start-ver=1.4
cd-journal=joma
no-vol=15
cd-vols=
no-issue=1
article-no=
start-page=e1579
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20220312
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Echelon analysis and its software for spatial lattice data
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=In this study, we explore the use of echelon analysis and its software named EcheScan for spatial lattice data. EcheScan is developed as a web application via an internet browser in R language and Shiny server for echelon analysis. The technique of echelon is proposed to analyze the topological structure for spatial lattice data. The echelon tree provides a dendrogram representation. Regional features, such as hierarchical spatial data structure and hotspots clusters, are shown in an echelon dendrogram. In addition, we introduce the conception of echelon with the values and neighbors for lattice data. We also explain the use of EcheScan for one- and two-dimensional regular lattice data. Furthermore, coronavirus disease 2019 death data corresponding to 50 US states are illustrated using EcheScan as an example of geospatial lattice data.<br>
<br>
This article is categorized under:<br>
　Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis<br>
　Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification<br>
　Data: Types and Structure > Image and Spatial Data
en-copyright=
kn-copyright=

en-aut-name=KuriharaKoji
en-aut-sei=Kurihara
en-aut-mei=Koji
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=IshiokaFumio
en-aut-sei=Ishioka
en-aut-mei=Fumio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

affil-num=1
en-affil=Graduate School of Environmental and Life Science, Okayama University
kn-affil=

affil-num=2
en-affil=Graduate School of Environmental and Life Science, Okayama University
kn-affil=
en-keyword=echelon analysis
kn-keyword=echelon analysis
en-keyword=hierarchical structure
kn-keyword=hierarchical structure
en-keyword=R language and shiny
kn-keyword=R language and shiny
en-keyword=spatial lattice data
kn-keyword=spatial lattice data
en-keyword=web application
kn-keyword=web application
END

start-ver=1.4
cd-journal=joma
no-vol=
cd-vols=
no-issue=
article-no=
start-page=110
end-page=120
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2014
dt-pub=201406
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Juvenile salmon patch identification and comparison using Echelon analysis
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=
en-copyright=
kn-copyright=

en-aut-name=ODAMakiko
en-aut-sei=ODA
en-aut-mei=Makiko
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kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=KOLJONENSaija
en-aut-sei=KOLJONEN
en-aut-mei=Saija
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kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=ISHIOKAFumio
en-aut-sei=ISHIOKA
en-aut-mei=Fumio
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kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=ALHOPetteri
en-aut-sei=ALHO
en-aut-mei=Petteri
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kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=SUITOHiroshi
en-aut-sei=SUITO
en-aut-mei=Hiroshi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=HUTTULATimo
en-aut-sei=HUTTULA
en-aut-mei=Timo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=KURIHARAKoji
en-aut-sei=KURIHARA
en-aut-mei=Koji
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

affil-num=1
en-affil=
kn-affil=

affil-num=2
en-affil=
kn-affil=

affil-num=3
en-affil=
kn-affil=

affil-num=4
en-affil=
kn-affil=

affil-num=5
en-affil=
kn-affil=

affil-num=6
en-affil=
kn-affil=

affil-num=7
en-affil=
kn-affil=
END

start-ver=1.4
cd-journal=joma
no-vol=17
cd-vols=
no-issue=1
article-no=
start-page=7
end-page=21
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2012
dt-pub=201203
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=A Survey Study of University Students’ Awareness on Social Contribution and Regional Cooperation
kn-title=大学生の社会貢献・地域連携に対する意識
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Recently, competition among universities has a whole new face, and emphasis is given toward interdependence and
interactive evolution between universities and regions. From this social perspective, universities should contribute to
regional development by serving the regional society using results of intellectual work, and it is necessary for
universities to accomplish social contribution and regional cooperation strategically. One of the most important parts in
strategic planning is that students participate in activities for social contribution and regional cooperation. This paper
presents a survey of the university students’ awareness on social contribution and regional cooperation using several
statistical methods and attempts to find factors which affect activities for social contribution and regional cooperation
using logistic regression analysis.
en-copyright=
kn-copyright=

en-aut-name=NaMyungjin
en-aut-sei=Na
en-aut-mei=Myungjin
kn-aut-name=羅明振
kn-aut-sei=羅
kn-aut-mei=明振
aut-affil-num=1
ORCID=

en-aut-name=ArakiMasaru
en-aut-sei=Araki
en-aut-mei=Masaru
kn-aut-name=荒木勝
kn-aut-sei=荒木
kn-aut-mei=勝
aut-affil-num=2
ORCID=

en-aut-name=KuriharaKoji
en-aut-sei=Kurihara
en-aut-mei=Koji
kn-aut-name=栗原考次
kn-aut-sei=栗原
kn-aut-mei=考次
aut-affil-num=3
ORCID=

affil-num=1
en-affil=
kn-affil=岡山大学大学院社会文化科学研究科

affil-num=2
en-affil=
kn-affil=岡山大学

affil-num=3
en-affil=
kn-affil=岡山大学大学院環境学研究科
en-keyword=University students’ awareness
kn-keyword=University students’ awareness
en-keyword=social contribution and regional cooperation
kn-keyword=social contribution and regional cooperation
en-keyword=volunteer activities
kn-keyword=volunteer activities
en-keyword=exploratory factor analysis
kn-keyword=exploratory factor analysis
en-keyword=logistic regression analysis
kn-keyword=logistic regression analysis
END

start-ver=1.4
cd-journal=joma
no-vol=13
cd-vols=
no-issue=1
article-no=
start-page=51
end-page=56
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2008
dt-pub=200803
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Detection of Hotspot for Korea Earthquake Data using Echelon Analysis and Seismic Wave Energy
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Echelon analysis (Myers et al., 1997) is a method to investigate the phase-structure of spatial data systematically and objectively. This method is also useful to prospect the areas of interest in regional monitoring of a surface variable. The spatial scan statistic (Kulldorff, 1997) is a method of detection and inference for the zones of significantly high or low rates based on the likelihood ratio. These zones are called hotspots. The purpose of this paper is to detect the hotspot area for spatial data using echelon. We perform echelon analysis for Korea earthquake data. We use ESRI’s ArcGIS that is geographical
information system (GIS) software to make the meshed areas and get contiguity information of these areas. With this contiguity information on the meshed areas, we detect the hotspot area using echelon analysis and spatial scan statistics. In addition, we compare with the result of analysis based on the total of number of times simply and the seismic wave energy.
en-copyright=
kn-copyright=

en-aut-name=
en-aut-sei=
en-aut-mei=
kn-aut-name=SanghoonHan
kn-aut-sei=Sanghoon
kn-aut-mei=Han
aut-affil-num=1
ORCID=

en-aut-name=IshiokaFumio
en-aut-sei=Ishioka
en-aut-mei=Fumio
kn-aut-name=石岡文生
kn-aut-sei=石岡
kn-aut-mei=文生
aut-affil-num=2
ORCID=

en-aut-name=KuriharaKoji
en-aut-sei=Kurihara
en-aut-mei=Koji
kn-aut-name=栗原考次
kn-aut-sei=栗原
kn-aut-mei=考次
aut-affil-num=3
ORCID=

affil-num=1
en-affil=
kn-affil=岡山大学

affil-num=2
en-affil=
kn-affil=岡山大学

affil-num=3
en-affil=
kn-affil=岡山大学
en-keyword=Hotspot
kn-keyword=Hotspot
en-keyword=Echelon analysis
kn-keyword=Echelon analysis
en-keyword=Spatial scan statistics
kn-keyword=Spatial scan statistics
en-keyword=Seismic Wave Energy
kn-keyword=Seismic Wave Energy
END

start-ver=1.4
cd-journal=joma
no-vol=13
cd-vols=
no-issue=1
article-no=
start-page=35
end-page=42
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2008
dt-pub=200803
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=Survey Study of Resident Awareness on Waste Final Disposal Site
kn-title=最終処分場に関する住民の意識調査
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=As construction of final waste disposal site is essential recently, a problem where we should build it becomes important issue. However, public opposition occurs for the construction because the final waste disposal site has negative image such as pollution of various kinds, increase of traffic volume and noise by truck and bulldozer, and aggravation of living conditions. Public opposition is the most critical problem in constructing final waste disposal site. The source of public opposition has been characterized as NIMBY or not-in-my-yard. This paper presents a survey of the resident awareness on final waste disposal site, and attempts to find factors which affect the public opposition using logistic regression analysis and CART(classification and regression tree).
en-copyright=
kn-copyright=

en-aut-name=NaMyungjin
en-aut-sei=Na
en-aut-mei=Myungjin
kn-aut-name=羅明振
kn-aut-sei=羅
kn-aut-mei=明振
aut-affil-num=1
ORCID=

en-aut-name=OnoYusaku
en-aut-sei=Ono
en-aut-mei=Yusaku
kn-aut-name=小野雄策
kn-aut-sei=小野
kn-aut-mei=雄策
aut-affil-num=2
ORCID=

en-aut-name=OnoYoshiro
en-aut-sei=Ono
en-aut-mei=Yoshiro
kn-aut-name=小野芳朗
kn-aut-sei=小野
kn-aut-mei=芳朗
aut-affil-num=3
ORCID=

en-aut-name=KuriharaKoji
en-aut-sei=Kurihara
en-aut-mei=Koji
kn-aut-name=栗原考次
kn-aut-sei=栗原
kn-aut-mei=考次
aut-affil-num=4
ORCID=

affil-num=1
en-affil=
kn-affil=岡山大学

affil-num=2
en-affil=
kn-affil=埼玉県環境科学国際センター

affil-num=3
en-affil=
kn-affil=岡山大学

affil-num=4
en-affil=
kn-affil=岡山大学
en-keyword=final waste disposal site
kn-keyword=final waste disposal site
en-keyword=resident awareness
kn-keyword=resident awareness
en-keyword=public opposition
kn-keyword=public opposition
en-keyword=logistic regression analysis
kn-keyword=logistic regression analysis
en-keyword=CART(classification and regression tree)
kn-keyword=CART(classification and regression tree)
END