• 2007

Lots of geographic phenomena have dynamic spatial patterns with time changes, and there have been lots of researches on exploring these dynamic spatial patterns. However, most of these researches focused on the static pattern analysis in a given period, rather than dealing with dynamic changes in the spatial pattern over time with the continual or cumulative perspective. For this reason, investigation of the inertia of spatial process in terms of temporal changes is needed. From this background, the purpose of this paper is to propose the methodology to explore the changes in spatial pattern cumulatively by considering the inertia of the spatial statistics over time, and to apply it to the case study That is, we introduce the new spatial statistic, and produce the z-values of the statistic using Monte Carlo Simulation, and then to explore the changes in spatial patterns over time cumulatively. To do this, the method to combine the J statistic with CUSUM statistic for exploring spatial patterns, and to apply it to the changes in the commercial landuse in Erie County, New York State. Through the proposed method for spatio-temporal Patterns, we could explore continual changes effectively in the spatial patterns reflecting the statistics by temporal spot cumulatively.

• Spatial Information Research
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• v.19 no.1
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• pp.61-72
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• 2011

Greenhouses gas emission due to usage of fossil fuel has been known as one of the main causes of global warming. Fundamentally, greenhouse gas is a by-product of economic activity. Since majority of economic activity happens in an urban setting, a countermeasure in an urban setting is needed. Therefore, an analysis of relationship between carbon dioxide emission and urban form will be investigated for urban planning and management in the future. The purpose of this study is to analyze the relationship between carbon dioxide emission and urban spatial patterns, and suggesting an urban form with low carbon dioxide emission. In order to achieve this, first theoretical analysis was carried out on urban spatial patterns related to physical size, usage rate, and activity level. Secondly, Seoul's dam on electricity, natural gas, local heating, petroleum, and water usage and mapping a carbon dioxide emission map. Thirdly, relationship between carbon dioxide emission and urban spatial patterns are analyzed and urban spatial patterns that affects energy usage in urban setting was elucidated, and elicited implications on future directions on urban planning based on our analyses above.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.203-219
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• 2020

The primary purpose of this study is to identify spatial distribution patterns of co-authoring activities in Korean cadastral science. The analysis showed that a small number of researchers played an essential role in the Korean cadastral co-authorship network. In particular, some authors not only had a significant influence on other nodes in the network but also served as intermediaries between researchers. Moreover, the distance between researchers influenced co-authorship decisions to a limited extent. This study differs considerably from previous studies in that it used spatial analysis techniques to identify spatial distribution patterns of co-authoring activities. However, this research is limited in that it applied only 2019 data to determine the spatial distribution pattern of co-authoring activities. We can overcome this limitation if we analyze the spatial distribution patterns of co-authoring activities using multi-year data in future studies.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.25-45
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• 2020

The primary purpose of this study is to compare and analyze the performance of local spatial autocorrelation techniques in identifying spatial distribution patterns of green spaces. To achieve the objective, this researcher uses satellite image analysis and spatial autocorrelation techniques. The result of the study shows that the LISA cluster map with the spatial outlier cluster is superior to other analytical methods in identifying the spatial distribution pattern of urban green space. This study can contribute to the related fields in that it uses several different research methods than the existing ones. Despite this differentiation and usefulness, this study has limitations in using low-resolution satellite imagery and NDVI among vegetation indices in identifying spatial distribution patterns of green areas. These limitations may be overcome in future studies by using UAV images or by simultaneously using several vegetation indices.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.625-635
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• 2011

This paper investigates the effects of spatial autocorrelation structures in low resolution data on downscaling without ground measurements or secondary data, as well as the potential of geostatistical downscaling. An advanced geostatistical downscaling scheme applied in this paper consists of two analytical steps: the estimation of the point-support spatial autocorrelation structure by variogram deconvolution and the application of area-to-point kriging. Point kriging of block data without variogram deconvolution is also applied for a comparison purpose. Experiments using two low resolution thematic maps derived from remote sensing data showing very different spatial patterns are carried out to discuss the objectives. From the experiments, it is demonstrated that the advanced geostatistical downscaling scheme can generate the downscaling results that well preserve overall patterns of original low resolution data and also satisfy the coherence property, regardless of spatial patterns in input low resolution data. Point kriging of block data can produce the downscaling result compatible to that by area-to-point kriging when the spatial continuity in block data is strong. If heterogeneous local variations are dominant in input block data, the treatment of the low resolution data as point data cannot generate the reliable downscaling result, and this simplification should not be applied to donwscaling.