Master Projects

Data Viz
Author

Pingfan Hu

Published

May 24, 2023

About

This is a collection of my master-level projects. It contains a multi-layer network analysis, and a renewable energy data viz.

Network Science of the Mid-Atlantic Region

This project is a multi-layer network science study on the Mid-Atlantic region. It is a student research project for the FEW’s Lab managed by Prof Caitlin Grady.

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Project Scope

The Mid-Atlantic Region
No. Abbr. Full Name
1 DC District of Columbia
2 DE Delaware
3 MD Maryland
4 NC North Carolina
5 NJ New Jersey
6 NY New York
7 PA Pennsylvania
8 VA Virginia
The Multilayers
No. Layer
1 Airline
2 Marine
3 Rail
4 Road

The latter part of this project focuses on layers of rail and road.

The codes can be modified so that you can expand to other selections of states, or to introduce other sources of shp files or csv files.

Project Overview

1. Spatial Analysis

This section is the starting part of this project. It takes in geographical files from trusted sources and generates the proper maps and csv files out of them.

This section takes in shp files and csv files from these sources:

This section generates:

  • 9 csv files:
    • FAF zone correspondence
    • edges of airlines, marine, rails, and roads
    • nodes of airports, marine, rails and roads
  • 2 maps - the spatial map and the topography map

The Spatial Map:

The Topography Map:

2. Centrality Analysis

This section takes in the 4 edge files and the 4 node files, and generates the 3 types of centralities of each.

The 3 types of centralities are: betweenness centrality, closeness centrality, and degree centrality.

Note that the centralities are all in relative values, which means their values are based on the size of the system network.

The centrality values are all listed in a descending order.

Hint: any centrality csv file can also be used as a collection of nodes in its corresponding layer.

3. Path Calculation

Starting from this section, we only focus on the rail and road layers.

This section takes in edge and node files of rail and roads, and generates path counts.

The path counts are based on FAF zones of all 8 states, so that each OD pair is inter-FAF-zone.

There are 40 rail OD pairs, and 186 road OD pairs.

The csv headers are:

  1. No. - counting number starting from 1
  2. Source - the source node
  3. Source State
  4. Source FAF Zone
  5. Source FAF Zone Description
  6. Target - the target node
  7. Target State
  8. Target FAF Zone
  9. Target FAF Zone Description
  10. Path Count - the count of shortest paths

4. Degree Centrality Dist

This section takes in the degree centralities of rails and roads, and generates the degree centrality distribution plots of them.

The Rail Degree Centrality Distribution plot:

The Road Degree Centrality Distribution plot:

5. Network Attacks

This section generates the deterministic and stochastic attacks on rails and roads.

It takes in these files:

  • edge and node csv files
  • path count files
  • 3 ranked centrality files (betweenness centrality also used as node collections)

The plots contain:

  • Deterministic removal plots of the 3 centralities
  • Stochastic removal with random node selections (surrounded by 95% CI)

The Rail Centrality Attack plot:

The Road Centrality Attack plot:

Solar and Wind Energy Advancements

This project studies the driving effects of solar and wind energy advancements. It is a Master-level project for EMSE 6572 instructed by Prof John Helveston.

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Project Overview

This project is performed as a team consisting of Pingfan Hu and Abbey Kollar.

Renewable energy in the USA has seen significant growth from 2000 to 2023. This research studies solar and wind energies and dives into three primary reasons behind this expansion:

  1. Cost of Solar and Wind Energy
  2. Policies and Incentives, and
  3. Energy Research Budgets

This project contains the following:

  1. Project Proposal
  2. Progress Report
  3. Final Report
  4. Presentation

The study aims to highlight which of these factors played the most crucial roles in this energy transformation. By understanding these relationships, we can better anticipate the future of renewable energy and make informed decisions.

Data wrangling and analysis are both performed using R coding language, presented with Quarto files.

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