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A geovisual analytic approach to understanding geo-social relationships in the international trade network.

Luo W, Yin P, Di Q, Hardisty F, MacEachren AM - PLoS ONE (2014)

Bottom Line: This research aims to address this challenge through the framework of geovisual analytics.In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics.We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

View Article: PubMed Central - PubMed

Affiliation: GeoVISTA Center, Department of Geography, Pennsylvania State University, University Park, Pennsylvania, United States of America.

ABSTRACT
The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

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Related in: MedlinePlus

The second run of the CONCOR process subdivides each of the first two groups.Figure 3A: One subgroup of economically core countries; Figure 3B: The other subgroup of economically core countries; Figure 3C: One subgroup of economic periphery countries; Figure 3D: The other subgroup of economic periphery countries.
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pone-0088666-g003: The second run of the CONCOR process subdivides each of the first two groups.Figure 3A: One subgroup of economically core countries; Figure 3B: The other subgroup of economically core countries; Figure 3C: One subgroup of economic periphery countries; Figure 3D: The other subgroup of economic periphery countries.

Mentions: The second application of CONCOR to the ITN subdivides the first two categories, resulting in a total of four groups as shown by Figure 3 (A list of countries for each group is in File S1.). The core countries and the periphery countries are partitioned into four new geographies, which further indicate a core–periphery arrangement: the mean GDP for each geography is sorted in Table 3. Figure 3A mainly includes more developed countries in the economically core group: North America, most countries in Europe, Australia, South Africa, and economically more-important countries in Asia (i.e., China, India), whereas Figure 3B mainly consists of less developed countries in the economically core groups: Russia, most countries in South America, and a small number of countries in Europe. Figure 3C mainly includes more developed countries in the economic periphery group: Central America, and a few countries from Eurasia (i.e., Vietnam, Iran), whereas Figure 3D mainly consists of the less developed countries in the economic periphery group: countries from Africa and some countries from Asia (e.g., Mongolia). In terms of spatial and social interaction identified by the second level of CONCOR, economically core countries in Figure 3A and Figure 3B (i.e., North America, Europe), as well as more developed periphery countries in Figure 3C exhibit regional patterns (i.e., Central America, Central Asia) that also fall into the same social groups across the globe. It suggests that international trade partners for those countries are related to both spatial proximity and similar economic development level (Figure 3A, 3B, and 3C). Economic periphery countries in Figure 3D have one major cluster (i.e., Africa). Compared to 3A, 3B, and 3C, Figure 3D suggests that spatial proximity has a stronger impact on the least developed countries in terms of international trade partners they have.


A geovisual analytic approach to understanding geo-social relationships in the international trade network.

Luo W, Yin P, Di Q, Hardisty F, MacEachren AM - PLoS ONE (2014)

The second run of the CONCOR process subdivides each of the first two groups.Figure 3A: One subgroup of economically core countries; Figure 3B: The other subgroup of economically core countries; Figure 3C: One subgroup of economic periphery countries; Figure 3D: The other subgroup of economic periphery countries.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928244&req=5

pone-0088666-g003: The second run of the CONCOR process subdivides each of the first two groups.Figure 3A: One subgroup of economically core countries; Figure 3B: The other subgroup of economically core countries; Figure 3C: One subgroup of economic periphery countries; Figure 3D: The other subgroup of economic periphery countries.
Mentions: The second application of CONCOR to the ITN subdivides the first two categories, resulting in a total of four groups as shown by Figure 3 (A list of countries for each group is in File S1.). The core countries and the periphery countries are partitioned into four new geographies, which further indicate a core–periphery arrangement: the mean GDP for each geography is sorted in Table 3. Figure 3A mainly includes more developed countries in the economically core group: North America, most countries in Europe, Australia, South Africa, and economically more-important countries in Asia (i.e., China, India), whereas Figure 3B mainly consists of less developed countries in the economically core groups: Russia, most countries in South America, and a small number of countries in Europe. Figure 3C mainly includes more developed countries in the economic periphery group: Central America, and a few countries from Eurasia (i.e., Vietnam, Iran), whereas Figure 3D mainly consists of the less developed countries in the economic periphery group: countries from Africa and some countries from Asia (e.g., Mongolia). In terms of spatial and social interaction identified by the second level of CONCOR, economically core countries in Figure 3A and Figure 3B (i.e., North America, Europe), as well as more developed periphery countries in Figure 3C exhibit regional patterns (i.e., Central America, Central Asia) that also fall into the same social groups across the globe. It suggests that international trade partners for those countries are related to both spatial proximity and similar economic development level (Figure 3A, 3B, and 3C). Economic periphery countries in Figure 3D have one major cluster (i.e., Africa). Compared to 3A, 3B, and 3C, Figure 3D suggests that spatial proximity has a stronger impact on the least developed countries in terms of international trade partners they have.

Bottom Line: This research aims to address this challenge through the framework of geovisual analytics.In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics.We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

View Article: PubMed Central - PubMed

Affiliation: GeoVISTA Center, Department of Geography, Pennsylvania State University, University Park, Pennsylvania, United States of America.

ABSTRACT
The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

Show MeSH
Related in: MedlinePlus