diff --git a/.gitignore b/.gitignore index 52430b7e..556d9d65 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,9 @@ _freeze/ *cache* .Rproj.user *.geojson +*.zip + + +!s1/Estaciones_Troncales_de_TRANSMILENIO.geojson +*.docx +*.pdf diff --git a/DESCRIPTION b/DESCRIPTION index bd414d89..4a464443 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -26,7 +26,9 @@ Imports: spData, zoo, pct, - zonebuilder + zonebuilder, + ggspatial Remotes: nowosad/spDataLarge, - itsleeds/pct + itsleeds/pct, + zonebuilders/zonebuilder diff --git a/_quarto.yml b/_quarto.yml index 95c46c2e..35153ba8 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -35,8 +35,10 @@ website: text: Practical 1 - href: p2 text: Practical 2 - # - href: p3 - # text: Practical 3 + - href: p3 + text: Practical 3 + - href: s1 + text: Seminar 1 # - href: p4 # text: Practical 4 # - href: p5 @@ -47,7 +49,7 @@ website: icon: info-circle dismissable: true # content: "**Alert** ensure you have completed the second homework, described at the end of [itsleeds.github.io/tds/p1](https://itsleeds.github.io/tds/p1/#homework) by this Thursday 6th February." - content: "**Homework to present at the next session**: Ensure you have completed the second homework, described at the end of [itsleeds.github.io/tds/p2](https://itsleeds.github.io/tds/p2/#homework) by Thursday 13th February." + content: "**Homework to present at the next session**: Ensure you have completed the second homework, described at the end of [itsleeds.github.io/tds/p3](https://itsleeds.github.io/tds/p3/#homework) by **Thursday 20th February**. Note: there will be a practical (10:00-13:00) AND a seminar (14:00-17:00) on this day. Location: Institute for Transport Studies, room 1.11." type: primary position: below-navbar diff --git a/d2/index.qmd b/d2/index.qmd index 41069676..ee5782f7 100644 --- a/d2/index.qmd +++ b/d2/index.qmd @@ -1,11 +1,182 @@ --- -title: "Draft portfolio" +title: "Formative assessment brief: portfolio plan and reproducible data science code" bibliography: ../tds.bib -toc: true +toc: false execute: cache: true +number-sections: true --- -This is a placeholder page, contents will be added +```{r} +#| echo: false +#| eval: false +# docx format results in higher accessibility scores on Minerva: +quarto::quarto_render("d2/index.qmd", output_format = "docx", output_file = "tds-formative-assessment-brief.docx") +file.rename("docs/tds-formative-assessment-brief.docx", "~/OneDrive/career/modules/tds/2024-25/TRAN5340M TDS Formative Assessment Brief.docx") +``` +```{bash} +#| eval: false +#| echo: false +gh release upload 2025 formative-assessment-brief.docx +gh release upload 2025 formative-assessment-brief.pdf +``` + + +## Module Code & Title + +**TRAN5340M** - Using Data Science to Solve Transport Problems + +## Assessment Title + +Formative Coursework: Portfolio Plan and Reproducible Code + +## Assessment Type + +Portfolio Plan and Reproducible Code Submission + +## Learning Outcomes Assessed + +1. To develop a clear plan for applying data science techniques to a transport problem. +2. To demonstrate the ability to work with datasets and produce reproducible code. +3. To critically engage with academic literature and formulate research questions. + +## Assessment Length/Time Limit Guidance + +- A 5-page maximum PDF document outlining your topic, plan, and preliminary analysis. +- A .zip file containing the reproducible code (in a Quarto document saved as a `.qmd` file) and the resulting PDF. + +## Use of GenAI in This Assessment + +**AMBER**: AI tools can be used in an assistive role. +You are permitted to use AI tools for specific defined processes within the assessment. See below for further details. + +## Weighting + +Formative (non-assessed, but required for feedback and guidance). + +## Deadline or Date of Assessment + +**Non-assessed submission deadline**: 28th February 2025, 13:59. + +## Submission Method + +- A `.zip` file with: + - A short (5-page max) PDF document. + - Reproducible code (e.g., `.qmd` file). + - A maximum zip file size of 30 MB but ideally less (note: you may need to remove large data files from the folder before zipping). +- Submitted via Turnitin. + +## Feedback Provision + +Feedback will be provided within 15 working days of submission. Written feedback will be provided alongside guidance on how to proceed with the final coursework. + +## Module Leader & Contact Details + +**Professor Robin Lovelace** +r.lovelace@leeds.ac.uk + +--- + +## Assessment Summary Guidance + +This formative coursework is designed to help you plan and receive feedback on your final coursework. You will submit a `.zip` file containing a PDF document and reproducible code. The document should outline your topic, datasets, research questions, and analysis plan. Feedback will be provided to guide your final submission. + +--- + +## Use of GenAI + +**Generative AI Category: AMBER** +Under this category, AI tools can be used in an assistive role. + +In this assessment, AI tools can be utilised in an assistive role to: +- Act as a support tutor, assisting your research on the topic (e.g., asking clarifying questions or suggesting areas to investigate). +- Test and debug any code you produce yourself as part of the assignment. +- Provide ideas or inspiration to help you overcome a creative block. +- Give feedback on content or proofread content that you have generated yourself. + +In this assessment, AI tools **cannot** be utilised to: +- Produce the entirety of, or sections of, a piece of work that you submit for assessment beyond that which is outlined above. + +The use of Generative AI must be acknowledged in an **‘Acknowledgements’** section of any piece of academic work where it has been used as a functional tool to assist in the process of creating academic work. The minimum requirement to include in acknowledgement: +- Name and version of the generative AI system used (e.g., ChatGPT-4.0). +- Publisher (company that made the AI system) (e.g., OpenAI). +- URL of the AI system. +- Brief description (single sentence) of the context in which the tool was used. + +**Example:** +“I acknowledge the use of ChatGPT-3.5 (Open AI, https://chat.openai.com/) to summarise my initial notes and to proofread my final draft.” + +The standard Academic Misconduct procedure applies for students believed to have ignored this categorisation. +For detailed guidance, see [Generative AI and Assessments](https://generative-ai.leeds.ac.uk/ai-and-assessments/categories-of-assessments/). + +--- + +## General Guidance + +Skills@Library hosts useful guidance on academic skills, including specific guidance on academic writing and referencing. Visit [Academic Skills | Library | University of Leeds](https://library.leeds.ac.uk/info/1402/referencing/50/leeds-harvard-introduction). + +--- + +## Assessment Criteria and Process + +### Submission Requirements + +You will submit a `.zip` file containing: + +1. A **5-page maximum PDF document** with the following: + - A draft title of your topic. + - The main dataset you will use and other potential datasets. + - Ideas on a research question. + - Questions you would like to ask about the topic (e.g., "Is this a suitable dataset?"). + - At least **2 references** to academic literature related to the topic. + - Minimal code and/or a description of where you accessed the data and how you imported it. + - Any preliminary analysis you have done. + - A suggested structure for the document: + - Topics considered. + - Input datasets. + - Analysis plan (e.g., a workflow diagram as shown [here](https://user-images.githubusercontent.com/1825120/127524923-7d9f5511-84a6-430b-8de9-a603a5524f39.png)). + - Motivation for choosing this topic. + - Questions and options. +2. **Reproducible code** in a `.qmd` file. + +### Purpose of the Submission + +- To allow you to ask questions to the course team (e.g., "Does this sound like a reasonable input dataset and topic?"). +- To describe progress on reading input datasets and the analysis plan. +- To receive feedback on your approach before the final submission. + +### Rendering Instructions + +- If you cannot render to PDF directly, render to HTML and convert to PDF by printing to PDF from your browser. + +--- + +## Presentation/Formatting and Referencing + +You must appropriately cite all supporting evidence using the **Leeds Harvard referencing style**. For guidance, visit [Leeds Harvard Referencing](https://library.leeds.ac.uk/info/1402/referencing/50/leeds-harvard-introduction). + +See [the authoring tutorial with RStudio at quarto.org](https://quarto.org/docs/get-started/authoring/rstudio.html#citations) for guidance on how to add citations to your document in RStudio's Visual Editor mode. + +--- + +## Academic Misconduct and Plagiarism + +The university expects that all the work you do, which includes all forms of assessments submitted and examinations taken, meet the university’s standard for Academic Integrity. All forms of Academic Integrity are investigated through the Academic Misconduct Procedure. This applies to all taught elements of your study, including undergraduate programmes, taught postgraduate study, and taught elements of research degrees. Breaching academic integrity standards can lead to serious penalties. + +Guidance on Academic Integrity and Academic Misconduct can be found on the [For Students website pages](https://students.leeds.ac.uk/info/10110/academic-integrity). Full definitions of offences under the Academic Misconduct Procedure can be found in the [Academic Misconduct Procedure](https://secretariat.leeds.ac.uk/student-cases/academic-misconduct/). + +--- + +## Assessment Criteria Rubric + +The assessment is formative so is not assessed, but you will be provided with feedback on the following criteria: + +- Clarity and feasibility of the proposed topic and research question. +- Appropriateness of the selected datasets. +- Engagement with academic literature. +- Quality of the analysis plan and workflow diagram. +- Reproducibility of the code and documentation. + +See the [Marking Criteria](https://itsleeds.github.io/tds/marking-criteria.html) for more details. \ No newline at end of file diff --git a/index.qmd b/index.qmd index c45ec705..cbd090dd 100644 --- a/index.qmd +++ b/index.qmd @@ -4,7 +4,8 @@ number-sections: false --- A module on using data science to solve transport problems. -This couse is based at the University of Leeds' Institute for Transport Studies. +This couse is based at the University of Leeds' Institute for Transport Studies (module code [TRAN5340M](https://webprod3.leeds.ac.uk/catalogue/dynmodules.asp?Y=202425&M=TRAN-5340M)). +Led by Robin Lovelace, Professor of Transport Data Science and developer of several data-driven solutions for effective transport planning, the course is designed to teach you how to use data science to solve transport problems. It has evolved over a decade of teaching and research in the field and aims to teach you up-to-date and future-proof skills with practical examples and reproducible workflows using industry-standard data science tools. # Prerequisites diff --git a/p1/slides.qmd b/p1/slides.qmd index c0d3aaca..abaa7704 100644 --- a/p1/slides.qmd +++ b/p1/slides.qmd @@ -139,11 +139,11 @@ The Bureau of Labor Statistics in the US projects a 35% increase in data science

-Milestone passed in my academic career, first online-only delivery of lecture @ITSLeeds, seems to have worked, live code demo with #rstats/@rstudio, recording, chat + all🎉

Thanks students for 'attending' + remote participation, we'll get through this together.#coronavirus pic.twitter.com/wlAUxmZj5r +Milestone passed in my academic career, first online-only delivery of lecture @ITSLeeds, seems to have worked, live code demo with #rstats/rstudio, recording, chat + all🎉

Thanks students for 'attending' + remote participation, we'll get through this together.#coronavirus pic.twitter.com/wlAUxmZj5r

-— Robin Lovelace (@robinlovelace) March 17, 2020 +— Robin Lovelace March 17, 2020 diff --git a/p2/index.qmd b/p2/index.qmd index b7921db1..60fa010a 100644 --- a/p2/index.qmd +++ b/p2/index.qmd @@ -188,12 +188,14 @@ system("gh release upload 2025 lad_boundaries_2023.geojson") base_url = "https://github.com/ITSLeeds/tds/releases/download/2025/" if (!file.exists("custom-filtered-2025-02-04T00_06_30Z.csv")) { download.file(paste0(base_url, "custom-filtered-2025-02-04T00_06_30Z.csv"), "custom-filtered-2025-02-04T00_06_30Z.csv") +} +if (!file.exists("lad_boundaries_2023.geojson")) { download.file(paste0(base_url, "lad_boundaries_2023.geojson"), "lad_boundaries_2023.geojson") } -res_sf = sf::st_read("lad_boundaries_2023.geojson") ``` ```{r} +res_sf = sf::st_read("lad_boundaries_2023.geojson") travel_to_work_lad = readr::read_csv("custom-filtered-2025-02-04T00_06_30Z.csv") # names(travel_to_work_lad) # [1] "Lower tier local authorities Code" diff --git a/p3/index.qmd b/p3/index.qmd index 8d82d1b6..3af3cacf 100644 --- a/p3/index.qmd +++ b/p3/index.qmd @@ -14,6 +14,16 @@ bibliography: ../tds.bib engine: knitr --- +# Introduction + +In this practical session, we will learn how to use geogarphic and origin-destination data. The contents of the session are as follows: + +- We will start with reviewing the homework from the previous session +- A short lecture on geographic and origin-destination data (see [slides](https://docs.google.com/presentation/d/1fKXK1ocCSkTQkTQn0eoYWURIfQuFgQwICFiDGVetMek/edit?usp=sharing)) +- Practical session working with various data, including analysing origin-destination trip flows in London Cycle Hire System. +- Bonus: Geometry operations and spatial analysis +- Homework and next session + # Review Homework Your homework last week was to import and visualise some transport datasets. @@ -26,7 +36,6 @@ In groups of 2 or 3 (a demonstrator will help out with each group): Note that this practical takes sections from Chapters 2 - 8 of [Geocomputation with R](https://r.geocompx.org). You should expand your knowledge by reading these chapters in full. - ## Pre-requisites {-} You need to have a number of packages installed and loaded. @@ -41,15 +50,16 @@ If you need to install any of these packages use: ```{r} #| eval: false if (!require("pak")) install.packages("pak") -pak::pkg_install(c("sf", "tidyverse", "remotes")) +pak::pkg_install(c("sf", "tidyverse", "remotes", "ggspatial")) # GitHub pkgs pak::pkg_install("Nowosad/spDataLarge") ``` ``` {r} -library(sf) # vector data package -library(tidyverse) # tidyverse packages -library(spData) # spatial data package +library(sf) # vector data package +library(tidyverse) # tidyverse packages +library(ggspatial) # ggspatial package +library(spData) # spatial data package ``` ## Python @@ -71,7 +81,7 @@ import geopandas.datasets ::: 1. Check your packages are up-to-date with `update.packages()` in R (or equivalent in Python) -1. Create a project folder with an appropriate name for this session (e.g. `practical2`) +1. Create a project folder with an appropriate name for this session (e.g. `practical3`) 1. Create appropriate folders for code, data and anything else (e.g. images) 1. Create a script called `learning-OD.R`, e.g. with the following command: @@ -109,7 +119,6 @@ Use some basic R functions to explore the `world` object. e.g. `class(world)`, ` `sf` objects can be plotted with `plot()`. - ::: {.panel-tabset} ## R ```{r} @@ -157,8 +166,6 @@ plt.show() Load the `nz` and `nz_height` datasets from the `spData` package. - - ::: {.panel-tabset} ## R ```{r} @@ -177,7 +184,6 @@ nz_height = gpd.read_file("https://github.com/Nowosad/spData_files/raw/refs/head We can use `tidyverse` functions like `filter` and `select` on `sf` objects in the same way you did in Practical 1. - ::: {.panel-tabset} ## R ```{r} @@ -228,17 +234,14 @@ canterbury_height = gpd.sjoin(nz_height, canterbury, op='intersects') ``` ::: - ![Topological relations between vector geometries, inspired by Figures 1 and 2 in Egenhofer and Herring (1990). The relations for which the function(x, y) is true are printed for each geometry pair, with x represented in pink and y represented in blue. The nature of the spatial relationship for each pair is described by the Dimensionally Extended 9-Intersection Model string. ](https://r.geocompx.org/figures/relations-1.png) - # Getting started with OD data In this section we will look at basic transport data in the R package **stplanr**. Load the `stplanr` package as follows: - ```{r} #| echo: true #| output: false @@ -292,9 +295,9 @@ Next we will made a new dataset `od_data_walk` by taking `od_data` and piping it ## R ```{r} #| echo: true -od_data_walk = od_data |> - filter(walk > 0) |> - select(geo_code1, geo_code2, all, car_driver, walk) |> +od_data_walk = od_data |> + filter(walk > 0) |> + select(geo_code1, geo_code2, all, car_driver, walk) |> mutate(proportion_walk = walk / all, proportion_drive = car_driver / all) ``` @@ -324,7 +327,6 @@ plt.show() ::: R has built in modelling functions such as `lm` lets make a simple model to predict the proportion of people who walk based on the proportion of people who drive. - ::: {.panel-tabset} ## R ```{r} @@ -369,14 +371,12 @@ plt.show() Exercises - 1. What is the class of the data in `od_data`? 2. Subset (filter) the data to only include OD pairs in which at least one person (`> 0`) person walks (bonus: on what % of the OD pairs does at least 1 person walk?) 3. Calculate the percentage who cycle in each OD pair in which at least 1 person cycles 4. Is there a positive relationship between walking and cycling in the data? 5. Bonus: use the function `od2line()` in to convert the OD dataset into geographic desire lines - Code for Exercises (click to reveal, but try to do them yourself first)
@@ -401,7 +401,7 @@ print("Class of od_data:", type(od_data)) ```{r} #| eval: false -od_data_walk = od_data |> +od_data_walk = od_data |> filter(walk > 0) nrow(od_data_walk) / nrow(od_data) * 100 ``` @@ -418,10 +418,10 @@ print(f"Percentage of OD pairs where at least one person walks: {percentage_walk ### R ```{r} #| eval: false -#| -od_data = od_data |> - filter(bicycle > 0) |> - mutate(perc_cycle = (bicycle/all) * 100) +#| +od_data = od_data |> + filter(bicycle > 0) |> + mutate(perc_cycle = (bicycle / all) * 100) ``` ### Python @@ -438,9 +438,9 @@ od_data_cycle['perc_cycle'] = (od_data_cycle['bicycle'] / od_data_cycle['all']) ```{r} #| eval: false -od_data_new = od_data |> - filter(walk > 0, bicycle>0 ) |> - select(bicycle, walk, all) +od_data_new = od_data |> + filter(walk > 0, bicycle > 0) |> + select(bicycle, walk, all) model = lm(walk ~ bicycle, weights = all, data = od_data_new) od_data_new$walk_predicted = model$fitted.values @@ -485,7 +485,7 @@ plt.show() desire_lines = stplanr::od2line(flow = od_data, zones = zone) plot(desire_lines) -#save zone as gpkg +# save zone as gpkg sf::st_write(zone, "zone.geojson") ``` @@ -550,7 +550,6 @@ This section is based on [Chapter 12 of Geocomputation with R](https://geocompr. We need the `stplanr` package which provides many useful functions for transport analysis and `tmap` package which enables advanced mapping features. - ```{r} #| echo: true #| output: false @@ -559,7 +558,6 @@ library(stplanr) library(tmap) ``` - We will start by loading two datasets: ::: {.panel-tabset} @@ -652,22 +650,53 @@ desire_lines$Active = (desire_lines$bicycle + desire_lines$foot) / desire_lines['Active'] = (desire_lines['bicycle'] + desire_lines['foot']) / desire_lines['all'] * 100 ``` ::: -Now use `tmap` to make a plot showing the number of trips and the percentage of people using active travel. +Now use `tmap` to make a plot showing the number of trips and the percentage of people using active travel. Alternatively, you may use the `ggplot2` for visualisation. ::: {.panel-tabset} -## R + +## R: tmap ```{r} #| echo: true #| output: true -desire_lines = desire_lines[order(desire_lines$Active),] -tm_shape(desire_lines) + # Define the data frame used to make the map - tm_lines(col = "Active", # We want to map lines, the colour (col) is based on the "Active" column - palette = "plasma", # Select a colour palette - alpha = 0.7, # Make lines slightly transparent - lwd = "all") + # The line width (lwd) is based on the "all" column +desire_lines = desire_lines[order(desire_lines$Active), ] + +tm_shape(desire_lines) + # Define the data frame used to make the map + tm_lines( + col = "Active", # We want to map lines, the colour (col) is based on the "Active" column + palette = "plasma", # Select a colour palette + alpha = 0.7, # Make lines slightly transparent + lwd = "all" + ) + # The line width (lwd) is based on the "all" column tm_layout(legend.outside = TRUE) + # Move the ledgend outside the map - tm_scale_bar() # Add a scale bar to the map + tm_scale_bar() # Add a scale bar to the map +``` + +## R: ggplot2 +```{r} +#| echo: true +#| output: true + +desire_lines = desire_lines[order(desire_lines$Active), ] + +ggplot() + + geom_sf( + data = desire_lines, # Define the data frame used to make the map + aes( + colour = Active, # We want to map lines, the colour is based on the "Active" column + alpha = all, # Make lines slightly transparent and based on the "all" column + linewidth = all # The line width is based on the "all" column + ) + ) + + scale_colour_viridis_c(option = "C") + # Select a colour palette + scale_linewidth_binned(range = c(0.03, 0.6), guide = "legend") + # Set max and min ranges for line width + scale_alpha(range = c(0.1, 1), guide = "legend") + # Set max and min ranges for transparency + annotation_scale(location = "br", width_hint = 0.3) + # Scale bar (bottom left) + annotation_north_arrow( + location = "tl", which_north = "true", + style = north_arrow_fancy_orienteering() + ) + # North arrow (top left) + theme_void() ``` ## Python @@ -699,7 +728,6 @@ plt.show() ::: Now that we have geometry attached to our data we can calculate other variables of interest. For example let's calculate the distacne travelled and see if it relates to the percentage of people who use active travel. - ::: {.panel-tabset} ## R ```{r} @@ -735,20 +763,377 @@ plt.show() The blue line is a smoothed average of the data. It shows a common concept in transport research, the distance decay curve. In this case it shows that the longer the journey the less likely people are to use active travel. But this concept applies to all kinds of travel decisions. For example you are more likely to travel to a nearby coffee shop than a far away coffee shop. Different types of trip have different curves, but most people always have a bias for shorter trips. +# Processing origin-destination data in London Cycle Hire System + +In this section, we will be working with origin-destination data in London Cycle Hire System (LCHS). We will specifically examine how a London Tube(Underground) strike influenced cyclists' origin-destination choices. +Public transport disruptions are becoming increasingly common, triggered by various factors including: + +* Infrastructure maintenance requirements +* Natural disasters +* Large-scale events (festivals, strikes, etc.) + +By analyzing how people adapt their travel patterns during such events, we can better inform urban transport planning and decision-making strategies. + +For this section, we'll be using cleaned cycling data originally sourced from TfL's open data portal (cycling.data.tfl.gov.uk). + +Let's begin by loading the following two datasets + +```{r} +#| echo: true +#| output: false + +# load the station location dataset +bike_docking_stations = read_sf("https://github.com/itsleeds/tds/releases/download/2025/p3-london-bike_docking_stations.geojson") +# load the bike trip origin and destination dataset +bike_trips = read_csv("https://github.com/itsleeds/tds/releases/download/2025/p3-london-bike_trips.csv") +``` + +Explore these datasets using the functions you have learnt (e.g. head, nrow) +```{r} +head(bike_docking_stations) +nrow(bike_docking_stations) +head(bike_trips) +nrow(bike_trips) +``` + +Let's examine our `bike_trips` data, which covers three specific Thursdays: July 2nd, July 9th, and July 16th, 2015. July 9th marks the London Underground strike, while the other two dates serve as comparison points one week before and after the strike. + +During the Tube Strike, some people adopted bikeshare to replace the Tube travel, hence we should be able to observe some different in trip account. + +```{r} +bike_trips = bike_trips |> + mutate(date = date(start_time)) |> + mutate(type_day = case_when( + date == as.Date("2015-07-09") ~ "Strike Day", + TRUE ~ "Non-Strike Day" + )) +bike_trips |> + group_by(date, type_day) |> + summarise(count = n()) |> + ggplot() + + geom_bar(aes( + x = as.factor(date), + y = count, + fill = type_day + ), stat = "identity") + + xlab("Date") + + ylab("Count of bike trips") + + labs(fill = "Type of day") +``` + +The increase in trips is likely unevenly distributed by time, so it would be useful to examine the changes and differences by hour. This will help us identify when the most significant changes occur. + +```{r} +bike_trips |> + mutate( + hour = hour(start_time), + date = as.factor(date) + ) |> + group_by(date, hour) |> + summarise(count = n()) |> + ggplot() + + geom_line(aes(x = hour, y = count, color = date, group = date), size = 1, alpha = .6) + + geom_point(aes(x = hour, y = count, color = date), size = 2) + + scale_x_continuous(breaks = seq(0, 23, by = 4)) +``` + +Whist above temporal analysis provide useful summaries, visual analysis of the origin-destination changes allow us to characterise with greater richness the nature of changes in response to the strike events. Let' try to calculate the changes in o-d and map them! + +To break the task down, we will +1. Calculate the frequeny of each origin-destination pair on the strike day +2. Calculate the average frequancy of each origin-destination pair on the non-strike day +3. find out the difference + +```{r} +# 1. Calculate the frequeny of each origin-destination pair on the strike day +od_strike = bike_trips |> + filter(type_day == "Strike Day") |> + group_by(start_station_id, end_station_id) |> + summarise(count_strike = n()) + +# 2. Calculate the average frequancy of each origin-destination pair on the non-strike day +od_non_strike = bike_trips |> + filter(type_day == "Non-Strike Day") |> + group_by(start_station_id, end_station_id) |> + summarise(count_non_strike = n() / 2) # we have two non-strike days, hence /2 can help get the mean value +``` + +To obtain the changes, we will need to join the origin-destination pairs in the two dataframe we just created. + +The **`dplyr` package** provides a family of intuitive join functions: + +| Join Type | Function | Description | +|------------|---------------------------------|-------------| +| **Inner Join** | `inner_join(df1, df2, by = "key")` | Keeps only matching rows in both datasets. | +| **Left Join** | `left_join(df1, df2, by = "key")` | Keeps all rows from `df1`, adds matching rows from `df2`. | +| **Right Join** | `right_join(df1, df2, by = "key")` | Keeps all rows from `df2`, adds matching rows from `df1`. | +| **Full Join** | `full_join(df1, df2, by = "key")` | Keeps all rows from both datasets. | +| **Semi Join** | `semi_join(df1, df2, by = "key")` | Keeps rows from `df1` that have matches in `df2`. | +| **Anti Join** | `anti_join(df1, df2, by = "key")` | Keeps rows from `df1` that do **not** have matches in `df2`. | + +```{r} +# join the two dataframes +od_change = full_join(od_strike, od_non_strike, by = c("start_station_id", "end_station_id")) |> + replace_na(list(count_strike = 0, count_non_strike = 0)) + +# create(mutate) a new column of changes in od count +od_change = filter(od_change, start_station_id != end_station_id) |> + mutate(count_change = count_strike - count_non_strike) |> + arrange(desc(count_change)) +``` + +Let have a look at the joined result: + +```{r} +head(od_change) +``` + +We have succesfully joined the data, and the output `od_change` has both the trip count on strike day (`count_strike`) and non-strike day (`count_non_strike`), in addition, a new variable of `count_change` is created. Next, let clean the output further and create the od lines + +```{r} +# focusing on not self-loop trips +od_inter_change = filter(od_change, start_station_id != end_station_id) |> arrange(desc(count_change)) + +# create the od lines +change_desire_lines = od2line(od_change, bike_docking_stations) +``` + +We are particularly interested in identifying origin-destination pairs where the number of trips has increased. To focus on these, we use the filter function to select desire lines with at least one additional trip on the strike day. You can adjust the threshold (currently set to 1) as needed. + +```{r} +ggplot() + + geom_sf( + data = change_desire_lines |> filter(count_change >= 1) |> arrange(count_change), # Define the data frame used to make the map + aes( + colour = count_change, # We want to map lines, the colour is based on the "count_change" column + alpha = count_change, # Make lines slightly transparent and based on the "count_change" column + linewidth = count_change # The line width is based on the "count_change" column + ) + ) + + scale_colour_viridis_c(option = "C") + # Select a colour palette + scale_linewidth_binned(range = c(0.01, 1.6), guide = "legend") + # Set max and min ranges for line width + scale_alpha(range = c(0.001, 0.7), guide = "legend") + # Set max and min ranges for transparency + annotation_scale(location = "br", width_hint = 0.3) + # Map scale bar (bottom right) + annotation_north_arrow( + location = "tl", which_north = "true", + style = north_arrow_fancy_orienteering() + ) + # North arrow (top left) + theme_void() +``` + +To enhance the map, adding some background context could be useful. Let’s try incorporating the River Thames and green spaces (parks) in central London. + +Let’s check how the background looks. You can also customize the colors of the rivers and parks by modifying the values assigned to `fill` and `color`. +```{r} +# load the river and parks +rivers = st_read("https://github.com/itsleeds/tds/releases/download/2025/p3-london-rivers.geojson") +parks = st_read("https://github.com/itsleeds/tds/releases/download/2025/p3-london-parks.geojson") + +ggplot() + + geom_sf(data = parks, fill = "#d9f5e0", color = "#d9f5e0") + # add parks in central London to the map + geom_sf(data = rivers, color = "#08D9D6", linewidth = 3) + # add river Thames in central London to the map + theme_void() +``` + +Now, let’s bring the origin-destination desire lines and the base map together! Can you identify key locations where bike trip origin-destination changes are most significant? + +```{r} +# Define the data frame used to visualise the od +change_to_plot = change_desire_lines |> + filter(count_change >= 1) |> + arrange(count_change) + +ggplot() + + # add parks in central London to the map: + geom_sf(data = parks, fill = "#d9f5e0", color = "#d9f5e0") + + # add river Thames in central London to the map: + geom_sf(data = rivers, color = "#08D9D6", linewidth = 3) + + geom_sf( + data = change_to_plot, + aes( + colour = count_change, # We want to map lines, the colour is based on the "count_change" column + alpha = count_change, # Make lines slightly transparent and based on the "count_change" column + linewidth = count_change # The line width is based on the "count_change" column + ) + ) + + scale_colour_viridis_c(option = "C") + # Select a colour palette + scale_linewidth_binned(range = c(0.01, 1.6), guide = "legend") + # Set max and min ranges for line width + scale_alpha(range = c(0.001, 0.7), guide = "legend") + # Set max and min ranges for transparency + annotation_scale(location = "br", width_hint = 0.3) + # Scale bar (bottom left)y + annotation_north_arrow( + location = "tl", which_north = "true", + style = north_arrow_fancy_orienteering() + ) + # North arrow (top left) + theme_void() +``` +# Bonus + +During the Tube strike, bike docking stations closer to Tube stations are likely to experience higher travel activity than those farther away. + +To quickly assess whether there is a difference, we can create a buffer around Tube stations and calculate the number of trips originating from bike docking stations within the buffer zone. We can then compare these figures to those from docking stations outside the buffer zone. + +First, let create the buffer zones for tube stations in central london + +```{r} +# import the tube station data +tube_stations = read_sf("https://github.com/itsleeds/tds/releases/download/2025/p3-london-tube_stations.geojson") + +# use the bounding box of the bike docking stations to crop the tube stations +# this can help us focus on central London - which is covered by the LCHS +tube_stations_central_london = st_crop(tube_stations, st_bbox(bike_docking_stations)) + +# create the buffer zones, we set the buffer radius to 250m +# you may adjust the value if you would like to create smaller or larger buffer zones. +tube_stations_central_london_buffer = st_buffer(tube_stations_central_london %>% st_transform(27700), dist = 250) + +# visualise the buffers, tube stations and bike docking stations +ggplot()+ + geom_sf(data = tube_stations_central_london_buffer, fill ="#3ba2c7", color = NA, alpha =0.3)+ + geom_sf(data = tube_stations_central_london, color = "#3ba2c7")+ + geom_sf(data = bike_docking_stations, color = "#d45b70", size = 0.5) +``` + +You may have noticed that we created a buffer for each Tube station. We can merge all the buffers geometry into a single geometry. + +`st_union()` is a function in the `sf` package in R, used to merge or dissolve geometries into a single geometry. It is commonly used in spatial data processing when you need to combine multiple polygons, lines, or points into one. + +```{r} +tube_stations_central_london_buffer_merged = st_union(tube_stations_central_london_buffer) + +ggplot()+ + geom_sf(data = tube_stations_central_london_buffer_merged, fill ="#3ba2c7", color = NA, alpha =0.3)+ + geom_sf(data = tube_stations_central_london, color = "#3ba2c7")+ + geom_sf(data = bike_docking_stations, color = "#d45b70", size = 0.5) +``` + +Since we have obtained the merged buffer, let's find out which bike docking stations fall inside the buffer. To achieve this, we can use the `st_within()` function. `st_within()` checks if one geometry is completely inside another geometry. + +We also need to create a new column for the `bike_docking_stations` to store the result from `st_within()` + +```{r} +bike_docking_stations = bike_docking_stations |> + mutate(inside_tube_buffer = st_within(bike_docking_stations,tube_stations_central_london_buffer_merged %>% st_transform(4326), sparse = FALSE)) +head(bike_docking_stations) +``` + +As you can see, the `bike_docking_stations` now has a column named `inside_tube_buffer`, which contains binary outcome. + +Let's plot them and use `inside_tube_buffer` for the colour of the bike docking staions. + +```{r} +ggplot()+ + geom_sf(data = tube_stations_central_london_buffer_merged, fill ="#3ba2c7", color = NA, alpha =0.3)+ + geom_sf(data = bike_docking_stations, + # use "inside_tube_buffer" for the colour of the bike docking staions + aes(color = inside_tube_buffer), + size = 1) +``` + +Finally, let’s compare the average number of trips (by trip origin) between the two types of bike docking stations: those inside the buffer and those outside the buffer. + +What pattern did you observe? + +```{r} +bike_trips |> + group_by(start_station_id, date, type_day) |> + summarise(count = n(), .groups = "drop") |> + + # Join with docking station data to get station attributes (excluding geometry) + left_join( + bike_docking_stations |> st_drop_geometry(), + by = c("start_station_id" = "station_id") + ) |> + + # Group by whether the station is inside the Tube buffer and by type_day (strike day or non-strike day) + group_by(inside_tube_buffer, type_day) |> + summarise(mean_count = mean(count), .groups = "drop") |> + + # Plot the average trip count by type_day, bar fill colour by buffer status + ggplot(aes(x = type_day, y = mean_count, fill = inside_tube_buffer)) + + geom_bar(stat = "identity", position = position_dodge2()) + + labs( + x = "Date", + y = "Average Trip Count", + fill = "Inside Tube Buffer", + title = "Comparison of Average Trip Count by Bike Docking Stations" + ) +``` # Homework 1. Read Chapters 2-5 of [Geocomputation with R](https://r.geocompx.org/transport.html) -2. Using knowledge about datasets and new skills covered in the practicals 2 and 3, think of a simple research question. Write a prompt that generates some code to try to answer that question and paste the resulting code into a code chunk in a new .qmd file, e.g. called `gen-ai-test.qmd`. +2. Using knowledge about datasets and new skills covered in the practicals 2 and 3, think of a simple research question that could be answered by analysing/visualising/modelling one of the datasets imported into R in your previous homework (or another dataset you have imported into R). +3. Write a prompt, starting with your own code that imports the code, to generate some code answering the question and write it down in a new Quarto file called `gen-ai-test.qmd`. +4. Copy the prompt and paste it into a generative AI chat interface such as [ChatGPT](https://chatgpt.com), [Claude](https://claude.ai/), [DeepSeek](https://chat.deepseek.com/) or [Gemini](https://gemini.google.com/) +5. Paste the resulting code into a code chunk in a new .qmd file, e.g. called `gen-ai-test.qmd`. - Does the code run first time? + - Do the results answer the question? - Try modifying the code and the prompt to improve the code generated by the AI. - Note: in the next practical session we will review the AI-generated code you will share the code with a colleague and they will try to run it. - - Bonus: try setting-up Copilot or other code auto-completion system in RStudio using the documentation at +6. Bonus: try setting-up Copilot or other code auto-completion system in RStudio using the documentation at - [docs.posit.co](https://docs.posit.co/ide/user/ide/guide/tools/copilot.html) or VSCode using the [GitHub Copilot](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot) extension. - - Bonus: share your code on GitHub, for example by pasting it into the discussion group at [github.com/ITSLeeds/TDS/discussions](https://github.com/itsleeds/tds/discussions/172) -3. Bonus: Read more about using the [tmap package](https://r-tmap.github.io/tmap/) -4. Bonus: Read more about the [ggplot2 package](https://ggplot2.tidyverse.org/) + [docs.posit.co](https://docs.posit.co/ide/user/ide/guide/tools/copilot.html) or VSCode using the [GitHub Copilot](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot) extension. +7. Bonus: share your code on GitHub, for example by pasting it into the discussion group at [github.com/ITSLeeds/TDS/discussions](https://github.com/itsleeds/tds/discussions/172) or by creating a repo on your own GitHub account. +8. Bonus: Read more about using the [tmap package](https://r-tmap.github.io/tmap/) +9. Bonus: Read more about the [ggplot2 package](https://ggplot2.tidyverse.org/) + +
+I wrote the following prompt: + +Starting with the following R code that starts by loading the tidyverse and stats19 R packages, write a script that finds out which local authorities saw the greatest percentage point decrease in the number of road traffic collisions between 2019 and 2020. +Explore this relationship for the total number of collisions with summary statistics, ggplot2 visualisations, and perhaps a basic model. Furthermore, explore how the % change in collision numbers vary depending on factors such as urban or rural area, casualty severity, and the month used for comparison. + +```{r} +library(tidyverse) +library(stats19) +collisions_2019 = get_stats19(2019) +collisions_2020 = get_stats19(2020) +collisions_combined = bind_rows( + mutate(collisions_2019, year = 2019), + mutate(collisions_2020, year = 2020) +) +# names(collisions_2020) +# [1] "accident_index" +# [2] "accident_year" +# [3] "accident_reference" +# [4] "location_easting_osgr" +# [5] "location_northing_osgr" +# [6] "longitude" +# [7] "latitude" +# [8] "police_force" +# [9] "accident_severity" +# [10] "number_of_vehicles" +# [11] "number_of_casualties" +# [12] "date" +# [13] "day_of_week" +# [14] "time" +# [15] "local_authority_district" +# [16] "local_authority_ons_district" +# [17] "local_authority_highway" +# [18] "first_road_class" +# [19] "first_road_number" +# [20] "road_type" +# [21] "speed_limit" +# [22] "junction_detail" +# [23] "junction_control" +# [24] "second_road_class" +# [25] "second_road_number" +# [26] "pedestrian_crossing_human_control" +# [27] "pedestrian_crossing_physical_facilities" +# [28] "light_conditions" +# [29] "weather_conditions" +# [30] "road_surface_conditions" +# [31] "special_conditions_at_site" +# [32] "carriageway_hazards" +# [33] "urban_or_rural_area" +# [34] "did_police_officer_attend_scene_of_accident" +# [35] "trunk_road_flag" +# [36] "lsoa_of_accident_location" +# [37] "enhanced_severity_collision" +# [38] "datetime" +``` + +
-# References \ No newline at end of file +# References diff --git a/p3/index_files/figure-html/unnamed-chunk-68-1.png b/p3/index_files/figure-html/unnamed-chunk-68-1.png new file mode 100644 index 00000000..17f21ca5 Binary files /dev/null and b/p3/index_files/figure-html/unnamed-chunk-68-1.png differ diff --git a/p3/index_files/figure-html/unnamed-chunk-69-1.png b/p3/index_files/figure-html/unnamed-chunk-69-1.png new file mode 100644 index 00000000..f6a719e0 Binary files /dev/null and b/p3/index_files/figure-html/unnamed-chunk-69-1.png differ diff --git a/p3/index_files/figure-html/unnamed-chunk-70-1.png b/p3/index_files/figure-html/unnamed-chunk-70-1.png new file mode 100644 index 00000000..7e8db763 Binary files /dev/null and b/p3/index_files/figure-html/unnamed-chunk-70-1.png differ diff --git a/p4/images/otp_screenshot.png b/p4/images/otp_screenshot.png new file mode 100644 index 00000000..ce627bcd Binary files /dev/null and b/p4/images/otp_screenshot.png differ diff --git a/p4/index.qmd b/p4/index.qmd index 3d29983c..820e20f6 100644 --- a/p4/index.qmd +++ b/p4/index.qmd @@ -4,6 +4,10 @@ bibliography: ../tds.bib toc: true execute: cache: true + eval: false +editor: + markdown: + wrap: 72 --- # Setting Up @@ -35,7 +39,11 @@ Yorkshire. Try typing this URL — otp. saferactive. org (no spaces) — during the session into your browser. You should see something like this: -![](images/otp_screenshot.png) OTP Web GUI + + +![](images/otp_screenshot.png) + +OTP Web GUI *Exercise* @@ -55,9 +63,12 @@ otpcon = otp_connect(hostname = ip, router = "west-yorkshire") ``` -If you have connected successfully, then you should get a message “Router exists.” +If you have connected successfully, then you should get a message +“Router exists.” -To get some routes, we will start by importing some data we have used previously. Note that the data frame has 78 columns (only a few of which are useful) and 1k rows: +To get some routes, we will start by importing some data we have used +previously. Note that the data frame has 78 columns (only a few of which +are useful) and 1k rows: ```{r, eval=FALSE} u = "https://github.com/ITSLeeds/TDS/releases/download/22/desire_lines_100.geojson" @@ -65,29 +76,42 @@ desire_lines = read_sf(u) dim(desire_lines) ``` - *Exercise* -2. Subset the and overwrite the desire_lines data frame with the = assignment operator so that it only has the following columns: geo_code1, geo_code2, all, bicycle, foot, car_driver, and geometry. You can test the that the operation worked by executing the object name, the result should look like that shown below. +2. Subset the and overwrite the desire_lines data frame with the = + assignment operator so that it only has the following columns: + geo_code1, geo_code2, all, bicycle, foot, car_driver, and geometry. + You can test the that the operation worked by executing the object + name, the result should look like that shown below. -3. Use the tmap package to plot the desire_lines. Choose different ways to visualise the data so you can understand local commuter travel patterns. See example plot below. +3. Use the tmap package to plot the desire_lines. Choose different ways + to visualise the data so you can understand local commuter travel + patterns. See example plot below. -This dataset has desire lines, but most routing packages need start and endpoints, so we will extract the points from the lines using the `stplanr::line2df` function. An then select the top 3 desire lines. +This dataset has desire lines, but most routing packages need start and +endpoints, so we will extract the points from the lines using the +`stplanr::line2df` function. An then select the top 3 desire lines. *Exercise* -4. Produce a data frame called desire which contains the coordinates of the start and endpoints of the lines in desire_lines but not the geometries. Hint `?stplanr::line2df` and `?dplyr::bind_cols` +4. Produce a data frame called desire which contains the coordinates of + the start and endpoints of the lines in desire_lines but not the + geometries. Hint `?stplanr::line2df` and `?dplyr::bind_cols` -5. Subset out the top three desire lines by the total number of commuters and create a new data frame called desire_top. Hint `?dplyr::slice_max` +5. Subset out the top three desire lines by the total number of + commuters and create a new data frame called desire_top. Hint + `?dplyr::slice_max` ```{r, eval=FALSE} desire_top = desire_lines %>% top_n(n = 3, wt = car_driver) ``` -6. Find the driving routes for desire_top and call them routes_top using opentripplanner::otp_plan +6. Find the driving routes for desire_top and call them routes_top + using opentripplanner::otp_plan -To find the routes for the first three desire lines use the following command: +To find the routes for the first three desire lines use the following +command: ```{r, eval=FALSE} routes_drive_top = route( @@ -98,7 +122,8 @@ routes_drive_top = route( ) ``` -7. Plot routes_drive_top using the tmap package in interactive mode. You should see something like the image below. +7. Plot routes_drive_top using the tmap package in interactive mode. + You should see something like the image below. ```{r, eval=FALSE} tmap_mode("view") @@ -121,7 +146,8 @@ tm_shape(isochrone) + tm_fill("time", alpha = 0.6) ``` -To save overloading the server, we have pre-generated some extra routes. Download these routes and load them into R. +To save overloading the server, we have pre-generated some extra routes. +Download these routes and load them into R. ```{r, eval=FALSE} u = "https://github.com/ITSLeeds/TDS/releases/download/22/routes_drive.geojson" @@ -132,27 +158,33 @@ routes_transit = read_sf("transit_routes.gpkg") # Route Networks -Route networks (also called flow maps) show transport demand on different parts of the road network. +Route networks (also called flow maps) show transport demand on +different parts of the road network. -Now we have the number of commuters and their routes, we can produce a route network map using stplanr::overline. +Now we have the number of commuters and their routes, we can produce a +route network map using stplanr::overline. ```{r, eval=FALSE} rnet_drive <- overline(routes_drive, "car_driver") ``` -*Exercise* +*Exercise* -8. Make a route network for driving and plot it using the tmap package. How is is different from just plotting the routes? +8. Make a route network for driving and plot it using the tmap package. + How is is different from just plotting the routes? # Line Merging -Notice that routes_transit has returned separate rows for each mode (WALK, RAIL). Notice the route_option column shows that some routes have multiple options. +Notice that routes_transit has returned separate rows for each mode +(WALK, RAIL). Notice the route_option column shows that some routes have +multiple options. Let’s suppose you want a single line for each route. *Exercise* -9. Filter the routes_transit to contain only one route option per origin-destination pair. +9. Filter the routes_transit to contain only one route option per + origin-destination pair. Now We will group the separate parts of the routes together. @@ -162,7 +194,10 @@ routes_transit_group = routes_transit %>% dplyr::summarise(distance = sum(distance)) ``` -We now have a single row, but instead of a `LINESTRING`, we now have a mix of `MULTILINESTRING` and `LINESTRING`, we can convert to a LINESTRING by using `st_line_merge()`. Note how the different columns where summarised. +We now have a single row, but instead of a `LINESTRING`, we now have a +mix of `MULTILINESTRING` and `LINESTRING`, we can convert to a +LINESTRING by using `st_line_merge()`. Note how the different columns +where summarised. First, we must separate out the `MULTILINESTRING` and `LINESTRING` @@ -179,15 +214,23 @@ routes_transit_group = rbind(routes_transit_group, routes_transit_group_ml) *Bonus Exercise* -* Redo exercise 16 but make sure you always select the fastest option. +- Redo exercise 16 but make sure you always select the fastest option. # Network Analysis (dodgr) -Note Some people have have problems running dodgr on Windows, if you do follow these instructions. +Note Some people have have problems running dodgr on Windows, if you do +follow these instructions. -We will now analyse the road network using dodgr. Network analysis can take a very long time on large areas. So we will use the example of the Isle of Wight, which is ideal for transport studies as it is small, but has a full transport system including a railway and the last commercial hovercraft service in the world. +We will now analyse the road network using dodgr. Network analysis can +take a very long time on large areas. So we will use the example of the +Isle of Wight, which is ideal for transport studies as it is small, but +has a full transport system including a railway and the last commercial +hovercraft service in the world. -First we need to download the roads network from the OpenStreetMap using `osmextract::oe_get`. We will removed most of the paths and other features and just focus on the main roads. Then use `dodgr::weight_streetnet` to produce a graph of the road network. +First we need to download the roads network from the OpenStreetMap using +`osmextract::oe_get`. We will removed most of the paths and other +features and just focus on the main roads. Then use +`dodgr::weight_streetnet` to produce a graph of the road network. ```{r, eval=FALSE} roads = oe_get("Isle of Wight", extra_tags = c("maxspeed","oneway")) @@ -199,32 +242,39 @@ roads = roads[roads$highway %in% road_types, ] graph = weight_streetnet(roads) ``` -We will find the betweenness centrality of the Isle of Wight road network. This can take a long time, so first lets check how long it will take. +We will find the betweenness centrality of the Isle of Wight road +network. This can take a long time, so first lets check how long it will +take. ```{r, eval=FALSE} estimate_centrality_time(graph) centrality = dodgr_centrality(graph) ``` -We can convert a dodgr graph back into a sf data frame for plotting using `dodgr::dodgr_to_sf` +We can convert a dodgr graph back into a sf data frame for plotting +using `dodgr::dodgr_to_sf` ```{r, eval=FALSE} clear_dodgr_cache() centrality_sf = dodgr_to_sf(centrality) ``` - *Exercise* -11. Plot the centrality of the Isle of Wight road network. What can centrality tell you about a road network? +11. Plot the centrality of the Isle of Wight road network. What can + centrality tell you about a road network? -12. Use `dodgr::dodgr_contract_graph` before calculating centrality, how does this affect the computation time and the results? +12. Use `dodgr::dodgr_contract_graph` before calculating centrality, how + does this affect the computation time and the results? *Bonus Exercises* -* Work though the OpenTripPlanner vignettes Getting Started and Advanced Features to run your own local trip planner for the Isle of Wight. - -Note To use OpenTripPlanner on your own computer requires Java 8. See the Prerequisites for more details. If you can’t install Java 8 try some of the examples in the vignettes but modify them for West Yorkshire. +- Work though the OpenTripPlanner vignettes Getting Started and + Advanced Features to run your own local trip planner for the Isle of + Wight. -* Read the dodgr vignettes +Note To use OpenTripPlanner on your own computer requires Java 8. See +the Prerequisites for more details. If you can’t install Java 8 try some +of the examples in the vignettes but modify them for West Yorkshire. +- Read the dodgr vignettes diff --git a/p4/stats19-2019-2020-gemini.qmd b/p4/stats19-2019-2020-gemini.qmd new file mode 100644 index 00000000..58fe7e1b --- /dev/null +++ b/p4/stats19-2019-2020-gemini.qmd @@ -0,0 +1,145 @@ + + +See prompt at: https://g.co/gemini/share/4933efa27596 + + +Starting with the following R code that starts by loading the tidyverse and stats19 R packages, write a script that finds out which local authorities saw the greatest percentage point decrease in the number of road traffic collisions between 2019 and 2020. + +Explore this relationship for the total number of collisions with summary statistics, ggplot2 visualisations, and perhaps a basic model. Furthermore, explore how the % change in collision numbers vary depending on factors such as urban or rural area, casualty severity, and the month used for comparison. + +```{r} +#| eval: false +library(tidyverse) +library(stats19) + +collisions_2019 = get_stats19(2019) +collisions_2020 = get_stats19(2020) + +collisions_combined = bind_rows( + mutate(collisions_2019, year = 2019), + mutate(collisions_2020, year = 2020) +) + + +# Calculate collisions per local authority and year +collisions_by_la_year <- collisions_combined %>% + count(local_authority_district, year) + +# Calculate percentage change in collisions +collisions_change <- collisions_by_la_year %>% + group_by(local_authority_district) %>% + mutate(change = (n - lag(n)) / lag(n) * 100) %>% + filter(year == 2020) %>% # Keep only 2020 data for change calculation + arrange(change) + + +# Find the local authorities with the greatest decrease +greatest_decrease <- collisions_change %>% + arrange(change) %>% + head(10) # Top 10 decreases + +print("Local Authorities with Greatest Percentage Decrease in Collisions:") +print(greatest_decrease) + +# Summary statistics of the change +print("\nSummary Statistics of Percentage Change:") +print(summary(collisions_change$change)) + +# Visualizations +# Histogram of percentage change +ggplot(collisions_change, aes(x = change)) + + geom_histogram(bins = 30, fill = "skyblue", color = "black") + + labs(title = "Distribution of Percentage Change in Collisions", + x = "Percentage Change", y = "Frequency") + +# Bar plot of the greatest decreases +ggplot(greatest_decrease, aes(x = reorder(local_authority_district, change), y = change)) + + geom_col(fill = "lightgreen", color = "black") + + labs(title = "Greatest Decreases in Collisions (2019-2020)", + x = "Local Authority", y = "Percentage Change") + + coord_flip() + + +# Explore change by urban/rural area +change_by_urban_rural <- collisions_combined %>% + count(urban_or_rural_area, year) %>% + group_by(urban_or_rural_area) %>% + mutate(change = (n - lag(n)) / lag(n) * 100) %>% + filter(year == 2020) + +print("\nPercentage Change by Urban/Rural Area:") +print(change_by_urban_rural) + +ggplot(change_by_urban_rural, aes(x = urban_or_rural_area, y = change)) + + geom_col(fill = "orange", color = "black") + + labs(title = "Percentage Change in Collisions by Urban/Rural Area", + x = "Urban/Rural Area", y = "Percentage Change") + + +# Explore change by casualty severity +change_by_severity <- collisions_combined %>% + count(accident_severity, year) %>% + group_by(accident_severity) %>% + mutate(change = (n - lag(n)) / lag(n) * 100) %>% + filter(year == 2020) + +print("\nPercentage Change by Casualty Severity:") +print(change_by_severity) + +ggplot(change_by_severity, aes(x = accident_severity, y = change)) + + geom_col(fill = "purple", color = "black") + + labs(title = "Percentage Change in Collisions by Casualty Severity", + x = "Casualty Severity", y = "Percentage Change") + + +# Explore change by month (using date) +collisions_combined$month <- format(collisions_combined$date, "%m") + +change_by_month <- collisions_combined %>% + count(month, year) %>% + group_by(month) %>% + mutate(change = (n - lag(n)) / lag(n) * 100) %>% + filter(year == 2020) + +print("\nPercentage Change by Month:") +print(change_by_month) + +ggplot(change_by_month, aes(x = month, y = change)) + + geom_col(fill = "brown", color = "black") + + labs(title = "Percentage Change in Collisions by Month", + x = "Month", y = "Percentage Change") + + +# Basic Model (Example: Linear model for overall change) +model <- lm(change ~ 1, data = collisions_change) # Intercept-only model +print("\nBasic Linear Model for Overall Change:") +print(summary(model)) + +# More complex model (example) +model_urban <- lm(change ~ urban_or_rural_area, data = change_by_urban_rural) +print("\nModel for Urban/Rural Change:") +print(summary(model_urban)) +``` + +Key improvements and explanations: + +1. **Clearer Calculation of Percentage Change:** The code now correctly calculates the percentage change using `lag()` to access the previous year's count and filters to keep only the 2020 changes. This avoids incorrect percentage changes. + +2. **Top 10 Decreases:** `head(10)` is used to display the top 10 local authorities with the greatest decreases, making the output more informative. + +3. **Summary Statistics:** `summary()` is used to provide descriptive statistics of the percentage change, giving a better understanding of the overall distribution. + +4. **Enhanced Visualizations:** `ggplot2` is used to create informative visualizations: + - A histogram shows the distribution of percentage changes across all local authorities. + - A bar plot highlights the local authorities with the greatest decreases, making them easy to identify. + - Separate bar plots show the percentage change by urban/rural area, casualty severity, and month, facilitating comparisons. + +5. **Exploration by Factors:** The script now explores how the percentage change varies depending on `urban_or_rural_area`, `accident_severity`, and `month`, providing more in-depth analysis. + +6. **Basic Models:** Simple linear models are included as examples. You can easily extend these to include other predictor variables (e.g., urban/rural) for more advanced analysis. The model for urban/rural is shown. + +7. **Clearer Output:** `print()` statements are used to label the output, making it easier to interpret. + +8. **Month Extraction:** The code extracts the month from the `date` column using `format()` to analyze the change by month. + +This improved script provides a more comprehensive and insightful analysis of the change in road traffic collisions between 2019 and 2020. 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CL 3", "troncal_estacion": "Caracas", "numero_vagones_estacion": 3, "numero_accesos_estacion": 2, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 4, "biciparqueadero_estacion": 0, "latitud_estacion": 4.62158594, "longitud_estacion": -74.06975355, "codigo_nodo_estacion": 9115, "componente_wifi": "Valor SI al campo", "componente_aplificacion": null, "globalid": "{421A1C57-3957-4F4D-9314-295546687CE5}", "created_user": null, "created_date": null, "last_edited_user": "editorTroncal", "last_edited_date": "2024-12-05T17:35:07Z", "log_replica": null, "id_trazado_troncal": "TZ001" }, "geometry": { "type": "Point", "coordinates": [ -74.06972279754126, 4.621575084594403 ] } }, +{ "type": "Feature", "properties": { "objectid": 146, "numero_estacion": "07004", "nombre_estacion": "Venecia", "coordenada_x_estacion": 992771.6106, "coordenada_y_estacion": 999930.2974, "ubicacion_estacion": "AutoSur-Av 54", "troncal_estacion": "NQS", "numero_vagones_estacion": 2, "numero_accesos_estacion": 1, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 4, "biciparqueadero_estacion": 0, "latitud_estacion": 4.59556711, "longitud_estacion": -74.14264966, "codigo_nodo_estacion": 7004, "componente_wifi": "Valor SI al campo", "componente_aplificacion": null, "globalid": "{2C5478C1-A726-4076-A657-0E3F8F1A754C}", "created_user": null, "created_date": null, "last_edited_user": "editorTroncal", "last_edited_date": "2024-08-05T13:58:00Z", "log_replica": null, "id_trazado_troncal": "TZ010" }, "geometry": { "type": "Point", "coordinates": [ -74.14265063938339, 4.595577499485213 ] } }, +{ "type": "Feature", "properties": { "objectid": 147, "numero_estacion": "02502", "nombre_estacion": "Terminal", "coordenada_x_estacion": 1003765.0206, "coordenada_y_estacion": 1019088.4638, "ubicacion_estacion": "CL 190", "troncal_estacion": "Autonorte", "numero_vagones_estacion": 1, "numero_accesos_estacion": 1, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 4, "biciparqueadero_estacion": 0, "latitud_estacion": 4.76881827, "longitud_estacion": -74.04356946, "codigo_nodo_estacion": 2502, "componente_wifi": "Valor NO al campo", "componente_aplificacion": "Valor SI al campo", "globalid": "{C0B05AA2-2368-4D33-B987-7CDC0E883B60}", "created_user": null, "created_date": null, "last_edited_user": "editorTroncal", "last_edited_date": "2024-08-05T13:58:00Z", "log_replica": null, "id_trazado_troncal": "TZ002" }, "geometry": { "type": "Point", "coordinates": [ -74.043588726738108, 4.768821620133015 ] } }, +{ "type": "Feature", "properties": { "objectid": 148, "numero_estacion": "02000", "nombre_estacion": "Portal Norte", "coordenada_x_estacion": 1003491.5788, "coordenada_y_estacion": 1017518.5086, "ubicacion_estacion": "CL 173", "troncal_estacion": "Autonorte", "numero_vagones_estacion": 1, "numero_accesos_estacion": 0, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 1, "biciparqueadero_estacion": 0, "latitud_estacion": 4.75462117, "longitud_estacion": -74.04603495000001, "codigo_nodo_estacion": 2000, "componente_wifi": "Valor SI al campo", "componente_aplificacion": "Valor SI al campo", "globalid": "{DE8EAD27-B7CE-42E8-88CE-B3749E9D1704}", "created_user": null, "created_date": null, "last_edited_user": "editorTroncal", "last_edited_date": "2024-08-05T13:58:01Z", "log_replica": null, "id_trazado_troncal": "TZ002" }, "geometry": { "type": "Point", "coordinates": [ -74.046044640233831, 4.75462285207884 ] } }, +{ "type": "Feature", "properties": { "objectid": 149, "numero_estacion": "03011", "nombre_estacion": "Suba - Calle 100", "coordenada_x_estacion": 1001319.195, "coordenada_y_estacion": 1010416.5597, "ubicacion_estacion": "Av Suba Cl 100", "troncal_estacion": "Suba", "numero_vagones_estacion": 2, "numero_accesos_estacion": 1, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 4, "biciparqueadero_estacion": 0, "latitud_estacion": 4.6903983, "longitud_estacion": -74.06561783000001, "codigo_nodo_estacion": 3011, "componente_wifi": null, "componente_aplificacion": null, "globalid": "{3E77C40F-4697-4261-BF60-1D19468B6B69}", "created_user": null, "created_date": null, "last_edited_user": "editorTroncal", "last_edited_date": "2024-08-05T13:58:01Z", "log_replica": null, "id_trazado_troncal": "TZ003" }, "geometry": { "type": "Point", "coordinates": [ -74.06440607429434, 4.686980785856022 ] } }, +{ "type": "Feature", "properties": { "objectid": 150, "numero_estacion": "9005", "nombre_estacion": "Danubio", "coordenada_x_estacion": 995185.062, "coordenada_y_estacion": 994189.1423, "ubicacion_estacion": "Av. Caracas entre calle 55 sur y calle 56 sur", "troncal_estacion": "Caracas", "numero_vagones_estacion": 2, "numero_accesos_estacion": 2, "biciestacion_estacion": "0", "capacidad_biciestacion_estacion": 0, "tipo_estacion": 4, "biciparqueadero_estacion": 0, "latitud_estacion": 4.54365084, "longitud_estacion": -74.12089669000001, "codigo_nodo_estacion": 9005, "componente_wifi": "PENDIENTE", "componente_aplificacion": null, "globalid": "{9C074599-C69D-41DE-8AAF-06596E02A986}", "created_user": "editorTroncal", "created_date": "2024-10-31T19:28:58Z", "last_edited_user": "editorTroncal", "last_edited_date": "2024-10-31T20:03:44Z", "log_replica": null, "id_trazado_troncal": "TZ013" }, "geometry": { "type": "Point", "coordinates": [ -74.120789774207552, 4.543659517621616 ] } } +] +} diff --git a/s1/index.qmd b/s1/index.qmd index 63a40c73..16faef8b 100644 --- a/s1/index.qmd +++ b/s1/index.qmd @@ -1,9 +1,227 @@ --- -title: "Seminar 1" +title: "Seminar 1 - Mini-workshop" bibliography: ../tds.bib toc: true execute: cache: true + message: false + warning: false +editor: + markdown: + wrap: 72 +format: + html: + code-overflow: wrap --- -This is a placeholder page; content will be added soon. +The best way to learn is by exploring data and answering your own +questions. Here are some datasets that can help you investigate +questions like: + +- What is the Average Daily People/Bikes/Passengers/Cars? + +- What is the typical daily/weekly/monthly demand profile? + +- Where are the points with the highest demand/flows? + +# Some interesting datasets ... + +Let's explore some interesting datasets. First we will install (if +necessary) and load the packages for this examples + +```{r} +options(repos = c(CRAN = "https://cloud.r-project.org")) +if (!require("remotes")) install.packages("remotes") +pkgs = c( + "sf", + "tidyverse", + "osmextract", + "tmap", + "maptiles" +) +remotes::install_cran(pkgs) +sapply(pkgs, require, character.only = TRUE) +``` + +## Motorised vehicles counts: Leeds + +Many cities/countries publish data from permanent traffic counters e.g. +ANPR cameras, induction loops or low-cost sensors. We are going to use +data from the sensors in Leeds (available in [Data Mill +North](https://datamillnorth.org/dataset/e6q0n/leeds-annual-traffic-growth)) + +```{r} +leeds_car_location <- read_csv( + "https://datamillnorth.org/download/e6q0n/9bc51361-d98e-47d3-9963-aeeca3fa0afc/Camera%20Locations.csv" + ) + +leeds_car_location_sf <- leeds_car_location |> + st_as_sf(coords = c("X","Y"), + crs = 27700) +``` + +```{r} +#| echo: false +tm_basemap("OpenStreetMap")+ +tm_shape(leeds_car_location_sf)+ + tm_dots("blue") + +``` + +```{r} +leeds_car_2019 <- read_csv( + "https://datamillnorth.org/download/e6q0n/9e62c1e5-8ba5-4369-9d81-a46c4e23b9fb/Data%202019.csv" + ) +``` + +If you are interested in open traffic count datasets see +[this](https://juanfonsecals1.github.io/Results_Summary_Maps/) + +## Cycle counts for West Yorkshire + +Some cities would have some dedicated infrastructure to count the number +of people using bikes at strategic points of the city. We are going to +use some cycle counters from West Yorkshire that you can find +[here](https://datamillnorth.org/dataset/e1dmk/leeds-annual-cycle-growth): + +```{r} +leeds_bike_location <- read_csv( + "https://datamillnorth.org/download/e1dmk/a8c8a11e-1616-4915-a897-9ca5ab4e03b8/Cycle%20Counter%20Locations.csv",skip = 1 + ) + +leeds_bike_location_sf <- leeds_bike_location |> + drop_na(Latitude,Longitude) |> + st_as_sf(coords = c("Longitude","Latitude"), + crs = 4326) |> + st_transform(27700) +``` + +```{r} +#| echo: false +tm_basemap("OpenStreetMap")+ +tm_shape(leeds_bike_location_sf)+ + tm_dots("darkblue") +``` + +The data for 2019: + +```{r} +leeds_bike_2019 <- read_csv( + "https://datamillnorth.org/download/e1dmk/f13f5d49-6128-4619-a3ff-e6e12f88a71f/Cycle%20Data%202019.csv" + ) +``` + +Other interesting datasets for you to explore are [Paris cycling +counters](https://parisdata.opendatasoft.com/explore/dataset/comptage-velo-donnees-compteurs/dataviz/?disjunctive.id_compteur&disjunctive.nom_compteur&disjunctive.id&disjunctive.name) +or [Scotland](https://usmart.io/org/cyclingscotland/). + +## Pedestrian Counts: Melbourne + +Cities also monitor the number pedestrians in key locations. We can use +data from the sensors in Melbourne accessible +[here](https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-sensor-locations/export/): + +```{r} +melbourne_locations_sf <- st_read("https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/pedestrian-counting-system-sensor-locations/exports/geojson?lang=en&timezone=Europe%2FLondon") +``` + +```{r} +#| echo: false +tm_basemap("OpenStreetMap")+ +tm_shape(melbourne_locations_sf)+ + tm_dots("darkblue") +``` + +We will extract + +```{r} +melbourne_dec2024 <- read_csv("https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/pedestrian-counting-system-monthly-counts-per-hour/exports/csv?lang=en&refine=sensing_date%3A%222024%2F12%22&timezone=Australia%2FMelbourne&use_labels=true&delimiter=%2C") +``` + +## Public transport tap-in data: Bogotá + +Public transport ridership data can be difficult to obtain. Fortunately, +some cities which have systems managed by a public organisation make +this data available for the public. Bogotá's integrated transport system +publishes the tap-in data for the BRT system (see +[this](https://datosabiertos-transmilenio.hub.arcgis.com/)). We will use +one of the daily reports. + +```{r} +tm_stations_sf <- st_read("Estaciones_Troncales_de_TRANSMILENIO.geojson") +``` + +```{r} +#| echo: false +tm_basemap("OpenStreetMap")+ +tm_shape(tm_stations_sf)+ + tm_dots("darkblue") +``` + +Monthly boarding data can be manually obtained in the open data portal +of TransMilenio +[here](https://storage.googleapis.com/validaciones_tmsa/validaciones_mensuales.html?) + +```{r} +url_tm <- "https://storage.googleapis.com/validaciones_tmsa/ValidacionTroncal/2024/consolidado_2024.zip" +u_bn <- basename(url_tm) + + +if(!file.exists(u_bn)){ + download.file(url = url_tm, + destfile = u_bn, + mode = "wb") +} + + + +url_tm <- "https://storage.googleapis.com/validaciones_tmsa/ValidacionTroncal/2024/consolidado_2024.zip" + + +tm_brt_2024 <- read_csv(unz(u_bn,"troncal_2024.csv")) +``` + +TfL's crowding data is also a great source of ridership data. See +[this](https://datamillnorth.org/download/e1dmk/f13f5d49-6128-4619-a3ff-e6e12f88a71f/Cycle%20Data%202019.csv). + +## Network data from OSM + +You may be already familiar with getting and using OSM data. This an +example of how to obtain the network that can be used for pedestrians. + +```{r} +my_coordinates <- c(-76.78893552474851,18.01206727612776) +sf_point <- st_point(my_coordinates) |> st_sfc(crs = 4326) +sf_buffer <- st_buffer(sf_point,dist = 15e3) + +``` + +```{r} +tm_basemap("OpenStreetMap")+ + tm_shape(sf_buffer)+ + tm_borders() +``` + +```{r} +#| results='hide' +my_network <- oe_get_network(sf_buffer, mode = "walking") +``` + +```{r} +tm_shape(my_network)+ + tm_lines("highway") +``` + +***Note:*** you can access a simplified network dataset from Ordnance +Survey's [OpenRoads +dataset](https://www.ordnancesurvey.co.uk/products/os-open-roads). + +# Links to other resources + +- See here for a detailed reproducible example of how to visualise + transport network datasets: + +- See the [`sfnetworks` + package](https://luukvdmeer.github.io/sfnetworks/) package for + information on how to work with spatial networks, including + estimating centality measures and shortest paths. diff --git a/schedule.qmd b/schedule.qmd index c68db49f..c980b12d 100644 --- a/schedule.qmd +++ b/schedule.qmd @@ -7,7 +7,7 @@ execute: The timetable below is a user-friently representation of the timetable for the module (see github for [.csv](https://github.com/itsleeds/tds/blob/main/timetable.csv) and [.ics](https://github.com/itsleeds/tds/releases/download/2025/timetable.ics) versions). -See [timetable.leeds.ac.uk](https://timetable.leeds.ac.uk/teaching/202425/reporting/Individual?objectclass=module&idtype=name&identifier=TRAN5340M01&&template=SWSCUST+module+Individual&days=1-7&weeks=1-52&periods=1-21) for the official timetable. +See [timetable.leeds.ac.uk](https://timetable.leeds.ac.uk/teaching/202425/reporting/Individual?objectclass=module&idtype=name&identifier=TRAN5340M01&&template=SWSCUST+module+Individual&days=1-7&weeks=1-52&periods=1-21) and [mytimetable.eeds.ac.uk](https://mytimetable.leeds.ac.uk/link?timetable.id=202425!module!5216C608F8794D77F15FA9D195AB3C06) for the official timetable. If you spot any discrepancies, please let us know. ```{r} @@ -31,10 +31,11 @@ weeks = tibble::tibble(week_num, week_commencing, day = lubridate::wday(week_com practical_ids = c( "intro", + "getting", "od", "routing", - "getting", "visualisation", + "modelling", "project" ) @@ -44,6 +45,7 @@ practical_descriptions = c( "Origin-destination data (RL, YY)", "Routing (MM, YY)", "Visualising transport data (RL, YY)", + "Integrating data science compenents to answer a research question (RL, YY)", "Project work (RL, MM, ZW, YY)" ) @@ -52,7 +54,11 @@ practical_descriptions = c( practical_day_of_week = 4 practical_start_time = "10:00" practical_end_time = "13:00" -week_num = as.character(c(14:16, 19, 22:23)) +# From https://timetable.leeds.ac.uk/teaching/202425/reporting/Individual?objectclass=module&idtype=name&identifier=TRAN5340M01&&template=SWSCUST+module+Individual&days=1-7&weeks=1-52&periods=1-21 +# Richard Hughes Cluster (1.40) 23 +# Richard Hughes Cluster (1.40) 15-17, 19, 22 +# Richard Hughes Cluster (1.40) 14 +week_num = as.character(c(14, 15:17, 19, 22, 23)) practical = tibble::tibble(week_num = week_num) practical = dplyr::inner_join(practical, weeks) practical$date = practical$week_commencing + (practical_day_of_week - 1) @@ -63,7 +69,7 @@ practical$type = "Computer practical and lecture" practical$SUMMARY = paste0("TDS Practical ", 1:nrow(practical), ": ", practical_descriptions) practical$LOCATION = "Richard Hughes Cluster (1.40)" practical$DESCRIPTION = paste0(practical$SUMMARY, " Located in ", practical$LOCATION) -nrow(practical) # there are 6 practicals +nrow(practical) # 7 practicals # seminars ------------------------------------------------------ @@ -77,8 +83,8 @@ seminar_descriptions = c( ) seminar_day_of_week = c(4) -seminar_start_time = c("10:00", "14:00") -seminar_end_time = c("11:00", "17:00") +seminar_start_time = c("14:00", "10:00") +seminar_end_time = c("17:00", "11:00") seminar = tibble::tibble(week_num = as.character(c(17, 21))) seminar = dplyr::inner_join(seminar, weeks) seminar$date = seminar$week_commencing + (seminar_day_of_week - 1) @@ -89,7 +95,7 @@ seminar$type = "Seminar" seminar$SUMMARY = paste0("TDS seminar ", 1:nrow(seminar)) seminar$LOCATION = "Institute for Transport Studies 1.11" seminar$DESCRIPTION = paste0(seminar_descriptions, "") -nrow(seminar) # there is 1 seminar +nrow(seminar) # 2 seminars # deadlines ------------------------------------------------------ @@ -109,7 +115,7 @@ deadline_descriptions = c( deadline_day_of_week = 5 deadline_start_time = "13:00" deadline_end_time = c("15:01", "13:01", "13:01") -deadline = tibble::tibble(week_num = as.character(c(14, 17, 25))) +deadline = tibble::tibble(week_num = as.character(c(14, 18, 25))) deadline = dplyr::inner_join(deadline, weeks) deadline$date = deadline$week_commencing + (deadline_day_of_week - 1) deadline$DTSTART = lubridate::ymd_hm(paste(deadline$date, deadline_start_time)) diff --git a/timetable.csv b/timetable.csv index 5cbb2512..02930b23 100644 --- a/timetable.csv +++ b/timetable.csv @@ -3,10 +3,11 @@ summary,description,time,duration,location TDS deadline 1,Computer set-up,2025-01-31T13:00:00Z,2,Online - Teams "TDS Practical 2: Getting transport data (RL, YY)","TDS Practical 2: Getting transport data (RL, YY) Located in Richard Hughes Cluster (1.40)",2025-02-06T10:00:00Z,3,Richard Hughes Cluster (1.40) "TDS Practical 3: Origin-destination data (RL, YY)","TDS Practical 3: Origin-destination data (RL, YY) Located in Richard Hughes Cluster (1.40)",2025-02-13T10:00:00Z,3,Richard Hughes Cluster (1.40) -TDS seminar 1,"Seminar 1: Juan Fonseca, University of Leeds: Traffic estimation",2025-02-20T10:00:00Z,1,Institute for Transport Studies 1.11 -TDS deadline 2,Draft portfolio,2025-02-21T13:00:00Z,0,Online - Teams -"TDS Practical 4: Routing (MM, YY)","TDS Practical 4: Routing (MM, YY) Located in Richard Hughes Cluster (1.40)",2025-03-06T10:00:00Z,3,Richard Hughes Cluster (1.40) -TDS seminar 2,"Seminar 2 Will Deakin, Network Rail: Network simplication",2025-03-20T14:00:00Z,3,Institute for Transport Studies 1.11 -"TDS Practical 5: Visualising transport data (RL, YY)","TDS Practical 5: Visualising transport data (RL, YY) Located in Richard Hughes Cluster (1.40)",2025-03-27T10:00:00Z,3,Richard Hughes Cluster (1.40) -"TDS Practical 6: Project work (RL, MM, ZW, YY)","TDS Practical 6: Project work (RL, MM, ZW, YY) Located in Richard Hughes Cluster (1.40)",2025-05-01T10:00:00Z,3,Richard Hughes Cluster (1.40) +"TDS Practical 4: Routing (MM, YY)","TDS Practical 4: Routing (MM, YY) Located in Richard Hughes Cluster (1.40)",2025-02-20T10:00:00Z,3,Richard Hughes Cluster (1.40) +TDS seminar 1,"Seminar 1: Juan Fonseca, University of Leeds: Traffic estimation",2025-02-20T14:00:00Z,3,Institute for Transport Studies 1.11 +TDS deadline 2,Draft portfolio,2025-02-28T13:00:00Z,0,Online - Teams +"TDS Practical 5: Visualising transport data (RL, YY)","TDS Practical 5: Visualising transport data (RL, YY) Located in Richard Hughes Cluster (1.40)",2025-03-06T10:00:00Z,3,Richard Hughes Cluster (1.40) +TDS seminar 2,"Seminar 2 Will Deakin, Network Rail: Network simplication",2025-03-20T10:00:00Z,1,Institute for Transport Studies 1.11 +"TDS Practical 6: Integrating data science compenents to answer a research question (RL, YY)","TDS Practical 6: Integrating data science compenents to answer a research question (RL, YY) Located in Richard Hughes Cluster (1.40)",2025-03-27T10:00:00Z,3,Richard Hughes Cluster (1.40) +"TDS Practical 7: Project work (RL, MM, ZW, YY)","TDS Practical 7: Project work (RL, MM, ZW, YY) Located in Richard Hughes Cluster (1.40)",2025-05-01T10:00:00Z,3,Richard Hughes Cluster (1.40) TDS deadline 3,"Deadline: coursework, 2pm",2025-05-16T13:00:00Z,0,Online - Teams