Analysis US Air97: North American Transportation Atlas Data (NORTAD)

These data sets about geospatial information for transportation modal networks and intermodal terminals, and related attribute information. It is a set of geographic data sets for transportation facilities in Canada, Mexico, and the United States. The data supports research, analysis, and decision making across all modes of transportation. These data sets do not provide explicit connections between modes and terminals.

Visualization

we are analyzing 332 terminals by nodes, and 2126 relationships which are represented by edges. The North American Transportation Atlas Data – 1998 (NORTAD) is a set of geographic data sets for transportation facilities in Canada, Mexico, and the United States.

   The data-set visualized by Gephi

  data sets include geospatial information for transportation modal networks and intermodal terminals, and related attribute information , which includes border crossing (US vs Canada and US vs Mexico), Canada, Mexico, and the United States. The updates to NTAD 1997 are the US part of NORTAD. The NORTAD is a special release for 1998 and it did replace NTAD for 1998. NTAD will return in 1999, with a projected release date of April 1999.

These databases are designed to be used with Geographic Information System (GIS) software packages to locate transportation features and provide a framework for transportation network analysis. The databases are most useful at the national level, but have major applications at the regional, state, and local scale throughout the transportation community.

Measurement

 

The result of the degree distribution is 12,807 so the degree of a node in a network is the number of connections or edges the node has to other nodes or terminals. and this network is directed, meaning that edges point in one direction from one node to another node, then nodes have two different degrees, the in-degree, which is the number of incoming edges, and the out-degree, which is the number of outgoing edges

An iterative algorithm that measures the importance of each node within the network. indicates the importance of each node within the network. The importance of each nodes is what terminal is most visited. The page rank values are the values in the eigenvector that has the highest corresponding eigenvalue of a normalized adjacency matrix A'. The standard adjacency matrix is normalized so that the columns of the matrix sum to 1.

This article is about ordering items with constraints. For centrality in social networks, it show the connectivity each terminals and he more frequent the connections, the better the connections. A frequent connection indicates a good relationship.

 Closeness Centrality

The items listed in these triples should be placed into a total order, with the property that for each of the given triples, the middle item in the triple appears in the output somewhere between the other two items. The items of each triple are not required to be consecutive in the output

Randomizing the algorithm can produce a better decomposition resulting in a higher modularity score, however randomizing will increase computation time.