Big data – Use case
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The document presents a framework for analyzing transportation networks using the North American Transportation Atlas Data (NORTAD), which includes geospatial data for transportation facilities in the U.S., Canada, and Mexico. The methodology involves data collection from various federal sources and analysis using network visualization tools like Gephi. The findings highlight Dallas as a strategic location based on centrality metrics, suggesting improvements for logistics and freight planning.
Big data – Use case
- 1. BIG DATA – USE CASE FRAMEWORK FOR TRANSPORTATION NETWORK ANALYSIS PRESENTED BY – SAURABH CHATTERJEE – MUDIT KHANPURI – HIMANSHU BIYALA
- 2. AGENDA - Introduction Data Collection Methodology Findings Conclusion References 10/15/2017 2
- 3. Introduction - 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. These data sets include geospatial information for transportation modal networks and intermodal terminals, and related attribute information. 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. 10/15/2017 3
- 4. Relevant Usage - It is a set of geographic data sets for transportation facilities in Canada, Mexico, and the United States, which includes geospatial information for transportation modal networks & intermodal terminals, and related attribute information. The data supports research, analysis, & decision making across all modes. The North American Transportation Atlas Data (NORTAD) is a set of transportation-related geospatial data for the North American countries participating in the North American Free Trade Agreement (NAFTA). The BTS (Bureau of Transportation Statistics) uses these data to analyse and develop various transportation preferences. 10/15/2017 4
- 5. Data Collection - The NORTAD is a cooperative effort throughout the US Department of Transportation and other Federal agencies. The major sources of data supplies are - The Federal Highway Administration (FHWA) The Federal Railroad Administration (FRA) The US Army Corps of Engineers (USACE) The US Bureau of the Census The National Park Service (NPS) The Federal Aviation Administration (FAA) The Federal Transit Administration (FTA) The Bureau of Economic Analysis (BEA) 10/15/2017 5
- 6. Methodology 10/15/2017 6 To analyse the results from the dataset, we proceed as: Import Dataset from the relevant sources (Source mentioned in references) Import the dataset into the network visualization tool (in our case GEPHI) Running the different statistics available in the tool.
- 7. 10/15/2017 7
- 8. Findings - Centrality Degree Betweeness Closeness Eigenvector Modularity 10/15/2017 8
- 12. Eigenvector & Modularity - 10/15/2017 12
- 13. Conclusion - Dallas has a high Eigenvector and also has a significant Degree (118). Has a high Modularity & betweeness Centrality. Has a moderate Closeness Centrality. From all the statistics value, we can observe, that Dallas emerges out as the strategic location. BTD (Bureau of Transportation Department) should develop their logistics plan and freight costs by inferring these analysis results. 10/15/2017 13
- 14. References - http://www.ctre.iastate.edu/research/bts_wb/cd-rom/spatial/nortad.htm https://github.com/gephi/gephi/wiki/datasets http://www.bts.gov/publications/north_american_transportation_atlas_data/ 10/15/2017 14