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Transportation policy analytics capstone project

listContents

Policy analysis project framework
Overview screen cast recording
Project tracker
Datasets and research examples
Specification

wb_incandescentProject Framework

policy framework

Sample impact analysis for oversized vehicle policy

Screen cast recording

The following video is a recording of the screen capture taken during the presentation of the alternative capstone project framework.

wb_incandescentProject tracker

wb_incandescentFeatured datasets and resources

bookProject specifications

The following table can be a starting point for project design and should be considered "negotiable" and flexible to address variations in project structure that make such inquiries rich and interesting.

project objective

Explore a novel question related to transportation by processing and analyzing one substantial or multiple smaller datasets in python or R for presentation to an academic audience

spec 1: novel question

Develop a compelling and relevant question whose exploration requires rigorous data analysis. The question should speak to a concern of one or more entities or groups involved in the national or global transportation system.

spec 2: background research

Conduct a thorough literature review of academic research related to your chosen research question. Write a thoughtful summary of the existing literature drawing from academia (peer-reviewed journals), government reports, and agency studies. The review should cite and summarize 6-10 substantial sources related to your topic which together position your question amidst existing work on your question and its larger subject of inquiry.

spec 3: analysis

Use python or R to assemble your data sources into analysis friendly formats, such as a CSV or relational database. Undertake exploratory analysis of each variable of concern, generating a descriptive statistical summary of each field (min, max, median, mode, mean, stdev).

Choose a dominant analytic tool for pursuing your question, such as min/max analysis, linear regression, classification, clustering, or the like. Secure a primary reference resource for your tool of choice and digest it thoroughly. Read studies which apply your tool to a similarly "shaped" dataset.

Undertake iterative analysis, trying variations of your analytic model and comparing the value of the results using your chosen tool's standard for comparison (e.g. r-squared for regression). Document your findings.

spec 4: visualization

Generate compelling visualizations of your analytic tool's output using a spreadsheet, pandas, or tools in R. Write detailed captions to accompany every figure such that a person unable to discern relevant patterns unassisted can glean the heart of your findings by reading only the captions to your figures.

Compose short--but meaningful--written summaries of the following:

  1. Your sourcing and munging (cleaning, re-arranging) of the data
  2. Your analytic method
  3. Your findings
  4. The limitations of your analysis
  5. The relevance of your findings to your target group or agency
  6. A statement of future research possibilities and potentials

spec 5: sharing

Assemble your work into a sharable form using any medium except a slide deck (i.e. "a Powerpoint"). This could be a tableau workbook, an R-Shiny interactive web tool, a website with embedded javascript, or a video presentation. Your chosen tool should be publicly accessible and free of glaring errors or bugs.

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