• Title/Summary/Keyword: spatial autocorrelation index

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Hotspot Analysis of Korean Twitter Sentiments (한국어 트위터 감정의 핫스팟 분석)

  • Lim, Joasang;Kim, Jinman
    • Journal of Korea Multimedia Society
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    • v.18 no.2
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    • pp.233-243
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    • 2015
  • A hotspot is a spatial pattern that properties or events of spaces are densely revealed in a particular area. Whereas location information is easily captured with increasing use of mobile devices, so is not our emotion unless asking directly through a survey. Tweet provides a good way of analyzing such spatial sentiment, but relevant research is hard to find. Therefore, we analyzed hotspots of emotion in the twitter using spatial autocorrelation. 10,142 tweets and related GPS data were extracted. Sentiment of tweets was classified into good or bad with a support vector machine algorithm. We used Moran's I and Getis-Ord $G_i^*$ for global and local spatial autocorrelation. Some hotspots were found significant and drawn on Seoul metropolitan area map. These results were found very similar to an earlier conducted official survey of happiness index.

Identifying Spatial Distribution Pattern of Water Quality in Masan Bay Using Spatial Autocorrelation Index and Pearson's r (공간자기상관 지수와 Pearson 상관계수를 이용한 마산만 수질의 공간분포 패턴 규명)

  • Choi, Hyun-Woo;Park, Jae-Moon;Kim, Hyun-Wook;Kim, Young-Ok
    • 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.

A Spatial Statistical Approach on the Correlation between Walkability Index and Urban Spatial Characteristics -Case Study on Two Administrative Districts, Busan- (도시 공간특성과 Walkability Index의 상관성에 관한 공간통계학적 접근 -부산광역시 2개 구를 대상으로-)

  • Choi, Don Jeong;Suh, Yong Cheol
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.32 no.4_1
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    • pp.343-351
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    • 2014
  • The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.

Application of Spatial Autocorrelation for Analysis of Spatial Distribution Characteristics of Birds Observed in Namdaecheon River, Muju-gun, Jeollabuk-do, Korea (무주 남대천에 서식하는 조류의 공간적 분포특성 분석을 위한 공간자기상관 적용 연구)

  • Kang, Jong-Hyun;Kim, Yong-Ki;Yeon, Myung-Hun
    • 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.

A Time-Series Analysis of Landscape Structural Changes using the Spatial Autocorrelation Method - Focusing on Namyangju Area - (공간자기상관분석을 통한 시계열적 경관구조의 변화 분석 - 남양주지역을 대상으로 -)

  • Kim, Heeju;Oh, Kyushik;Lee, Dongkun
    • 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.

Phytosociological Study and Spatial autocorrelation on the Forest Vegetation of Mt. Yeonae at Gijang-gun

  • Choi, Byoung-Ki;Huh, Man Kyu
    • Journal of Environmental Science International
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    • v.22 no.11
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    • pp.1373-1381
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    • 2013
  • Mt. Yeonae is at Gijang-gun in Busan and is surrounded by farming lands on three sides. The search for the species composition and dynamics of local communities were studied at Mt. Yeonae of how spatial similarity decays with geographic distance. The index values of Z$\ddot{u}$rich-Montpellier School's phytosociology at the 12 plots was compared to a distribution of similarly using 20 m quadrates at 12 sites. The specific communities were five including Pinus densiflora - Quercus variabilis community. Six species were significant similarity between neighboring sites by using the spatial autocorrelation coefficient, Moran's I. If Mt. Yeonae was destroyed by an artificial action, some spatial correlated species such as P. densiflora and Q. variabilis will be collapsed because of no maintaining the effective population sizes.

Analysis of Relation Between Criminal Types and Spatial Characteristics in Urban Areas (도심지역의 범죄 종류와 공간적 특성 관계분석)

  • Cha, Gyeong Hyeon;Kim, Kyung Ho;Son, Ki Jun;Kim, Sang Ji;Lee, Dong Chang;Kim, Jin Young
    • 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.

An Analysis on the Spatial Pattern of Local Safety Level Index Using Spatial Autocorrelation - Focused on Basic Local Governments, Korea (공간적 자기상관을 활용한 지역안전지수의 공간패턴 분석 - 기초지방자치단체를 중심으로)

  • Yi, Mi Sook;Yeo, Kwan Hyun
    • 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.

A Comparison of Neighborhood Definition Methods for Spatial Autocorrelation (공간자기상관 산출을 위한 인접성 정의 방법 비교)

  • Park, Jae-Moon;Hwang, Do-Hyun;Yoon, Hong-Joo
    • Journal of Fisheries and Marine Sciences Education
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    • v.23 no.3
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    • pp.477-485
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    • 2011
  • For the identifying of spatial distribution pattern, Moran's Index(I) which has the range of values from -1 to +1 is common method for the spatial autocorrelation measurement. When I is close to 1, all neighboring features have close to the same value, indicating clustered pattern. Conversely, if the spatial pattern is dispersed, I is close to -1. And I closing to 0 means spatially random pattern. However, this index equation is influenced by how defining the neighboring features for target feature. To compare and understand the difference of neighborhood definition methods, fixed distance neighboring method and Gabriel Network method were used for I. In this study, these two methods were applied to two marine environments with water quality data. One is Gwangyang Bay which has complex geometric coastal structure located in South Sea of Korea. Another is Uljin area adjacent to open sea located in east coast of Korea. The distances between water quality observed locations were relatively regular in Gwangyang Bay, however, irregular in Uljin area. And for the fixed distance method popular Arc GIS tool was used, but, for the Gabriel Network, Visual Basic program was developed to produce Gabriel Network and calculate Moran's I and its Z-score automatically. According to this experimental results, different spatial pattern was showed differently for some data with using of neighboring definition methods. Therefore there is need to choose neighboring definition method carefully for spatial pattern analysis.