Student 12345
This template contains information and suggested headings for the TDS module. It is based on an RMarkdown file. See the source code here: https://github.com/ITSLeeds/TDS/blob/master/coursework-template.Rmd . Do not submit coursework that contains this note or any text (other than the headings) from this template. It is just designed to get you started. You will submit the .Rmd file, optionally included in a .zip file if you want to include data, and the resulting PDF document as your coursework submission.
As outlined in the module catalogue, the coursework should be a maximum
of 10 pages long, excluding references and appendices (you should
include your best work and think about maximising the use of space - the
chunk option out.width="50%", for example, can help with this as
outlined here )
An example of a good submission can be found here (requires a University of Leeds login): https://leeds365.sharepoint.com/sites/msteams_cbf52a/Shared%20Documents/General/stats19-example.zip
The information below provides guidance on the coursework.
- Deadline for non-assessed submission of a .zip file containing a 1
or 2 page pdf document with ideas before the final submission. The
document will allow you to ask questions (e.g. “does this sound like
a reasonable input dataset and topic?”) and describe progress on
reading in input datasets and the analysis plan. The document will
contain:
- A draft title of your topic
- The main dataset that you will use and other datasets that you could use
- Ideas on a research question
- Questions you would like to ask about the topic, e.g. ‘is this a suitable dataset?’
- 2 or more references to the academic literature related to the topic
- Any preliminary analysis you have done
- The structure of the document could include
- Topics considered
- Input datasets
- Analysis plan - I suggest creating a workflow diagram for this, e.g. as presented here
- Motivation for choosing this topic
- Questions and options
- Deadline: Friday 19th May 2023, 14:00
- Format: a PDF file (max 10 pages) and an Rmd file in a .zip file containing the Rmd file and minimal dataset needed to reproduce the results if possible (40 MB max size)
- Template: You can download a template .Rmd file as the basis of your submission: https://github.com/ITSLeeds/TDS/raw/master/coursework-template.Rmd
For the coursework you will submit a pdf a document with a maximum of 10 pages that contains code and results demonstrating your transport data science skills.
You should submit you work in .zip file containing everything needed to reproduce the results: https://github.com/ITSLeeds/TDS/releases/download/0.20.1/coursework-template.zip
You will need to choose a topic, one or more datasets to analyse and research questions for the 10 page coursework report.
Designing and writing a good data science project involves many stages, not just writing and and knitting an Rmd document reporting data analysis methods and results. The process involves:
- Brainstorming: what kind of topics and research questions are you interested in?
- Dataset identification: what datasets are available on the topic? If there are not good datasets related to the topic you may want to rethink your topic.
- Research questions: what questions do you want to know
- Data processing: what did you do to read-in the data?
- Exploratory data analysis: visualisation, describing the data
- Modelling/communication
As in real world data science work, there are many options to choose from. You should decide on a topic based on your personal interest and the availability of a good dataset. You can choose from and adapt one of the following options or choose a topic of your own.
-
Data collection and analysis
- What is the relationship between travel behaviour (e.g. as
manifested in origin-destination data represented as desire
lines, routes and route networks) and road traffic casualties in
a transport region (e.g. London, West Midlands and other regions
in the
pct::pct_regions$region_namedata) - Analysis of a large transport dataset, e.g. https://www.nature.com/articles/sdata201889
- What is the relationship between travel behaviour (e.g. as
manifested in origin-destination data represented as desire
lines, routes and route networks) and road traffic casualties in
a transport region (e.g. London, West Midlands and other regions
in the
-
Infrastructure and travel behaviour
- What are the relationships between specific types of infrastructure and travel, e.g. between fast roads and walking?
- How do official sources of infrastructure data (e.g. the
CID)
compare with crowd-sourced datasets such as OpenStreetMap (which
can be accessed with the new
osmextractR package) - Using new data sources to support transport planning, e.g. using data from https://telraam.net/ or https://dataforgood.facebook.com/dfg/tools/high-resolution-population-density-maps
-
Changing transport systems
- Modelling change in transport systems, e.g. by comparing before/after data for different countries/cities, which countries had the hardest lockdowns and where have changes been longer term? - see here for open data: https://github.com/ActiveConclusion/COVID19_mobility
- How have movement patterns changed during the Coronavirus pandemic and what impact is that likely to have long term (see here for some graphics on this)
-
Software development
- Creating a package to make a particular data source more accessible, see https://github.com/ropensci/stats19 and https://github.com/elipousson/crashapi examples
- Integration between R and A/B Street - see https://github.com/a-b-street/abstr
-
Road safety - how can we makes roads and transport systems in general safer?
-
Other
- Other topics are welcome
You should choose the main dataset that you will use for the coursework based on the topic and the availability of datasets. If you are interested in a particular dataset that could help you decide a topic. Good datasets include:
- STATS19 road crash data (other countries have other datasets),
e.g. using data from the
stats19package: https://docs.ropensci.org/stats19/ - ‘PCT’ data from UK travel behaviour - see https://itsleeds.github.io/pct/
- OpenStreetMap data (global, you will need to think of a subset by area/type), e.g. from the https://docs.ropensci.org/osmextract/index.html package
- Traffic count data, e.g. from the DfT, as described here: https://github.com/ITSLeeds/dftTrafficCounts
- Open data from a single city, e.g. Seattle: https://data-seattlecitygis.opendata.arcgis.com/
- See here: https://github.com/awesomedata/awesome-public-datasets#transportation
- And here: https://github.com/CUTR-at-USF/awesome-transit
- and here
If you are struggling for ideas and example code, these resources, in addition to the links provided in the lectures and practicals can help:
If this is a resit you must choose a different topic.
You could pick one of these topics:
-
What explanatory variables best predict the level of walking in Leeds (or a different English city), and how do walking levels relate to pedestrian safety? You could use the command
pct::get_desire_lines(region = "west-yorkshire")to get data on walking desire lines andget_stats19()to get road crash data for Leeds. In terms of the topics covered in the lectures you could:- Show understanding of data science in context with a brief introduction that touches on the definition of transport data science
- Demonstrate understanding of data structures by converting from a data frame of road crashes to an sf object
- Show routes, e.g. by getting route data with
pct::get_pct_routes_fast() - Show your data visualisation skills by visualising the datasets
- Demonstrate understanding of modelling with a simple model to explain why the rate of walking varies, e.g. with the distance of the trip and variables that you will calculate (e.g. distance from the city centre)
-
What factors are associated with low levels of walking and cycling and high road traffic casualty rates in the UK?
-
How accessible are parks and other amenities for different areas of a particular city
- This could be done using data from OSM and perhaps official data from local government
Marks are awarded in 4 categories, accounting for the following criteria:
**Data, processing access and cleaning**
-
The selection and effective use of input datasets that are large (e.g. covering multiple years), complex (e.g. containing multiple variables) and diverse (e.g. input datasets from multiple sources are used and where appropriate combined in the analysis)
-
Describe how the data was collected and implications for data quality, and outline how the input datasets were downloaded (with a reproducible example if possible)
-
Evidence of data cleaning techniques and adding value to the dataset (e.g. by creating new variables or by joining two datasets)
-
Use of transport planning techniques such as use of buffers, scenarios, origin-destination data analysis geographic desire lines and routing
Choice of topic and impact
-
Topic selection, including originality, availability of datasets related to the topic and relevance to solving transport planning problems
-
Discuss alternative topics considered and justify the topic
-
Make specific recommendations for further research or policies that are supported by the data analysis undertaken
-
Explain the potential impacts of the work
Visualization
-
Creation of figures that are readable and well-described (e.g. with captions and description)
-
High quality, attractive and innovative visualisations
-
Description of visualisation techniques, e.g. with reference to particular packages, commands, or the literature on how to produce effective data visualisations
-
Using visualisation techniques appropriate to the topic and data and interpreting the results correctly (e.g. mentioning potential confounding factors that could account for observed patterns)
Understand data science processes, concepts, methods and approaches
-
Demonstrate understanding of core concepts of data science, such as cleaning and reshaping data (e.g. data aggregation and dimension reduction), combining multiple datasets, joining, exploratory data analysis and visualisation, and how the work could be tranferred to different contexts (e.g. other time periods, cities and countries)
-
Discuss alternative methods/approaches that could have been used to research the topic (e.g. qualitative research, use of alternative data sources), and consider and their advantages and disadvantages compared with your data science approach
-
Enable reproducibility, by documenting key parts the code have written as part of the analysis, e.g. with reference to a key code chunk shown in the report and with reference to particular lines of code in the RMarkdown file underpinning the analysis, links to online resources for others wanting to reproduce the analysis for another area, and links to the input data, the report is fully reproducible, including generation of figures where possible and guidance on how to reproduce the results (e.g. provided in a footnote in the report or with a link to code stored in a platform such as GitHub/GitLab)
-
Discussion of the limitations of the analysis and implications for the findings and how they could be addressed in future research and what type of data collection activities could address those limitations
The report should have a logical structure with key headings such as:
- Introduction
- Input data and data cleaning
- Exploratory data analysis
- Discussion (e.g. strengths and weaknesses)
- Conclusion (e.g. how the results could be used and next steps)
- References
An example report structure is shown below.
This is an R Markdown file. You can set the output by changing
output: github_document to something different, like
output: html_document. You will need to submit your work as a pdf
document, which can be generated by converting html output to pdf
(e.g. with the pagedown package) or (recommended) by setting the
output to pdf_document. The first lines of your RMarkdown document
could look something like this to ensure that the output is a PDF
document and that the R code does not run (set eval = FALSE to not run
the R code):
---
title: "Coursework submission for Transport Data Science (TRAN5340M)"
subtitle: "Enter your own title here (e.g. Exploring open transport data: a study of the Isle of Wight)"
output:
pdf_document:
number_sections: true
author: "Student 12345"
bibliography: references.bib
---
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, eval = TRUE)See here for more info: https://rmarkdown.rstudio.com/lesson-2.html
When you open this file in RStudio and click the Knit button all R code chunks are run and a markdown file (.md) suitable for publishing to GitHub is generated.
To ensure the document is reproducible, you should include a code chunk that shows which packages you used. You may need to install new packages:
# install.packages("remotes")
install.packages("osmextract")
install.packages("pct")
install.packages("stats19")We load the package as follows:
library(pct)
library(sf)
library(stplanr)
library(tidyverse)
library(tmap)You can add references manually or with [@citation-key] references
linking to a .bib file like this[@lovelace_stplanr_2017]. And this
[@fox_data_2018].
You can include R code in the document as follows:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the code chunk to
prevent printing of the R code that generated the plot.
This example report explores road casualty data in the Isle of Wight.
You can get zone, OD and even route data for any city in the UK with the following commands. We got data for the Isle of Wight with the following commands:
library(pct)
region_name = "isle-of-wight"
z = get_pct_zones(region = region_name, geography = "msoa")
od = get_od()
od_in_zones = od %>%
filter(geo_code1 %in% z$geo_code) %>%
filter(geo_code2 %in% z$geo_code)
desire_lines = od2line(od_in_zones, z)You could get data from OpenStreetMap with the osmdata package.
library(osmdata)
osm_data = opq("isle of wight") %>%
add_osm_feature(key = "highway", value = "primary") %>%
osmdata_sf()You can get large OSM datasets with osmextract:
iow_highways = osmextract::oe_get("Isle of Wight", layer = "lines")## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | 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## 0...10...20...30...40...50...60...70...80...90...100 - done.
## Reading layer `lines' from data source
## `C:\RTemp\RtmpEjggFu\geofabrik_isle-of-wight-latest.gpkg' using driver `GPKG'
## Simple feature collection with 51155 features and 9 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: -5.715479 ymin: 43.35489 xmax: 1.92832 ymax: 51.16517
## Geodetic CRS: WGS 84
summary(as.factor(iow_highways$highway))## bridleway busway construction cycleway footway
## 175 3 37 225 7075
## living_street path pedestrian primary primary_link
## 3 806 33 603 22
## proposed raceway residential secondary secondary_link
## 12 1 2915 387 1
## service steps tertiary tertiary_link track
## 8091 452 531 2 4206
## unclassified NA's
## 873 24702
iow_highways2 = iow_highways %>%
filter(!is.na(highway)) %>%
filter(!str_detect(string = highway, pattern = "primary|track|resi|service|foot"))
summary(as.factor(iow_highways2$highway))## bridleway busway construction cycleway living_street
## 175 3 37 225 3
## path pedestrian proposed raceway secondary
## 806 33 12 1 387
## secondary_link steps tertiary tertiary_link unclassified
## 1 452 531 2 873
You could get road casualty data with the stats19 package, as shown
below.
crashes = stats19::get_stats19(year = 2018, output_format = "sf") %>%
sf::st_transform(crs = sf::st_crs(z))
crashes_in_region = crashes[z, ]
tm_shape(z) +
tm_fill("car_driver", palette = "Greys") +
tm_shape(iow_highways2) +
tm_lines(col = "green", lwd = 2) +
tm_shape(osm_data$osm_lines) +
tm_lines(col = "maxspeed", lwd = 5) +
tm_shape(crashes_in_region) +
tm_dots(size = 0.5, col = "red")
plot(desire_lines)
A next step could be route network analysis.
See https://luukvdmeer.github.io/sfnetworks/ for an approach we could use (this could be a coursework topic on its own).
Here you could explain how you explored answers to policy questions such as:
- how to make the roads safer?
- how to reduce congestion?
- where to build bike parking?
Include here limitations and ideas for further research.
What are the main things we have learned from this project?
To make the code reproducible I saved the code and data as follows
# Save outputs into zip file:
zip(zipfile = "coursework-template.zip", files = c(
"coursework-template.Rmd",
"tds.bib",
"timetable.csv"
))
# piggyback::pb_upload("coursework-template.zip") # ignore this command
# piggyback::pb_upload("coursework-template.pdf") # ignore this commandAdd your references here.
Reference 1
Reference 2
…
Lovelace, R., 2021. Open source tools for geographic analysis in transport planning. J Geogr Syst. https://doi.org/10.1007/s10109-020-00342-2
Boeing, G., 2021. Spatial information and the legibility of urban form: Big data in urban morphology. International Journal of Information Management 56, 102013. https://doi.org/10.1016/j.ijinfomgt.2019.09.009