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Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

Chin WC, Wen TH - PLoS ONE (2015)

Bottom Line: The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network.Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics.This implies that geographic proximity remains a key factor in human mobility.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, National Taiwan University, Taipei, Taiwan.

ABSTRACT
A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

No MeSH data available.


Related in: MedlinePlus

Transformation from transportation system to geospatial network.Spatial distribution of (A) national-scale population centers and urbanization status, (B) junctions of the streets, and (C) the centroid nodes where people gathering together. Data souce: Institute of Transportation, MOTC (Taiwan).
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pone.0139509.g003: Transformation from transportation system to geospatial network.Spatial distribution of (A) national-scale population centers and urbanization status, (B) junctions of the streets, and (C) the centroid nodes where people gathering together. Data souce: Institute of Transportation, MOTC (Taiwan).

Mentions: The national-scale case study area was the Taiwan Island, which has a population of approximately 22.6 million. We transformed the Taiwan Island transportation layers into a geospatial link-node network of national-scale intercity relationships. The network consists of nodes, which are defined as population centers where people reside, and links between nodes are defined as connections between settlements. Links represent the possibility of moving between settlements in one hour of travel time. The national-scale transportation system includes all levels of street and railway networks [32]. We used k-means clustering procedure to group the streets’ junctions (a total of 391,588 junctions) to identify the centroid nodes where people agglomerate. The k-value (number of node) is selected based on the total population of Taiwan. In this study, we chose one node to represent around 50 thousand people. Because Taiwan’s total population is around 22.6 million, the k-value was set to 500 (Fig 3). The travel time between each node through the street and railway networks was then calculated, and a link was established if two nodes were reachable from both directions within one hour [33]. The minimum and maximum distance between two nodes are 2.95 km and 83.26 km, respectively. The intercity network was then used for the calculations of the PR algorthms.


Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network.

Chin WC, Wen TH - PLoS ONE (2015)

Transformation from transportation system to geospatial network.Spatial distribution of (A) national-scale population centers and urbanization status, (B) junctions of the streets, and (C) the centroid nodes where people gathering together. Data souce: Institute of Transportation, MOTC (Taiwan).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4593571&req=5

pone.0139509.g003: Transformation from transportation system to geospatial network.Spatial distribution of (A) national-scale population centers and urbanization status, (B) junctions of the streets, and (C) the centroid nodes where people gathering together. Data souce: Institute of Transportation, MOTC (Taiwan).
Mentions: The national-scale case study area was the Taiwan Island, which has a population of approximately 22.6 million. We transformed the Taiwan Island transportation layers into a geospatial link-node network of national-scale intercity relationships. The network consists of nodes, which are defined as population centers where people reside, and links between nodes are defined as connections between settlements. Links represent the possibility of moving between settlements in one hour of travel time. The national-scale transportation system includes all levels of street and railway networks [32]. We used k-means clustering procedure to group the streets’ junctions (a total of 391,588 junctions) to identify the centroid nodes where people agglomerate. The k-value (number of node) is selected based on the total population of Taiwan. In this study, we chose one node to represent around 50 thousand people. Because Taiwan’s total population is around 22.6 million, the k-value was set to 500 (Fig 3). The travel time between each node through the street and railway networks was then calculated, and a link was established if two nodes were reachable from both directions within one hour [33]. The minimum and maximum distance between two nodes are 2.95 km and 83.26 km, respectively. The intercity network was then used for the calculations of the PR algorthms.

Bottom Line: The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network.Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics.This implies that geographic proximity remains a key factor in human mobility.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, National Taiwan University, Taipei, Taiwan.

ABSTRACT
A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

No MeSH data available.


Related in: MedlinePlus