• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.6-11
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• 2015

In this paper, we analyzed current states and spatial characteristics of crime occurring in A city of Colombia using big data of crime. The analysis draws on the crime statistics of Colombia National Police Agency from 2013 January to September. We also investigated spatial autocorrelation of crime using global and local Moran's Index. Spatial autocorrelation analysis shows significant spatial autocorrelation in the high frequency of crime. Global Moran's I analysis indicates that there are statistically significant value of crime area. Using local Moran's Index analysis, we also implement Local Indicators of Spatial Association(LISA) map and hot spot analysis helps us identify crime distribution.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.93-103
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• 2018

The purpose of this study is to analyze the spatial cluster characteristics of fire using long-term fire data. For this, fire data which were broke out in the last 40 years were converted into GIS data and spatial analysis was performed at Gangwon-do province's minimum administrative district level. In order to grasp the spatial distribution of the fire, Moran's I, Geary's Ci and Getis-Ord's Gi*, which are methods that analyze the local indicators of spatial association(LISA), were used. By integrating the characteristics of the spatial distribution of fire by integrating the results obtained from each analysis, the advantages of the individual analysis methods were reflected in the study results. As a result of the study, hotspot areas of fire in Gangwon-do was derived out. Among the hot spot areas, some areas, where the fire frequency is higher than the adjacent areas, have been identified. The results of this study can be used as information for predicting the fire hazard area and relocating of fire-fighting facilities in the study area.

• Journal of Korean Society of Forest Science
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• v.105 no.3
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• pp.336-341
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• 2016

The object of this study was to classify the local types in relation to regional differences using Height-DBH growth of Pinus densiflora in Korea. The regional types were clustered according to Getis-Ord's $G_i$ among Local indicators of spatial association (LISA) by characteristics of spatial distribution which were calculated the residual of sample plots by fitting Height-DBH growth model using Weibull growth equation. Accordingly, Pinus densiflora were classified 3 groups, It indicated that annual precipitation had one of the biggest impacts among the considered site and climate factors. This results can become the standard for regional management of Pinus densiflora forests.

• Journal of the Korean Geographical Society
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• v.45 no.4
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• pp.502-520
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• 2010

The main objective of the paper is to develop a GIS-based method for delineating spatial clusters. Major tasks are: (i) to devise a sustainable algorithm with reference to various methods developed in the fields of geographic boundary analysis and cluster detection; (ii) to develop a GIS-based program to implement the algorithm. The main results are as follows. First, it is recognized that the AMOEBA technique utilizing LISA is the best candidate. Second, a modified version of the AMOEBA technique is proposed and implemented in a GIS environment. Third, the validity and usefulness of the modified AMOEBA algorithm is assured by its applications to test and real data sets.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 2021

Risk factors that threaten public safety such as crime, fire, and traffic accidents have spatial characteristics. Since each region has different dangerous environments, it is necessary to analyze the spatial pattern of risk factors for each sector such as traffic accident, fire, crime, and living safety. The purpose of this study is to analyze the spatial distribution pattern of local safety level index, which act as an index that rates the safety level of each sector (traffic accident, fire, crime, living safety, suicide, and infectious disease) for basic local governments across the nation. The following analysis tools were used to analyze the spatial autocorrelation of local safety level index : Global Moran's I, Local Moran's I, and Getis-Ord's G⁎i. The result of the analysis shows that the distribution of safety level on traffic accidents, fire, and suicide tends to be more clustered spatially compared to the safety level on crime, living safety, and infectious disease. As a result of analyzing significant spatial correlations between different regions, it was found that the Seoul metropolitan areas are relatively safe compared to other cities based on the integrated index of local safety. In addition, hot spot analysis using statistical values from Getis-Ord's G⁎i derived three hot spots(Samchuck, Cheongsong-gun, and Gimje) in which safety-vulnerable areas are clustered and 15 cold spots which are clusters of areas with high safety levels. These research findings can be used as basic data when the government is making policies to improve the safety level by identifying the spatial distribution and the spatial pattern in areas with vulnerable safety levels.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.590-609
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• 2010

The purpose of the study is to propose and evaluate Exploratory Spatial Data Analysis(ESDA) methods for examining age-specific population migration characteristics. First, population migration pyramid which is a pyramid-shaped graph designed with in-migration, out-migration, and net migration by age (or age group), was developed as a tool exploring age-specific migration propensities and structures. Second, various spatial statistics techniques based on local indicators of spatial association(LISA) such as Local Moran''s $I_i$, Getis-Ord ${G_i}^*$, and AMOEBA were suggested as ways to detect spatial dusters of age-specific net migration rate. These ESDA techniques were applied to age-specific population migration of Daegu Metropolitan City. Application results demonstrated that suggested ESDA methods can effectively detect new information and patterns such as contribution of age-specific migration propensities to population changes in a given region, relationship among different age groups, hot and cold spot of age-specific net migration rate, and similarity between age-specific spatial clusters.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.