• International Journal of Contents
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• v.8 no.2
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• pp.89-96
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• 2012

Scientific analysis of crime hot spots is essential in preventing and/or suppressing crime. However, results could be different depending on the analytic methods, which highlights the importance of choosing adequate tools. The purpose of this study was to introduce two advanced techniques for detecting crime hot spots, GAM and Local Moran's I, hoping for more police agencies to adopt better techniques.GAM controls for the number of population in study regions, but local Moran's I does not. That is, GAM detects high crime rate areas, whereas local Moran's I identifies high crime volume areas. For GAM, physical disorder was used as a proxy measure for population at risk based on the logic of the broken windows theory. Different regions were identified as hot spots. Although GAM is generally regarded as a more advanced method in that it controls for population, it's usage is limited to only point data. Local Moran's I is adequate for zonal data, but suffers from the unavoidable MAUP(Modifiable Areal Unit Problem).

• Journal of Korean Society of Forest Science
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• v.101 no.2
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• pp.203-212
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• 2012

This study aims to test presence of spatial autocorrelation of burn severity in Uljin and Youngduk areas burned in 2011. SPOT satellite images were used to compute the NDVI representing burn severity, and NDVI values were sampled for 5,000 randomly dispersed points for each site. Spatial autocorrelations of sampled NDVI values were analyzed with Moran's I and Variogram models. Moran's I values of burn severity in Uljin and Youngduk areas were 0.7745 and 0.7968, respectively, indicating presence of strong spatial autocorrelations. On the basis of Variogram and changes of Moran's I values by lag class, ideal sampling distance were proposed, which were 566-2,151 m for Uljin and 272-402 m for Youngduk. It was recommended to apply these ranges of sampling distance in flexible corresponding to Anisotropic characteristics of burned areas.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.289-292
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• 2007

객체기반 영상분류를 위한 영상분할에 있어서 중요한 요소로는 분할축척(Scale), 분광 정보(Color), 공간 정보(Shape) 등이 있으며 공간 정보에 해당하는 공간 변수는 평활도(Smoothness)와 조밀도(Compactness)가 있다. 이들 가중치의 선택이 최종적으로 객체기반 영상분류의 결과를 좌우하게 된다. 본 연구는 객체기반 영상분류의 준비 과정이라 할 수 있는 영상분할에 있어서 다양한 가중치를 적용을 통하여 영상을 분할하였다. 영상분할을 위해 적용한 가중치는 10, 20, 30의 분할축척(Scale)과 분광 정보(Color)와 공간 정보(Shape)간의 가중치 조합, 공간 변수인 평활도(Smoothness)와 조밀도(Compactness)간의 가중치 조합을 사용하였다. 각 가중치 조합을 통하여 분할된 영상의 분석은 Moran's I 와 객체 내부 분산(Intrasegment Variance)을 이용하여 분석하였다. 각 객체간의 상관관계 분석을 위하여 Moran's I를 계산하였으며 분류된 지역의 동질성을 분석하기 위하여 객체 면적을 고려한 객체 내부 분산(Intrasegment Variance)값을 계산하였다. Moran's I 가 낮은 값을 가질수록 객체 간의 공간상관관계가 낮아지므로 이웃 객체간의 이질성은 높아지며 객체 내부 분산(Intrasegment Variance)이 낮은 값을 가질수록 지역간의 동질성은 높아지게 된다. Moran's I 와 객체 내부 분산(Intrasegment Variance)의 조합을 통하여 객체기반 영상분류 시 가장 높은 분류 정확도가 예상되는 밴드별 영상분할 가중치를 얻을 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.155-166
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• 2008

The quantitative bivariate spatial pattern analysis was applied for the water quality and nutrients data of Masan Bay, and for this analysis Pearson's r as aspatial correlation measurement, Moran's I as spatial association measurement and L index as integration of aspatial and spatial measurement methods were used. To understand the aspatial and spatial characteristics implicated in L index, Pearson's r as well as Moran's I were classified into 3 types respectively, and Pearson's r and Moran's I were combined with 9 types, and also quantile of L index value was used for each of those 9 types. Finally, these types were defined as 5 groups having not overlapped L index range. According to the application result of L index groups, bivariate water quality and nutrients showed no aspatial correlation regardless of spatial association in February and July, but they showed aspatial correlation having clustered spatial pattern in May and November. The result of this study providing the guideline for the interpretation of aspatial correlation and spatial association using L index is expected to be helpful for the marine environment pattern analysis using quantitative index for further study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.29-37
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• 2011

The purpose of this research is to measure Quality of Life indices using Factor Analysis and Principle Component Analysis and to analyze the spatial patterns of Quality of life distribution in the Seoul Metropolitan Area in terms of spatial association using spatial statistics and spatial exploratory technique. In order to check the degree of clustering, this study used spatial autocorrelation indices, global Moran's I index. In addition, local scale analysis was conducted using Moran Scatterplot and Local Moran's I to identify the spatial association pattern and the high Quality of life. The analysis based on global statics showed that, in the Seoul Metropolitan Area, QoL Indices had been distributed with positive spatial association. According to the local spatial statistics, the general tendency of clustering H-H clusters which were mainly concentrated on the Seoul, L-H clusters were concentrated on the Kyunggi-Do and L-L Clusters showed the regional extent of lagging behind. However, in case of H-H, L-H Clusters they had been spread out in the Newtown as population increase.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1153-1166
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• 2020

The purpose of this study was to analyze the spatial correlation between the heat distribution map of the satellite imaging base and the factors that deepen the heat wave, and to explore the heat concentration area and the space where the risk of future heat wave may increase. The global Moran's I of population, land use, and buildings, which are the causes of heat concentration and heat wave deepening, is found to be high and concentrated in specific spaces. According to the analysis results of local Moran's I, heat concentration areas appeared mainly in large cities such as metropolitan and metropolitan areas, and forests were dominant in areas with relatively low temperatures. Areas with high population growth rates were distributed in the surrounding areas of Gyeonggi-do, Daejeon, and Busan, and the use of land and buildings were concentrated in the metropolitan area and large cities. Analysis by Bivarate Local Moran's I has shown that population growth is high in heat-intensive areas, and that artificial and urban building environments and land use take place. The results of this research can lead to the ranking of heat concentration areas and explore areas with environments where heat concentration is concentrated nationwide and deepens it, so ultimately it is considered to contribute to the establishment of preemptive measures to deal with extreme heat.

• Ocean and Polar Research
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• v.29 no.4
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• pp.391-400
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• 2007

To identify the spatial distribution pattern of water quality in Masan Bay, Pearson's correlation as a common statistic method and Moran's I as a spatial autocorrelation statistics were applied to the hydrological data seasonally collected from Masan Bay for two years ($2004{\sim}2005$). Spatial distribution of salinity, DO and silicate among the hydrological parameters clustered strongly while chlorophyll a distribution displayed a weak clustering. When the similarity matrix of Moran's I was compared with correlation matrix of Pearson's r, only the relationships of temperature vs. salinity, temperature vs. silicate and silicate vs. total inorganic nitrogen showed significant correlation and similarity of spatial clustered pattern. Considering Pearson's correlation and the spatial autocorrelation results, water quality distribution patterns of Masan Bay were conceptually simplified into four types. Based on the simplified types, Moran's I and Pearson's r were compared respectively with spatial distribution maps on salinity and silicate with a strong clustered pattern, and with chlorophyll a having no clustered pattern. According to these test results, spatial distribution of the water quality in Masan Bay could be summed up in four patterns. This summation should be developed as spatial index to be linked with pollutant and ecological indicators for coastal health assessment.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.411-411
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• 2017

유역 내 발생하는 강우의 공간적인 분포는 인접성 및 거리에 따라 달라질 수 있다. 공간자기상관 분석은 공간단위(유역 또는 행정구역)의 변수(강수 등)가 주변지역과 갖는 관계를 통해 얼마나 분산되어 있는지 혹은 군집되어 있는지를 판별하는 기법으로 최근 많은 연구에서 활성화 되고 있다. 본 연구에서는 낙동강유역을 대상으로 1980~2000년까지 20개년의 기상청을 통해 수집한 강우자료와 CMIP5(Coupled Model Intercomparison Project Phase 5)에서 제공하는 기후변화 자료 중 가용할 수 있는 20개 모델의 강우를 수집하였다. 기후변화 자료는 정상성 분위사상법으로 지역오차보정을 실시하고 불확실성을 저감하고자 베이지안 모델 평균기법을 통해 새로운 시계열을 생성하였다. 생성된 시계열의 공간적인 분포를 정량적으로 평가하고자 중권역별 공간자기상관 분석을 수행하였다. 대부분의 연구에서는 GIS를 활용하여 정성적으로 강우의 분포를 나타내고 있지만 본 연구에서는 공간단위의 인접성 또는 거리에 따른 척도를 기반으로 공간자기상관을 탐색할 수 있는 Moran's I와 LISA(Local Indicators of Spatial Association)기법을 적용하였다. Moran's I는 전체 연구지역에 대한 관계를 하나의 값으로 보여주는 전역적인 기법이며, LISA는 상대적으로 넓은 지역을 국지적으로 구분하여 특정지역에 대한 Hot spot 및 Cold spot을 통해 공간자기상관 정도를 나타내는 국지적인 기법이다. 두 기법을 적용하기 위하여 인접성 기반의 공간매트릭스를 산정하고 계절별 관측값과 베이지안 앙상블 강우의 Moran's I 및 LISA 분석을 실시하였다. 관측자료와 베이지안 앙상블 강우의 분석결과가 매우 유사하게 나타남으로써 베이지안 앙상블 강우의 공간적인 분포가 관측강우를 충분히 재현하고 있다고 판단된다.

• Spatial Information Research
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• v.8 no.2
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• pp.243-255
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• 2000

The spatial distribution characteristics and patterns of geographic features in space can be understood through a variety of analysis techniques. The scale is one of most important factors in spatial analysis techniques. This study is aimed at identifying the characteristics of spatial data with a coarser spatial resolution and finding procedures for spatial resolution in operational scale. To achieve these objectives, this study selected LANSAT TM imagery for Sunchon Bay, a coastal wetland for a study site, applied the indices for representing scale characteristics with resolution, and compared those indices. Local variance and fractal dimension developed by previous studies were applied to measure the textual characteristics. In this study, Moran s I was applied to measure spatial pattern change of variance data which were generated from the process of coarser resolution. Drawing upon the Moran s I of variancedata was optimum technique for analysing spatial structure than those of previous studies (local variance and fractal dimension). When the variance data represents maximum Moran´s I at certainly resolution, spatial data reveals maximum change at that resolution. The optimum resolution for spatial data can be explored by applying these results.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.3
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• pp.1-14
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• 2011

In order to determine temporal changes of the urban landscape, interdependence and interaction among geo-spatial objects can be analyzed using GIS analytic methods. In this study, to investigate changes in the landscape structure of the Namyangju area, the size and shape of landscape patches, and the distance between the patches were analyzed with the Spatial Autocorrelation Method. In addition, both global and local spatial autocorrelation analyses were conducted. The results of global Moran's I revealed that both patch size and shape index transformed to a more dispersed pattern over time. Next, the local Moran's I of patch size in all time series determined that almost all patches were of a high-low pattern. Meanwhile, the local Moran's I of the shape index was found to have changed from a high-high pattern to a high-low pattern in time series. Finally, as time passes, the number of hot spot patches about size and shape index had been decreased according to the results of hot spot analysis. These changes appeared around the development projects in the study area. From the results of this study, degradation of landscape patches in Namyangju were ascertained and their specific areas were delineated. Such results can be used as useful data in selecting areas for conservation and for preparing plans and strategies in environmental restoration.