Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach

Guo, Diansheng; Gahegan, Mark; MacEachren, Alan M.; Zhou, Biliang

doi:10.1559/1523040053722150

Cited by 122 publications

(94 citation statements)

References 36 publications

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“…In this plot we can observe the profile of each cluster. In more detail, we can see eleven parallel axes (one for each indicator) scaled by a nested means method (Guo et al, 2005). The profile of each segment (that is to say of each cluster) has to be compared to the central value of each axe that is the value of that indicator computed for the total area.…”

Section: Application and Resultsmentioning

confidence: 99%

“…RedCap is essentially based on a group of six methods of regionalization which are composed by the combination of three agglomerative clustering methods (Single Linkage Clustering, SLK; Average Linkage Clustering, AVG; Complete Linkage Clustering, CLK) and two different spatial constraining strategies: First-Order constraining and Full-Order constraining. We refer to the work of Guo et al (2005); Guo (2008) for technical and computational details about these six methods of regionalization. Considering the aim of this paper, we select as input variables some statistical indicators referring to demographic and migratory dimensions of the foreign population for each province.…”

Section: Methodsmentioning

confidence: 99%

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New Technologies and Statistics: Partners for Environmental Monitoring and City Sensing

Borga

Camporese

Iandelli

et al. 2014

Studies in Theoretical and Applied Statistics

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The agricultural investigation of the italian Parliament, published in 1884, also highlighted, especially in reports on individual territorial districts, the importance of chestnut in the italian mountains. Since the early twentieth century are available long time series collected by ISTAT (National Statistics Institute) data on surface and production of the chestnut and foreign trade of the chestnuts. Key words:Role of the chestnut, statistics, multifunctionality, agricultural policy Official statistics and role of the chestnutThe chestnut (Castanea sativa Miller) is the breadfruit and the tree of life that precedes and then accompanies the first man in history. Since Roman times, and even more in the Middle Ages and in Modern times, Italy is the european territory in which the chestnut spreads further contributing increasingly to the survival of the mountaineer.In the second half of the nineteenth century, the widespread presence of chestnut still identifies two particular social and economic situations: "The international poverty and the chestnut" portrays the role of the chestnuts in human nutrition in large parts of the Alps and the Apennines and "The civilization of the chestnut" photographs with a still of the customs, traditions, use of timber, fruit and use rules that govern the lives of many mountaineers from Piemonte to Veneto, from Lombardia to Calabria [1].The agricultural investigation of the italian Parliament, published in 1884-1886, also highlighted, especially in reports on individual territorial districts, the importance of chestnut in the italian mountains [2].Since the early twentieth century are available long time series collected by Ministry of Finance [3] and ISTAT (National Statistics Institute) [4, 5, 6] data on surface and production of the chestnut and foreign trade of the chestnuts.The examination of the many available statistical data to divide the evolution of italian chestnut by the unit to date in four historical periods.Adua Mario, Istituto Nazionale di Statistica (ISTAT), email: adua@istat.it 1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 Years The first period: "the autumn of the patriarch"The first period, from 1901 to 1950, "the autumn of the patriarch", is characterized by a general decrease in cultivation, however, maintains basically stable overall role of the italian mountain agro-forestry. In the early twentieth century, based on the conclusion of the Agricultural cadastre, it began the publishing of annual data of forestry; for 1910 it was estimated a total area in high forest of chestnut of 652 thousand hectares and a production of 607 thousand tons (Fig. 1). In 1911 it was observed the historical maximun of production: 830 thousand tons. In the thirties ISTAT (then Central Statistics Institute) [7] realizes the survey on the cultivation of chestnut in Italy -Years 1934. This survey is the picture sharper and more detailed on the role of the chestnut tree in the agricultural landscape and chestnuts in the diet. The survey shows a total area o...

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Section: Application and Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

New Technologies and Statistics: Partners for Environmental Monitoring and City Sensing

Borga

Camporese

Iandelli

et al. 2014

Studies in Theoretical and Applied Statistics

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show abstract

“…We used a series of clustering exercises to explore co-ethnic MNE number thresholds, and found natural breaks in communities between 10 and 35. We then tested our ranges with geo-visualization sensitivity analyses using confidence-interval mapping (Guo, Gahegan, MacEachren, & Zhou, 2005). We found that not all co-ethnic investments near agglomerations could be included in the co-ethnic cores.…”

Section: Defining Core and Periphery With Geo-visualizationmentioning

confidence: 99%

Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs

et al. 2018

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We show how the initial subnational entry location of foreign multinational enterprises (MNEs) in China influences their subsequent within-country location choices and expansion speed. We distinguish between MNEs that establish their first subsidiary in co-ethnic cores -dense agglomerations of other firms from the same country of origin -and MNEs that locate their first subsidiary in the periphery, i.e., outside of these co-ethnic cores. To identify co-ethnic cores in China, we employ a geo-visualization methodology, which draws the boundaries of cores organically and dynamically over time. We contrast our findings with the prevailing approach of using static administrative boundaries for identifying agglomerations. Our results provide evidence of path dependency, in that (a) entry through subnational locations with strong co-ethnic communities is followed by expansion into other locations where co-ethnic communities are present, and that (b) entry through co-ethnic communities accelerates the pace at which MNEs establish additional subsidiaries in China. We also find that co-ethnic community effects continue to influence within-country MNE activities over time, despite a host of economic, institutional, and investment developments.

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“…PCP transformed the points from high dimensional space into 2D space in the form Scatter-PCA and PCP had been presented in this study for visualizing multidimensional data including spatio-temporal clusters, without considering spatial components. However, in the spatio-temporal clusters obtained by using the proposed algorithm, they contain spatial information that are necessary to be considered for detecting geographic patterns [20].…”

Section: Finding the Reduced Projected Datamentioning

confidence: 99%

Mining Spatio-Temporal Data of Fatal Accident

Musdholifah¹,

Hadhim²

2013

IJCA

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Traffic accidents are an important concern of today's governments and societies, due to the high cost of human and economical resources involved. Data mining has been proven able to significantly help in improving traffic safety. Among several data mining tasks, clustering technique is mostly applied on spatio-temporal data, especially for the traffic data. A number of traffic related works proposed different clustering techniques for mining the spatio-temporal of traffic accident. However, some difficulties appeared when analyzing these datasets, such as the size of data, the lack of statistical evaluation methods, and interpreting the valuable patterns. With regard to solving this problem, this paper proposes a clustering approach for mining spatio-temporal data of fatal accident using local triangular kernel clustering (LTKC) algorithm. LTKC is kernel-density-based clustering algorithm that has the ability to determine the number of clusters automatically. We also propose three visualization techniques for use to interpret and present the optimal clustering result in an easy-understanding form. From the experimental results, LTKC approach was found to be able to discover responsible clusters within fatal accident data, which had proven by silhouette and Dunn index values close to 1. In addition, using visual techniques, we can state that the clustering results were well-separated and compact clusters.

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Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach

Cited by 122 publications

References 36 publications

New Technologies and Statistics: Partners for Environmental Monitoring and City Sensing

New Technologies and Statistics: Partners for Environmental Monitoring and City Sensing

Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs

Mining Spatio-Temporal Data of Fatal Accident

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