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50 changes: 45 additions & 5 deletions reading.qmd
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Expand Up @@ -8,21 +8,59 @@ This reading list contains key resources for the Transport Data Science module,
# Core Reading

- [R for Data Science](https://r4ds.had.co.nz/) [@wickham_data_2023]
- This is an excellent and very popular applied introduction to data science with R, covering the Tidyverse and data visualization. It is open access and based on open code, check out the source code at [github.com/hadley/r4ds](https://github.com/hadley/r4ds) for insights into how they use Quarto and embed code in their book.
- This is an excellent and very popular applied introduction to data science with R, covering the Tidyverse and data visualization. It is open access and based on open code. See [github.com/hadley/r4ds](https://github.com/hadley/r4ds) for insights into how Quarto can be used to embed code in written outputs.
- [Geocomputation with R](https://r.geocompx.org/) [@lovelace_geocomputation_2025]
- A comprehensive guide to geographic data analysis, visualization, and modeling using R.
- A guide to geographic data analysis, visualization, and modeling with R.
<!-- https://r.geocompx.org/transport.html -->
- The Transportation chapter, which can be found online at [r.geocompx.org/transport.html](https://r.geocompx.org/transport.html), is a key resource for this module.

# Software and Tools
# Skills Development

There is a wealth of material in physical books and online teaching the skills needed for this course.
The advantage of online materials is that they can be updated more easily, and are often free to access.
Below are some key resources for developing the skills needed for this course.
Search online for topics you are interested in and see the [Quarto gallery of books](https://quarto.org/docs/gallery/#books) and the [bookdown.org](https://bookdown.org/) website for more resources.

## Key Skills

- [Quarto](https://quarto.org/) [@allaire_quarto_2024]
- The software used to create this document, Quarto is a powerful tool for creating reproducible documents with code and data.
- [Quarto](https://quarto.org/) documentation [@allaire_quarto_2024]

<!-- Articles & Reports
Presentations
Dashboards
Websites
Books
Interactive Docs -->

- The software used to create the Transport Data Science course materials and [numerous websites, presentations, dashboards, and books](https://quarto.org/docs/gallery/), Quarto is a powerful tool for creating reproducible documents with code and data.
- See the [technical writing](https://quarto.org/docs/visual-editor/technical.html) page of Quarto's documentation for key information on how to add references, figure captions, and more.
- [Introduction to GitHub](https://github.com/skills/introduction-to-github) [@heis_introduction_2025]
- A good starting point for learning how to use GitHub for version control and collaboration.
<!-- https://docs.github.com/en/codespaces/setting-up-your-project-for-codespaces/adding-a-dev-container-configuration/introduction-to-dev-containers -->
- See also their introduction to Devcontainers at [docs.github.com/en/codespaces/](https://docs.github.com/en/codespaces/)

## Python

- [Course Materials for: Geospatial Data Science](https://github.com/mszell/geospatialdatascience) [@szell_course_2025]
- Course materials covering various aspects of geospatial data science, including data analysis, visualization, and working with street networks using Python.
- [Modern Polars](https://kevinheavey.github.io/modern-polars/) [@heavey_modern_2025]
- A side-by-side comparison of the Polars and Pandas libraries.
- [Python Polars: The definitive guide](https://github.com/jeroenjanssens/python-polars-the-definitive-guide) [@janssens_python_2025]
- Guide to using the polars for data manipulation in Python, due to be published in February 2025.
- [A course on Geographic Data Science](https://darribas.org/gds_course/content/home.html) [@arribas-bel_course_2019]
- Free and open source online book on using GeoPandas and other Python libraries for geographic data analysis.
- [Python for Data Analysis](https://wesmckinney.com/book/) [@mckinney_python_2022]
- Dta wrangling with Pandas, NumPy, and Jupyter, written by the creator of the Pandas library.
- [Geocomputation with Python](https://py.geocompx.org/) [@dorman_geocomputation_2025]
- Resource for working with geographic data using Python, covering both vector and raster data models.

## R

- [Advanced R](https://adv-r.hadley.nz/)
- A comprehensive guide to advanced programming in R, covering topics such as functional programming and object-oriented programming.

# Software and Tools

- [stats19](https://itsleeds.github.io/stats19/) [@lovelace_stats19_2019]
- R package for working with official road crash data
- [stplanr: A Package for Transport Planning](https://doi.org/10.32614/RJ-2018-053) [@lovelace_stplanr_2018]
Expand All @@ -48,6 +86,8 @@ This reading list contains key resources for the Transport Data Science module,

- [The Visual Display of Quantitative Information](https://www.edwardtufte.com/tufte/books_vdqi) [@tufte_visual_2001]
- Classic work on the principles of data visualization
- [Visualization Curriculum](https://idl.uw.edu/visualization-curriculum/intro.html) [@heer_visualization_2021]
- A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair. This book contains a series of Python-based Jupyter notebooks, with a corresponding set of JavaScript notebooks available online on Observable.

## Miscellaneous

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110 changes: 85 additions & 25 deletions tds.bib
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@book{_python_a,
title = {Python {{Polars}}: {{The Definitive Guide}}},
shorttitle = {Python {{Polars}}},
url = {https://learning.oreilly.com/library/view/python-polars-the/9781098156077/},
urldate = {2025-01-22},
abstract = {Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing....},
isbn = {978-1-09-815607-7},
langid = {english}
}

@software{abstreet_2022,
title = {A/{{B Street}}},
shorttitle = {A-b-Street/Abstreet},
Expand All @@ -21,6 +31,22 @@ @software{allaire_quarto_2024
version = {1.6}
}

@article{arribas-bel_course_2019,
title = {A Course on {{Geographic Data Science}}},
author = {Arribas-Bel, Dani},
date = {2019-04-26},
journaltitle = {Journal of Open Source Education},
shortjournal = {JOSE},
volume = {2},
number = {14},
pages = {42},
issn = {2577-3569},
doi = {10.21105/jose.00042},
url = {https://jose.theoj.org/papers/10.21105/jose.00042},
urldate = {2025-01-22},
annotation = {13 citations (Crossref) [2025-01-22]}
}

@article{boeing_osmnx_2017,
title = {{{OSMnx}}: {{New Methods}} for {{Acquiring}}, {{Constructing}}, {{Analyzing}}, and {{Visualizing Complex Street Networks}}},
shorttitle = {{{OSMnx}}},
Expand Down Expand Up @@ -59,6 +85,21 @@ @software{carlino_osm2streets_2025
organization = {A/B Street}
}

@book{dorman_geocomputation_2025,
title = {Geocomputation with {{Python}}},
author = {Dorman, Michael and Graser, Anita and Nowosad, Jakub and Lovelace, Robin},
date = {2025-02-14},
eprint = {Gl03EQAAQBAJ},
eprinttype = {googlebooks},
publisher = {CRC Press},
url = {https://py.geocompx.org/},
abstract = {Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.},
isbn = {978-1-04-030160-9},
langid = {english},
pagetotal = {309},
keywords = {Computers / Mathematical & Statistical Software,Mathematics / Probability & Statistics / General,Psychology / Research & Methodology,Science / Earth Sciences / Geology,Science / Life Sciences / Biological Diversity,Science / Life Sciences / Botany,Social Science / Human Geography}
}

@inproceedings{fernandez_natural_2016,
title = {Natural User Interfaces for Human-Drone Multi-Modal Interaction},
booktitle = {2016 {{International Conference}} on {{Unmanned Aircraft Systems}} ({{ICUAS}})},
Expand Down Expand Up @@ -116,20 +157,6 @@ @article{goodman_scenarios_2019
keywords = {Active travel,Carbon emissions,Cycling,Modelling,nosource,Physical activity,School}
}

@article{goodman_scenarios_2019,
title = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts: {{Application}} of the `{{Propensity}} to {{Cycle Tool}}'},
shorttitle = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts},
author = {Goodman, Anna and Rojas, Ilan Fridman and Woodcock, James and Aldred, Rachel and Berkoff, Nikolai and Morgan, Malcolm and Abbas, Ali and Lovelace, Robin},
date = {2019-03},
journaltitle = {Journal of Transport \& Health},
volume = {12},
pages = {263--278},
issn = {2214-1405},
doi = {10/ghdcfx},
keywords = {\nosource,Active travel,Carbon emissions,Cycling,Modelling,Physical activity,School},
annotation = {25 citations (Crossref) [2025-01-21]}
}

@article{graells-garrido_adoptiondriven_2020,
title = {Adoption-{{Driven Data Science}} for {{Transportation Planning}}: {{Methodology}}, {{Case Study}}, and {{Lessons Learned}}},
shorttitle = {Adoption-{{Driven Data Science}} for {{Transportation Planning}}},
Expand Down Expand Up @@ -167,6 +194,24 @@ @article{gschwender_using_2016
keywords = {\nosource,Automatic fare collection,Automatic vehicle location,nosource,Passive data,Public transport}
}

@book{heavey_modern_,
title = {Modern {{Polars}}},
author = {Heavey, Kevin},
url = {https://kevinheavey.github.io/modern-polars/},
urldate = {2025-01-22},
abstract = {A side-by-side comparison of the Polars and Pandas libraries.},
langid = {english}
}

@book{heer_visualization_2021,
title = {Visualization {{Curriculum}}},
author = {Heer, Jeffrey},
date = {2021},
url = {https://idl.uw.edu/visualization-curriculum/intro.html},
urldate = {2025-01-22},
abstract = {A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair. This book contains a series of Python-based Jupyter notebooks, a corresponding set of JavaScript notebooks are available online on Observable.}
}

@software{heis_introduction_2025,
title = {Introduction to {{GitHub}}},
author = {Heis, Kevin},
Expand Down Expand Up @@ -284,6 +329,21 @@ @article{lovelace_stplanr_2018
keywords = {\nosource,nosource}
}

@book{mckinney_python_2022,
title = {Python for {{Data Analysis}}: {{Data Wrangling}} with Pandas, {{NumPy}}, and {{Jupyter}}},
shorttitle = {Python for {{Data Analysis}}},
author = {McKinney, Wes},
date = {2022-09-20},
edition = {3rd edition},
publisher = {O'Reilly Media},
location = {Beijing Boston Farnham Sebastopol Tokyo},
url = {https://wesmckinney.com/book/},
abstract = {Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples},
isbn = {978-1-09-810403-0},
langid = {english},
pagetotal = {579}
}

@article{morgan_opentripplanner_2019,
title = {{{OpenTripPlanner}} for {{R}}},
author = {Morgan, Malcolm and Young, Marcus and Lovelace, Robin and Hama, Layik},
Expand Down Expand Up @@ -439,6 +499,17 @@ @book{rodrigues_building_2023
langid = {english}
}

@software{szell_course_2025,
title = {Course Materials for: {{Geospatial Data Science}}},
author = {Szell, Michael},
date = {2025-01-22T05:53:53Z},
origdate = {2022-05-14T12:59:52Z},
url = {https://github.com/mszell/geospatialdatascience},
urldate = {2025-01-22},
abstract = {Course materials for: Geospatial Data Science},
keywords = {course-materials,data-science,geospatial,geospatial-analysis,geospatial-data,geospatial-visualization,gis,openstreetmap,osmnx,python,street-networks,teaching-materials}
}

@unpublished{szell_growing_2021,
title = {Growing {{Urban Bicycle Networks}}},
author = {Szell, Michael and Mimar, Sayat and Perlman, Tyler and Ghoshal, Gourab and Sinatra, Roberta},
Expand All @@ -452,17 +523,6 @@ @unpublished{szell_growing_2021
keywords = {⛔ No DOI found,Computer Science - Computers and Society,Physics - Physics and Society}
}

@software{szell_mszell_2025,
title = {Mszell/Geospatialdatascience},
author = {Szell, Michael},
date = {2025-01-22T05:53:53Z},
origdate = {2022-05-14T12:59:52Z},
url = {https://github.com/mszell/geospatialdatascience},
urldate = {2025-01-22},
abstract = {Course materials for: Geospatial Data Science},
keywords = {course-materials,data-science,geospatial,geospatial-analysis,geospatial-data,geospatial-visualization,gis,openstreetmap,osmnx,python,street-networks,teaching-materials}
}

@article{timms_imagineering_2014,
title = {Imagineering Mobility: Constructing Utopias for Future Urban Transport},
shorttitle = {Imagineering Mobility},
Expand Down

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